We stopped bloging on this site in 2019 as this topic was ‘early.’ Much has happened in the world of AI since then. It is time to restart.
Yaël Eisenstat is a former CIA officer, national security adviser to vice president Biden, and CSR leader at ExxonMobil. She was elections integrity operations head at Facebook from June to November 2018.
In my six months at Facebook … . I did not know anyone who intentionally wanted to incorporate bias into their work. But I also did not find anyone who actually knew what it meant to counter bias in any true and methodical way. #AI #AIbias @YaelEisenstathttps://t.co/5f1pS3AymP— Phil & Pam Lawson (@SocializingAI) February 14, 2019
Has anyone stopped to ask whether the humans that feed the machines really understand what bias means?
Over more than a decade working as a CIA officer, I went through months of training and routine retraining on structural methods for checking assumptions and understanding cognitive biases.
It is one of the most important skills for an intelligence officer to develop. Analysts and operatives must hone the ability to test assumptions and do the uncomfortable and often time-consuming work of rigorously evaluating one’s own biases when analyzing events. They must also examine the biases of those providing information—assets, foreign governments, media, adversaries—to collectors.
While tech companies often have mandatory “managing bias” training to help with diversity and inclusion issues, I did not see any such training on the field of cognitive bias and decision making, particularly as it relates to how products and processes are built and secured.
I believe that many of my former coworkers at Facebook fundamentally want to make the world a better place. I have no doubt that they feel they are building products that have been tested and analyzed to ensure they are not perpetuating the nastiest biases.
But the company has created its own sort of insular bubble in which its employees’ perception of the world is the product of a number of biases that are engrained within the Silicon Valley tech and innovation scene
Becoming overly reliant on data—which in itself is a product of availability bias—is a huge part of the problem.
In my time at Facebook, I was frustrated by the immediate jump to “data” as the solution to all questions. That impulse often overshadowed necessary critical thinking to ensure that the information provided wasn’t tainted by issues of confirmation, pattern, or other cognitive biases.
To counter algorithmic, machine, and AI bias, human intelligence must be incorporated into solutions, as opposed to an over-reliance on so-called “pure” data.
Attempting to avoid bias without a clear understanding of what that truly means will inevitably fail.
I’m an expert on trying to get the technology to work, not an expert on social policy. – Geoff Hintonhttps://t.co/VyH0iiZEbI
— Phil & Pam Lawson (@SocializingAI) December 14, 2018
WIRED: The recent boom of interest and investment in AI and machine learning means there’s more funding for research than ever. Does the rapid growth of the field also bring new challenges?
GH: One big challenge the community faces is that if you want to get a paper published in machine learning now it’s got to have a table in it, with all these different data sets across the top, and all these different methods along the side, and your method has to look like the best one. If it doesn’t look like that, it’s hard to get published. I don’t think that’s encouraging people to think about radically new ideas.
Now if you send in a paper that has a radically new idea, there’s no chance in hell it will get accepted, because it’s going to get some junior reviewer who doesn’t understand it. Or it’s going to get a senior reviewer who’s trying to review too many papers and doesn’t understand it first time round and assumes it must be nonsense.
Anything that makes the brain hurt is not going to get accepted. And I think that’s really bad.
What we should be going for, particularly in the basic science conferences, is radically new ideas. Because we know a radically new idea in the long run is going to be much more influential than a tiny improvement. That’s I think the main downside of the fact that we’ve got this inversion now, where you’ve got a few senior guys and a gazillion young guys.
“Tesla,” Stilgoe tells me, “is turning a blind eye to their drivers’ own experiments with Autopilot. People are using Autopilot irresponsibly, and Tesla are overlooking it because they are gathering data.” #ai #aiethics
The Deadly Recklessness ofhttps://t.co/wLcaqqn00h
— Phil & Pam Lawson (@SocializingAI) December 14, 2018
Let’s be clear about this, because it seems to me that these companies have gotten a bit of a pass for undertaking a difficult, potentially ‘revolutionary’ technology, and because blame can appear nebulous in car crashes, or in these cases can be directed toward the humans who were supposed to be watching the road. These companies’ actions (or sometimes, lack of action) have led to loss of life and limb. In the process, they have darkened the outlook for the field in general, sapping public trust in self-driving cars and delaying the rollout of what many hope will be a life-saving technology.
- According to an email recently obtained by the Information, Uber’s self-driving car division may not only be reckless, but outright negligent. The company’s executive staff reportedly ignored detailed calls from its own safety team and continued unsafe practices and a pedestrian died. Before that, a host of accidents and near-misses had gone unheeded.
- At least one major executive in Google’s autonomous car division reportedly exempted himself from test program protocol, directly caused a serious crash, injured his passenger, and never informed police that it was caused by a self-driving car. Waymo, now a subsidiary of Google, has been involved, by my count, in 21 reported crashes this year, according to California DMV records, though it was at fault in one.
But the fact is, those of us already on the road in our not-so-autonomous cars have little to no say over how we coexist with self-driving ones.
Over whether or not we’re sharing the streets with AVs running on shoddy software or overseen by less-than-alert human drivers because executives don’t want to lag in their quest to vacuum up road data or miss a sales opportunity.
Source: Gizmodo – Brian Merchant
Scholar Oscar Gandy said “rational discrimination” does not require hatred towards any class or race of people. It doesn’t even require unconscious bias to operate. “It only requires ignoring the bias that already exists” hashtag #AI #AIbias @nilofer https://t.co/V56KFNB7Mw
— Phil & Pam Lawson (@SocializingAI) December 14, 2018
Adam Galinsky of Columbia University has researched that “power and status act as self-reinforcing loops”, allowing those who have power and status to have their ideas heard and those without power to be ignored and silenced.
It’s not that the original idea is weighed and deemed unworthy but that the person bringing that new and unusual idea is deemed unworthy of being listened to.
When you are hiring to bring in new ideas or designing hackathons within your firm to unlock innovation levels within your firm, know this: you cannot do innovation and access original ideas without addressing the deep and pervasive role of bias.
Either you are doing something to explicitly dismantle the structural ways in which we limit who is allowed to have ideas, to unlock their capacity… or you are allowing the same old people to keep doing the same old things, perpetuating the status quo.
By your actions, you’re picking a side.
Source: Nilofer Merchant
… messing with elections, and the like.
– Richard Socher, Saleforce’s chief data scientist
Kathy Baxter, the architect of Ethical AI Practice at Salesforce, has a great saying: Ethics is a mindset, not a checklist. Now, it doesn’t mean you can’t have any checklists, but it means you do need to always think about the broader applications as it touches human life and informs important decisions #AI https://t.co/JemB76eFbt pic.twitter.com/IZjvGSrAdN
— Phil & Pam Lawson (@SocializingAI) December 12, 2018
Look at recommender engines — when you click a conspiracy video on a platform like YouTube, it optimizes more clicks and advertiser views and shows you crazier and crazier conspiracy theories to keep you on the platform.
And then you basically have people who become very radicalized, because anybody can put up this crazy stuff on YouTube, right? And so that is like a real issue.
Those are the things we should be talking about a lot more, because they can mess up society and make the world less stable and less democratic.
This will further destabilize Europe and the U.S., and I expect that in panic we will see #AI be used in harmful ways in light of other geopolitical crises – danah boyd a Microsoft researcher founder of the Data & Society research institute. @kavehwaddell https://t.co/zbe44xPthb pic.twitter.com/hdQuSE7DOf
— Phil & Pam Lawson (@SocializingAI) December 12, 2018
Why ‘Why’ Matters: “The Book of Why”https://t.co/a2A74W7xzS
— Phil & Pam Lawson (@SocializingAI) December 11, 2018
Causal diagrams are a form of optimized information compression. Causal diagrams crystalize knowledge, make it more transmissible, more accessible, and reduce evaporation of information.
The necessity for causal models is a paradigm shift that collides with the prevailing AI/ML meme of digital culture. The “causal revolution,” like all real revolutions, will be bumpy and full of friction. I think the resistance to Pearl (see “bashing statistics”, or “this book is a failure”) reflects, and is proportional to, our ‘automagical’ fantasy. And our emotional attachment to cognitive ease. It is my impression that the greater part of the resistance to ‘Why’ may come from those beguiled by the promise of AI/ML relieving us of complexity and the onus of cognitive effort. Those invested with the status quo, who identify with the prevalent ‘data-centric intelligence’ or with conventional statistical practice will also be offended. This is natural behavioral economics: bounded rationality and ‘satisficing’; and is to be expected.
Our becoming better scientists (health scientists, data scientists, computer scientists, social scientists, etc.) will not progress without ‘a push’ (extrinsic information). ‘Why’ is a cause, of progress.
We need to ask deeper, more complex questions: Who is in the room when these technologies are created, and which assumptions and worldviews are embedded in this process? How does our identity shape our experiences of AI systems? #AI #AIethics #AIbias https://t.co/MWxJ4k2ErC
— Phil & Pam Lawson (@SocializingAI) December 1, 2018
and those decisions won’t necessarily be in our best interests.
Tomorrow’s big tech companies will leverage intelligence (via AI) and control (via robots) associated with the lives of their users. In such a world, third-party entities may know more about us than we know about ourselves. Decisions will be made on our behalf and increasingly without our awareness, and those decisions won’t necessarily be in our best interests. #AI #AIEthics https://t.co/wvogj1dm5d
— Phil & Pam Lawson (@SocializingAI) November 28, 2018
we need it to do things like reasoning, learning causality, and exploring the world in order to learn and acquire information…If you have a good causal model of the world you are dealing with, you can generalize even in unfamiliar situations. #ai https://t.co/TmPZeYKfZC
— Phil & Pam Lawson (@SocializingAI) November 17, 2018
You mention causality—in other words, grasping not just patterns in data but why something happens. Why is that important, and why is it so hard?
If you have a good causal model of the world you are dealing with, you can generalize even in unfamiliar situations. That’s crucial. We humans are able to project ourselves into situations that are very different from our day-to-day experience. Machines are not, because they don’t have these causal models.
We can hand-craft them, but that’s not enough. We need machines that can discover causal models. To some extent it’s never going to be perfect. We don’t have a perfect causal model of the reality; that’s why we make a lot of mistakes. But we are much better off at doing this than other animals.
Right now, we don’t really have good algorithms for this, but I think if enough people work at it and consider it important, we will make advances.
In March 2017, Treasury secretary Steven Mnuchin said that the idea of humans losing jobs because of AI “is not even on our radar screen.” It might be a threat, he added, in “50 to 100 more years.” That same year, China committed itself to building a $150 billion AI industry by 2030.
And what’s at stake is not just the technological dominance of the United States. At a moment of great anxiety about the state of modern liberal democracy, AI in China appears to be an incredibly powerful enabler of authoritarian rule. Is the arc of the digital revolution bending toward tyranny, and is there any way to stop it?
AFTER THE END of the Cold War, conventional wisdom in the West came to be guided by two articles of faith: that liberal democracy was destined to spread across the planet, and that digital technology would be the wind at its back.
As the era of social media kicked in, the techno-optimists’ twin articles of faith looked unassailable. In 2009, during Iran’s Green Revolution, outsiders marveled at how protest organizers on Twitter circumvented the state’s media blackout. A year later, the Arab Spring toppled regimes in Tunisia and Egypt and sparked protests across the Middle East, spreading with all the virality of a social media phenomenon—because, in large part, that’s what it was.
“If you want to liberate a society, all you need is the internet,” said Wael Ghonim, an Egyptian Google executive who set up the primary Facebook group that helped galvanize dissenters in Cairo.
It didn’t take long, however, for the Arab Spring to turn into winter
…. in 2013 the military staged a successful coup. Soon thereafter, Ghonim moved to California, where he tried to set up a social media platform that would favor reason over outrage. But it was too hard to peel users away from Twitter and Facebook, and the project didn’t last long. Egypt’s military government, meanwhile, recently passed a law that allows it to wipe its critics off social media.
Of course, it’s not just in Egypt and the Middle East that things have gone sour. In a remarkably short time, the exuberance surrounding the spread of liberalism and technology has turned into a crisis of faith in both. Overall, the number of liberal democracies in the world has been in steady decline for a decade. According to Freedom House, 71 countries last year saw declines in their political rights and freedoms; only 35 saw improvements.
While the crisis of democracy has many causes, social media platforms have come to seem like a prime culprit.
Which leaves us where we are now: Rather than cheering for the way social platforms spread democracy, we are busy assessing the extent to which they corrode it.
VLADIMIR PUTIN IS a technological pioneer when it comes to cyberwarfare and disinformation. And he has an opinion about what happens next with AI: “The one who becomes the leader in this sphere will be the ruler of the world.”
It’s not hard to see the appeal for much of the world of hitching their future to China. Today, as the West grapples with stagnant wage growth and declining trust in core institutions, more Chinese people live in cities, work in middle-class jobs, drive cars, and take vacations than ever before. China’s plans for a tech-driven, privacy-invading social credit system may sound dystopian to Western ears, but it hasn’t raised much protest there.
In a recent survey by the public relations consultancy Edelman, 84 percent of Chinese respondents said they had trust in their government. In the US, only a third of people felt that way.
… for now, at least, conflicting goals, mutual suspicion, and a growing conviction that AI and other advanced technologies are a winner-take-all game are pushing the two countries’ tech sectors further apart.
A permanent cleavage will come at a steep cost and will only give techno-authoritarianism more room to grow.
Source: Wired (click to read the full article)
The scenario played out in ways that probed nine types of dilemmas, asking users to make judgements based on species, the age or gender of the pedestrians, and the number of pedestrians involved. Sometimes other factors were added. Pedestrians might be pregnant, for instance, or be obviously members of very high or very low socio-economic classes.
All up, the researchers collected 39.61 million decisions from 233 countries, dependencies, or territories.
On the positive side, there was a clear consensus on some dilemmas.
“The strongest preferences are observed for sparing humans over animals, sparing more lives, and sparing young lives,”
“Accordingly, these three preferences may be considered essential building blocks for machine ethics, or at least essential topics to be considered by policymakers.”
The four most spared characters in the game, they report, were “the baby, the little girl, the little boy, and the pregnant woman”.
So far, then, so universal, but after that divisions in decision-making started to appear and do so quite starkly. The determinants, it seems, were social, cultural and perhaps even economic.
Awad’s team noted, for instance, that there were significant differences between “individualistic cultures and collectivistic cultures” – a division that also correlated, albeit roughly, with North American and European cultures, in the former, and Asian cultures in the latter.
In individualistic cultures – “which emphasise the distinctive value of each individual” – there was an emphasis on saving a greater number of characters. In collectivistic cultures – “which emphasise the respect that is due to older members of the community” – there was a weaker emphasis on sparing the young.
Given that car-makers and models are manufactured on a global scale, with regional differences extending only to matters such as which side the steering wheel should be on and what the badge says, the finding flags a major issue for the people who will eventually have to program the behaviour of the vehicles.
“Ethics, like technology, is design,”
“As we’re designing the system, we’re designing society. Ethical rules that we choose to put in that design [impact the society]… Nothing is self evident. Everything has to be put out there as something that we think we will be a good idea as a component of our society.”
If your tech philosophy is the equivalent of ‘move fast and break things’ it’s a failure of both imagination and innovation to not also keep rethinking policies and terms of service — “to a certain extent from scratch” — to account for fresh social impacts, he argued in the speech.
He described today’s digital platforms as “sociotechnical systems” — meaning “it’s not just about the technology when you click on the link it is about the motivation someone has to make such a great thing because then they are read and the excitement they get just knowing that other people are reading the things that they have written”.
“We must consciously decide on both of these, both the social side and the technical side,”
“[These platforms are] anthropogenic, made by people … Facebook and Twitter are anthropogenic. They’re made by people. They’ve coded by people. And the people who code them are constantly trying to figure out how to make them better.”
First, the site’s artificial intelligence (AI) chooses a story based on what’s popular on the internet right now. Once it picks a topic, it looks at more than a thousand news sources to gather details. Left-leaning sites, right-leaning sites – the AI looks at them all.
Then, the AI writes its own “impartial” version of the story based on what it finds (sometimes in as little as 60 seconds). This take on the news contains the most basic facts, with the AI striving to remove any potential bias. The AI also takes into account the “trustworthiness” of each source, something Knowhere’s co-founders preemptively determined. This ensures a site with a stellar reputation for accuracy isn’t overshadowed by one that plays a little fast and loose with the facts.
For some of the more political stories, the AI produces two additional versions labeled “Left” and “Right.” Those skew pretty much exactly how you’d expect from their headlines:
- Impartial: “US to add citizenship question to 2020 census”
- Left: “California sues Trump administration over census citizenship question”
- Right: “Liberals object to inclusion of citizenship question on 2020 census”
Some controversial but not necessarily political stories receive “Positive” and “Negative” spins:
- Impartial: “Facebook scans things you send on messenger, Mark Zuckerberg admits”
- Positive: “Facebook reveals that it scans Messenger for inappropriate content”
- Negative: “Facebook admits to spying on Messenger, ‘scanning’ private images and links”
Even the images used with the stories occasionally reflect the content’s bias. The “Positive” Facebook story features CEO Mark Zuckerberg grinning, while the “Negative” one has him looking like his dog just died.
So, impartial stories written by AI. Pretty neat? Sure. But society changing? We’ll probably need more than a clever algorithm for that.
When it comes to creating safe AI and regulating this technology, these great minds have little clue what they’re doing. They don’t even know where to begin.
I met with Michael Page, the Policy and Ethics Advisor at OpenAI.
Beneath the glittering skyscrapers of the self-proclaimed “city of the future,” he told me of the uncertainty that he faces. He spoke of the questions that don’t have answers, and the fantastically high price we’ll pay if we don’t find them.
The conversation began when I asked Page about his role at OpenAI. He responded that his job is to “look at the long-term policy implications of advanced AI.” If you think that this seems a little intangible and poorly defined, you aren’t the only one. I asked Page what that means, practically speaking. He was frank in his answer: “I’m still trying to figure that out.”
Page attempted to paint a better picture of the current state of affairs by noting that, since true artificial intelligence doesn’t actually exist yet, his job is a little more difficult than ordinary.
He noted that, when policy experts consider how to protect the world from AI, they are really trying to predict the future.
They are trying to, as he put it, “find the failure modes … find if there are courses that we could take today that might put us in a position that we can’t get out of.” In short, these policy experts are trying to safeguard the world of tomorrow by anticipating issues and acting today.
The problem is that they may be faced with an impossible task.
Page is fully aware of this uncomfortable possibility, and readily admits it. “I want to figure out what can we do today, if anything. It could be that the future is so uncertain there’s nothing we can do,” he said.
asked for a concrete prediction of where humanity and AI will together be in a year, or in five years, Page didn’t offer false hope: “I have no idea,”
However, Page and OpenAI aren’t alone in working on finding the solutions. He therefore hopes such solutions may be forthcoming: “Hopefully, in a year, I’ll have an answer. Hopefully, in five years, there will be thousands of people thinking about this,” Page said.
“Fifteen years ago, when we were coming here to Austin to talk about the internet, it was this magical place that was different from the rest of the world,” said Ev Williams, now the CEO of Medium, at a panel over the weekend.
“It was a subset” of the general population, he said, “and everyone was cool. There were some spammers, but that was kind of it. And now it just reflects the world.” He continued: “When we built Twitter, we weren’t thinking about these things. We laid down fundamental architectures that had assumptions that didn’t account for bad behavior. And now we’re catching on to that.”
Questions about the unintended consequences of social networks pervaded this year’s event. Academics, business leaders, and Facebook executives weighed in on how social platforms spread misinformation, encourage polarization, and promote hate speech.
The idea that the architects of our social networks would face their comeuppance in Austin was once all but unimaginable at SXSW, which is credited with launching Twitter, Foursquare, and Meerkat to prominence.
But this year, the festival’s focus turned to what social apps had wrought — to what Chris Zappone, a who covers Russian influence campaigns at Australian newspaper The Age, called at his panel “essentially a national emergency.”
Steve Huffman, the CEO of Reddit discouraged strong intervention from the government. “The foundation of the United States and the First Amendment is really solid,” Huffman said. “We’re going through a very difficult time. And as I mentioned before, our values are being tested. But that’s how you know they’re values. It’s very important that we stand by our values and don’t try to overcorrect.”
Sen. Mark Warner (D-VA), vice chairman of the Senate Select Committee on intelligence, echoed that sentiment. “We’re going to need their cooperation because if not, and you simply leave this to Washington, we’ll probably mess it up,” he said at a panel that, he noted with great disappointment, took place in a room that was more than half empty. “It needs to be more of a collaborative process. But the notion that this is going to go away just isn’t accurate.”
Nearly everyone I heard speak on the subject of propaganda this week said something like “there are no easy answers” to the information crisis.
And if there is one thing that hasn’t changed about SXSW, it was that: a sense that tech would prevail in the end.
“It would also be naive to say we can’t do anything about it,” Ev Williams said. “We’re just in the early days of trying to do something about it.”
Source: The Verge – Casey Newton
In the hours since the news of his death broke, fans have been resurfacing some of their favorite quotes of his, including those from his Reddit AMA two years ago.
He wrote confidently about the imminent development of human-level AI and warned people to prepare for its consequences:
“When it eventually does occur, it’s likely to be either the best or worst thing ever to happen to humanity, so there’s huge value in getting it right.”
When asked if human-created AI could exceed our own intelligence, he replied:
It’s clearly possible for a something to acquire higher intelligence than its ancestors: we evolved to be smarter than our ape-like ancestors, and Einstein was smarter than his parents. The line you ask about is where an AI becomes better than humans at AI design, so that it can recursively improve itself without human help. If this happens, we may face an intelligence explosion that ultimately results in machines whose intelligence exceeds ours by more than ours exceeds that of snails.
As for whether that same AI could potentially be a threat to humans one day?
“AI will probably develop a drive to survive and acquire more resources as a step toward accomplishing whatever goal it has, because surviving and having more resources will increase its chances of accomplishing that other goal,” he wrote. “This can cause problems for humans whose resources get taken away.”
In the forward to Microsoft’s recent book, The Future Computed, executives Brad Smith and Harry Shum proposed that Artificial Intelligence (AI) practitioners highlight their ethical commitments by taking an oath analogous to the Hippocratic Oath sworn by doctors for generations.
In the past, much power and responsibility over life and death was concentrated in the hands of doctors.
Now, this ethical burden is increasingly shared by the builders of AI software.
Future AI advances in medicine, transportation, manufacturing, robotics, simulation, augmented reality, virtual reality, military applications, dictate that AI be developed from a higher moral ground today.
In response, I (Oren Etzioni) edited the modern version of the medical oath to address the key ethical challenges that AI researchers and engineers face …
The oath is as follows:
I swear to fulfill, to the best of my ability and judgment, this covenant:
I will respect the hard-won scientific gains of those scientists and engineers in whose steps I walk, and gladly share such knowledge as is mine with those who are to follow.
I will apply, for the benefit of the humanity, all measures required, avoiding those twin traps of over-optimism and uniformed pessimism.
I will remember that there is an art to AI as well as science, and that human concerns outweigh technological ones.
Most especially must I tread with care in matters of life and death. If it is given me to save a life using AI, all thanks. But it may also be within AI’s power to take a life; this awesome responsibility must be faced with great humbleness and awareness of my own frailty and the limitations of AI. Above all, I must not play at God nor let my technology do so.
I will respect the privacy of humans for their personal data are not disclosed to AI systems so that the world may know.
I will consider the impact of my work on fairness both in perpetuating historical biases, which is caused by the blind extrapolation from past data to future predictions, and in creating new conditions that increase economic or other inequality.
My AI will prevent harm whenever it can, for prevention is preferable to cure.
My AI will seek to collaborate with people for the greater good, rather than usurp the human role and supplant them.
I will remember that I am not encountering dry data, mere zeros and ones, but human beings, whose interactions with my AI software may affect the person’s freedom, family, or economic stability. My responsibility includes these related problems.
I will remember that I remain a member of society, with special obligations to all my fellow human beings.
Source: TechCrunch – Oren Etzioni
For a field that was not well known outside of academia a decade ago, artificial intelligence has grown dizzyingly fast.
Tech companies from Silicon Valley to Beijing are betting everything on it, venture capitalists are pouring billions into research and development, and start-ups are being created on what seems like a daily basis. If our era is the next Industrial Revolution, as many claim, A.I. is surely one of its driving forces.
I worry, however, that enthusiasm for A.I. is preventing us from reckoning with its looming effects on society. Despite its name, there is nothing “artificial” about this technology — it is made by humans, intended to behave like humans and affects humans. So if we want it to play a positive role in tomorrow’s world, it must be guided by human concerns.
I call this approach “human-centered A.I.” It consists of three goals that can help responsibly guide the development of intelligent machines.
- First, A.I. needs to reflect more of the depth that characterizes our own intelligence.
- the second goal of human-centered A.I.: enhancing us, not replacing us.
- the third goal of human-centered A.I.: ensuring that the development of this technology is guided, at each step, by concern for its effect on humans.
No technology is more reflective of its creators than A.I. It has been said that there are no “machine” values at all, in fact; machine values are human values.
A human-centered approach to A.I. means these machines don’t have to be our competitors, but partners in securing our well-being. However autonomous our technology becomes, its impact on the world — for better or worse — will always be our responsibility.
Fei-Fei Li is a professor of computer science at Stanford, where she directs the Stanford Artificial Intelligence Lab, and the chief scientist for A.I. research at Google Cloud.
Twitter wants experts to help it learn to be a less toxic place online.
The company, which has been plagued by a number of election-meddling, harassment, bot, and scam-related scandals since the 2016 presidential election, announced that it was looking to partner with outside experts to help “identify how we measure the health of Twitter.”
The company said it was looking to find new ways to fight abuse and spam, and to encourage “healthy” debates and conversations.
Twitter is now inviting experts to help define “what health means for Twitter” by submitting proposals for studies.
Huge MIT Study of ‘Fake News’: Falsehoods Win on Twitter
Falsehoods almost always beat out the truth on Twitter, penetrating further, faster, and deeper into the social network than accurate information.
The massive new study analyzes every major contested news story in English across the span of Twitter’s existence—some 126,000 stories, tweeted by 3 million users, over more than 10 years—and finds
that the truth simply cannot compete with hoax and rumor.
By every common metric, falsehood consistently dominates the truth on Twitter, the study finds: Fake news and false rumors reach more people, penetrate deeper into the social network, and spread much faster than accurate stories.
their work has implications for Facebook, YouTube, and every major social network. Any platform that regularly amplifies engaging or provocative content runs the risk of amplifying fake news along with it.
Twitter users seem almost to prefer sharing falsehoods. Even when the researchers controlled for every difference between the accounts originating rumors—like whether that person had more followers or was verified—falsehoods were still 70 percent more likely to get retweeted than accurate news.
In short, social media seems to systematically amplify falsehood at the expense of the truth, and no one—neither experts nor politicians nor tech companies—knows how to reverse that trend.
It is a dangerous moment for any system of government premised on a common public reality.
Source: The Atlantic
In the summer of 2017, a now infamous memo came to light. Written by James Damore, then an engineer at Google, it claimed that the under-representation of women in tech was partly caused by inherent biological differences between men and women.
That Google memo is an extreme example of an imbalance in how different ways of knowing are valued.
Silicon Valley tech companies draw on innovative technical theory but have yet to really incorporate advances in social theory.
Social theorists in fields such as sociology, geography, and science and technology studies have shown how race, gender and class biases inform technical design.
So there’s irony in the fact that employees hold sexist and racist attitudes, yet ‘we are supposed to believe that these same employees are developing “neutral” or “objective” decision-making tools’, as the communications scholar Safiya Umoja Noble at the University of Southern California argues in her book Algorithms of Oppression (2018).
If tech companies are serious about building a better society, and aren’t just paying lip service to justice for their own gain, they must attend more closely to social theory.
If social insights were easy, and if practice followed readily from understanding, then racism, poverty and other debilitating systems of power and inequality would be a thing of the past.
New insights about society are as challenging to produce as the most rarified scientific theorems – and addressing pressing contemporary problems requires as many kinds of knowers and ways of knowing as possible.
A year ago this past Friday, Mark Zuckerberg published a lengthy post titled “Building a Global Community.” It offered a comprehensive statement from the Facebook CEO on how he planned to move the company away from its longtime mission of making the world “more open and connected” to instead create “the social infrastructure … to build a global community.”
“Social media is a short-form medium where resonant messages get amplified many times,” Zuckerberg wrote. “This rewards simplicity and discourages nuance. At its best, this focuses messages and exposes people to different ideas. At its worst, it oversimplifies important topics and pushes us towards extremes.”
By that standard, Robert Mueller’s indictment of of a Russian troll farm last week showed social media at its worst.
Facebook has estimated that 126 million users saw Russian disinformation on the platform during the 2016 campaign. The effects of that disinformation went beyond likes, comments, and shares. Coordinating with unwitting Americans through social media platforms, Russians staged rallies and paid Americans to participate in them. In one case, they hired Americans to build a cage on a flatbed truck and dress up in a Hillary Clinton costume to promote the idea that she should be put in jail.
Russians spent thousands of dollars a month promoting those groups on Facebook and other sites, according to the indictment. They meticulously tracked the growth of their audience, creating and distributing reports on their growing influence. They worked to make their posts seem more authentically American, and to create posts more likely to spread virally through the mechanisms of the social networks.
the dark side of “developing the social infrastructure for community” is now all too visible.
The tools that are so useful for organizing a parenting group are just as effective at coercing large groups of Americans into yelling at each other. Facebook dreams of serving one global community, when in fact it serves — and enables —countless agitated tribes.
The more Facebook pushes us into groups, the more it risks encouraging the kind of polarization that Russia so eagerly exploited.
Source: The Verge
As autonomous and intelligent systems become more and more ubiquitous and sophisticated, developers and users face an important question:
How do we ensure that when these technologies are in a position to make a decision, they make the right decision — the ethically right decision?
It’s a complicated question. And there’s not one single right answer.
But there is one thing that people who work in the budding field of AI ethics seem to agree on.
“I think there is a domination of Western philosophy, so to speak, in AI ethics,” said Dr. Pak-Hang Wong, who studies Philosophy of Technology and Ethics at the University of Hamburg, in Germany. “By that I mean, when we look at AI ethics, most likely they are appealing to values … in the Western philosophical traditions, such as value of freedom, autonomy and so on.”
Wong is among a group of researchers trying to widen that scope, by looking at how non-Western value systems — including Confucianism, Buddhism and Ubuntu — can influence how autonomous and intelligent designs are developed and how they operate.
“We’re providing standards as a starting place. And then from there, it may be a matter of each tradition, each culture, different governments, establishing their own creation based on the standards that we are providing.”
Jared Bielby, who heads the Classical Ethics committee
A Cambodian opposition leader has filed a petition in a California court against Facebook, demanding the company disclose its transactions with his country’s authoritarian prime minister, whom he accuses of falsely inflating his popularity through purchased “likes” and spreading fake news.
The petition, filed Feb. 8, brings the ongoing debate over Facebook’s power to undermine democracies into a legal setting.
[The petitioner, Sam Rainsy] alleges that Hun had used “click farms” to artificially boost his popularity, effectively buying “likes.”
The petition says that Hun had achieved astonishing Facebook fame in a very short time, raising questions about whether this popularity was legitimate. For instance, the petition says, Hun Sen’s page is “liked” by 9.4 million people “even though only 4.8 million Cambodians use Facebook,” and that millions of these “likes” come from India, the Philippines, Brazil, and Myanmar, countries that don’t speak Khmer, the sole language the page is written in, and that are known for “click farms.”
According to leaked correspondence that the petition refers to, the Cambodian government’s payments to Facebook totaled $15,000 a day “in generating fake ‘likes’ and advertising on the network to help dissiminate[sic] the regime’s propaganda and drown-out any competing voices.”
“The biggest misconception is that we have it. I wouldn’t even call it AI. I would say it’s right to call the field AI, we’re pursuing AI, but we don’t have it yet,” he said.
Gray says at present, humans are still sorely needed.
“No matter how you look at it, there’s a lot of handcrafting [involved]. We have ever increasingly powerful tools but we haven’t made the leap yet,”
According to Gray, we’re only seeing “human-level performance” for narrowly defined tasks. Most machine learning-based algorithms have to analyze thousands of examples, and haven’t achieved the idea of one-shot or few-shot learnings.
“Once you go slightly beyond that data set and it looks different. Humans win. There will always be things that humans can do that AI can’t do. You still need human data scientists to do the data preparation part — lots of blood and guts stuff that requires open domain knowledge about the world,” he said.
Artificial intelligence is perhaps the most hyped yet misunderstood field of study today.
Gray said while we may not be experiencing the full effects of AI yet, it’s going to happen a lot faster than we think — and that’s where the fear comes in.
“Everything moves on an exponential curve. I really do believe that we will start to see entire classes of jobs getting impacted.
My fear is that we won’t have the social structures and agreements on what we should do to keep pace with that. I’m not sure if that makes me optimistic or pessimistic.”
This week my colleague Dieter Vanderelst presented our paper: “The Dark Side of Ethical Robots” at AIES 2018 in New Orleans.
I blogged about Dieter’s very elegant experiment here, but let me summarize. With two NAO robots he set up a demonstration of an ethical robot helping another robot acting as a proxy human, then showed that with a very simple alteration of the ethical robot’s logic it is transformed into a distinctly unethical robot—behaving either competitively or aggressively toward the proxy human.
Here are our paper’s key conclusions:
The ease of transformation from ethical to unethical robot is hardly surprising. It is a straightforward consequence of the fact that both ethical and unethical behaviors require the same cognitive machinery with—in our implementation—only a subtle difference in the way a single value is calculated. In fact, the difference between an ethical (i.e. seeking the most desirable outcomes for the human) robot and an aggressive (i.e. seeking the least desirable outcomes for the human) robot is a simple negation of this value.
Let us examine the risks associated with ethical robots and if, and how, they might be mitigated. There are three.
- First there is the risk that an unscrupulous manufacturer
- Perhaps more serious is the risk arising from robots that have user adjustable ethics settings.
- But even hard-coded ethics would not guard against undoubtedly the most serious risk of all, which arises when those ethical rules are vulnerable to malicious hacking.
It is very clear that guaranteeing the security of ethical robots is beyond the scope of engineering and will need regulatory and legislative efforts.
Considering the ethical, legal and societal implications of robots, it becomes obvious that robots themselves are not where responsibility lies. Robots are simply smart machines of various kinds and the responsibility to ensure they behave well must always lie with human beings. In other words, we require ethical governance, and this is equally true for robots with or without explicit ethical behaviors.
Two years ago I thought the benefits of ethical robots outweighed the risks. Now I’m not so sure.
I now believe that – even with strong ethical governance—the risks that a robot’s ethics might be compromised by unscrupulous actors are so great as to raise very serious doubts over the wisdom of embedding ethical decision making in real-world safety critical robots, such as driverless cars. Ethical robots might not be such a good idea after all.
Thus, even though we’re calling into question the wisdom of explicitly ethical robots, that doesn’t change the fact that we absolutely must design all robots to minimize the likelihood of ethical harms, in other words we should be designing implicitly ethical robots within Moor’s schema.
Early Facebook and Google Employees Form Coalition to Fight What They Built
A group of Silicon Valley technologists who were early employees at Facebook and Google, alarmed over the ill effects of social networks and smartphones, are banding togethe to challenge the companies they helped build.
The cohort is creating a union of concerned experts called the Center for Humane Technology. Along with the nonprofit media watchdog group Common Sense Media, it also plans an anti-tech addiction lobbying effort and an ad campaign at 55,000 public schools in the United States.
The campaign, titled The Truth About Tech
“We were on the inside,” said Tristan Harris, a former in-house ethicist at Google who is heading the new group. “We know what the companies measure. We know how they talk, and we know how the engineering works.”
An unprecedented alliance of former employees of some of today’s biggest tech companies. Apart from Mr. Harris, the center includes Sandy Parakilas, a former Facebook operations manager; Lynn Fox, a former Apple and Google communications executive; Dave Morin, a former Facebook executive; Justin Rosenstein, who created Facebook’s Like button and is a co-founder of Asana; Roger McNamee, an early investor in Facebook; and Renée DiResta, technologist who studies bots.
“Facebook appeals to your lizard brain — primarily fear and anger. And with smartphones, they’ve got you for every waking moment. This is an opportunity for me to correct a wrong.” Roger McNamee, an early investor in Facebook
Edits from a Microsoft podcast with Dr. Ece Kamar, a senior researcher in the Adaptive Systems and Interaction Group at Microsoft Research.
I’m very interested in the complementarity between machine intelligence and human intelligence and what kind of value can be generated from using both of them to make daily life better.
We try to build systems that can interact with people, that can work with people and that can be beneficial for people. Our group has a big human component, so we care about modelling the human side. And we also work on machine-learning decision-making algorithms that can make decisions appropriately for the domain they were designed for.
My main area is the intersection between humans and AI.
we are actually at an important point in the history of AI where a lot of critical AI systems are entering the real world and starting to interact with people. So, we are at this inflection point where, whatever AI does, and the way we build AI, have consequences for the society we live in.
So, let’s look for what can augment human intelligence, what can make human intelligence better.” And that’s what my research focuses on. I really look for the complementarity in intelligences, and building these experience that can, in the future, hopefully, create super-human experiences.
So, a lot of the work I do focuses on two big parts: one is how we can build AI systems that can provide value for humans in their daily tasks and making them better. But also thinking about how humans may complement AI systems.
And when we look at our AI practices, it is actually very data-dependent these days … However, data collection is not a real science. We have our insights, we have our assumptions and we do data collection that way. And that data is not always the perfect representation of the world. This creates blind spots. When our data is not the right representation of the world and it’s not representing everything we care about, then our models cannot learn about some of the important things.
“AI is developed by people, with people, for people.”
And when I talk about building AI for people, a lot of the systems we care about are human-driven. We want to be useful for human.
We are thinking about AI algorithms that can bias their decisions based on race, gender, age. They can impact society and there are a lot of areas like judicial decision-making that touches law. And also, for every vertical, we are building these systems and I think we should be working with the domain experts from these verticals. We need to talk to educators. We need to talk to doctors. We need to talk to people who understand what that domain means and all the special considerations we should be careful about.
So, I think if we can understand what this complementary means, and then build AI that can use the power of AI to complement what humans are good at and support them in things that they want to spend time on, I think that is the beautiful future I foresee from the collaboration of humans and machines.
Source: Microsoft Research Podcast
On Wednesday, Facebook announced that its recent overhaul of the News Feed algorithm caused users to collectively spend 50 million fewer hours per day on the service. Another worrying statistic: Facebook reported that daily active users fell in the US and Canada for the first time.
But Facebook also reported impressive fourth-quarter results despite the changes, which are designed to weed out content from media publishers and brand pages and instead promote posts that spur “meaningful” engagement like comments, rather than likes and shares.
On the earnings call Wednesday, the messaging from Facebook’s management was clear:
Decreased usage might actually be a good thing, leading to better ads with higher margins. It’s also good news for Facebook’s video product, Watch, which features high-quality videos produced by traditional media companies and Facebook itself.
“By focusing on meaningful interaction, I expect the time we all spend on Facebook will be more valuable. I always believe that if we do the right thing, and deliver deeper value, our community and our business will be stronger over the long term.” Mark Zuckerber
Facebook may be facing a reckoning for its role and influence on politics, media, and social well being, but Wall Street seems to be ignoring all that for now.
Source: Business Insider
“But it is a test that I am confident we can meet”
Thereas May, Prime Minister UK
The prime minister is to say she wants the UK to lead the world in deciding how artificial intelligence can be deployed in a safe and ethical manner.
Theresa May will say at the World Economic Forum in Davos that a new advisory body, previously announced in the Autumn Budget, will co-ordinate efforts with other countries.
In addition, she will confirm that the UK will join the Davos forum’s own council on artificial intelligence.
But others may have stronger claims.
Earlier this week, Google picked France as the base for a new research centre dedicated to exploring how AI can be applied to health and the environment.
Facebook also announced it was doubling the size of its existing AI lab in Paris, while software firm SAP committed itself to a 2bn euro ($2.5bn; £1.7bn) investment into the country that will include work on machine learning.
Meanwhile, a report released last month by the Eurasia Group consultancy suggested that the US and China are engaged in a “two-way race for AI dominance”.
It predicted Beijing would take the lead thanks to the “insurmountable” advantage of offering its companies more flexibility in how they use data about its citizens.
she is expected to say that the UK is recognised as first in the world for its preparedness to “bring artificial intelligence into government”.
Pichai also warned that the development of artificial intelligence could pose as much risk as that of fire if its potential is not harnessed correctly.
“AI is one of the most important things humanity is working on” Pichai said in an interview with MSNBC and Recode
“My point is AI is really important, but we have to be concerned about it,” Pichai said. “It’s fair to be worried about it—I wouldn’t say we’re just being optimistic about it— we want to be thoughtful about it. AI holds the potential for some of the biggest advances we’re going to see.”
The ethics of artificial intelligence must be central to its development
Humanity faces a wide range of challenges that are characterised by extreme complexity
… the successful integration of AI technologies into our social and economic world creates its own challenges. They could either help overcome economic inequality or they could worsen it if the benefits are not distributed widely.
They could shine a light on damaging human biases and help society address them, or entrench patterns of discrimination and perpetuate them. Getting things right requires serious research into the social consequences of AI and the creation of partnerships to ensure it works for the public good.
This is why I predict the study of the ethics, safety and societal impact of AI is going to become one of the most pressing areas of enquiry over the coming year.
It won’t be easy: the technology sector often falls into reductionist ways of thinking, replacing complex value judgments with a focus on simple metrics that can be tracked and optimised over time.
There has already been valuable work done in this area. For example, there is an emerging consensus that it is the responsibility of those developing new technologies to help address the effects of inequality, injustice and bias. In 2018, we’re going to see many more groups start to address these issues.
Of course, it’s far simpler to count likes than to understand what it actually means to be liked and the effect this has on confidence or self-esteem.
Progress in this area also requires the creation of new mechanisms for decision-making and voicing that include the public directly. This would be a radical change for a sector that has often preferred to resolve problems unilaterally – or leave others to deal with them.
We need to do the hard, practical and messy work of finding out what ethical AI really means. If we manage to get AI to work for people and the planet, then the effects could be transformational. Right now, there’s everything to play for.
DeepMind made this announcement Oct 2017
Google-owned DeepMind has announced the formation of a major new AI research unit comprised of full-time staff and external advisors
As we hand over more of our lives to artificial intelligence systems, keeping a firm grip on their ethical and societal impact is crucial.
DeepMind Ethics & Society (DMES), a unit comprised of both full-time DeepMind employees and external fellows, is the company’s latest attempt to scrutinise the societal impacts of the technologies it creates.
DMES will work alongside technologists within DeepMind and fund external research based on six areas: privacy transparency and fairness; economic impacts; governance and accountability; managing AI risk; AI morality and values; and how AI can address the world’s challenges.
Its aim, according to DeepMind, is twofold: to help technologists understand the ethical implications of their work and help society decide how AI can be beneficial.
“We want these systems in production to be our highest collective selves. We want them to be most respectful of human rights, we want them to be most respectful of all the equality and civil rights laws that have been so valiantly fought for over the last sixty years.” [Mustafa Suleyman]
The other night, my nine-year-old daughter (who is, of course, the most tech-savvy person in the house), introduced me to a new Amazon Alexa skill.
“Alexa, start a conversation,” she said.
We were immediately drawn into an experience with new bot, or, as the technologists would say, “conversational user interface” (CUI). It was, we were told, the recent winner in an Amazon AI competition from the University of Washington.
At first, the experience was fun, but when we chose to explore a technology topic, the bot responded, “have you heard of Net Neutrality?” What we experienced thereafter was slightly discomforting.
The bot seemingly innocuously cited a number of articles that she “had read on the web” about the FCC, Ajit Pai, and the issue of net neutrality. But here’s the thing: All four articles she recommended had a distinct and clear anti-Ajit Pai bias.
Now, the topic of Net Neutrality is a heated one and many smart people make valid points on both sides, including Fred Wilson and Ben Thompson. That is how it should be.
But the experience of the Alexa CUI should give you pause, as it did me.
To someone with limited familiarity with the topic of net neutrality, the voice seemed soothing and the information unbiased. But if you have a familiarity with the topic, you might start to wonder, “wait … am I being manipulated on this topic by an Amazon-owned AI engine to help the company achieve its own policy objectives?”
The experience highlights some of the risks of the AI-powered future into which we are hurtling at warp speed.
If you are going to trust your decision-making to a centralized AI source, you need to have 100 percent confidence in:
- The integrity and security of the data (are the inputs accurate and reliable, and can they be manipulated or stolen?)
- The machine learning algorithms that inform the AI (are they prone to excessive error or bias, and can they be inspected?)
- The AI’s interface (does it reliably represent the output of the AI and effectively capture new data?)
In a centralized, closed model of AI, you are asked to implicitly trust in each layer without knowing what is going on behind the curtains.
Welcome to the world of Blockchain+AI.
3 blockchain projects tackling decentralized data and AI (click here to read the blockchain projects)
Source: Venture Beat
You see a man walking toward you on the street. He reminds you of someone from long ago. Such as a high school classmate, who belonged to the football team? Wasn’t a great player but you were fond of him then. You don’t recall him attending fifth, 10th and 20th reunions. He must have moved away and established his life there and cut off his ties to his friends here.
You look at his face and you really can’t tell if it’s Bob for sure. You had forgotten many of his key features and this man seems to have gained some weight.
The distance between the two of you is quickly closing and your mind is running at full speed trying to decide if it is Bob.
At this moment, you have a few choices. A decision tree will emerge and you will need to choose one of the available options.
In the logic diagram I show, there are some question that is influenced by the emotion. B2) “Nah, let’s forget it” and C) and D) are results of emotional decisions and have little to do with fact this may be Bob or not.
The human decision-making process is often influenced by emotion, which is often independent of fact.
You decision to drop the idea of meeting Bob after so many years is caused by shyness, laziness and/or avoiding some embarrassment in case this man is not Bob. The more you think about this decision-making process, less sure you’d become. After all, if you and Bob hadn’t spoken for 20 years, maybe we should leave the whole thing alone.
Thus, this is clearly the result of human intelligence working.
If this were artificial intelligence, chances are decisions B2, C and D wouldn’t happen. Machines today at their infantile stage of development do not know such emotional feeling as “too much trouble,” hesitation due to fear of failing (Bob says he isn’t Bob), or laziness and or “too complicated.” In some distant time, these complex feelings and deeds driven by the emotion would be realized, I hope. But, not now.
At this point of the state of art of AI, a machine would not hesitate once it makes a decision. That’s because it cannot hesitate. Hesitation is a complex emotional decision that a machine simply cannot perform.
There you see a huge crevice between the human intelligence and AI.
In fact, animals (remember we are also an animal) display complex emotional decisions daily. Now, are you getting some feeling about human intelligence and AI?
Shintaro “Sam” Asano was named by the Massachusetts Institute of Technology in 2011 as one of the 10 most influential inventors of the 20th century who improved our lives. He is a businessman and inventor in the field of electronics and mechanical systems who is credited as the inventor of the portable fax machine.
But that general principle — that we’re not treating people well enough with digital systems — still bothers me. I do still think that is very true.
Well, this is maybe the greatest tragedy in the history of computing, and it goes like this: there was a well-intentioned, sweet movement in the ‘80s to try to make everything online free. And it started with free software and then it was free music, free news, and other free services.
But, at the same time, it’s not like people were clamoring for the government to do it or some sort of socialist solution. If you say, well, we want to have entrepreneurship and capitalism, but we also want it to be free, those two things are somewhat in conflict, and there’s only one way to bridge that gap, and it’s through the advertising model.
And advertising became the model of online information, which is kind of crazy. But here’s the problem: if you start out with advertising, if you start out by saying what I’m going to do is place an ad for a car or whatever, gradually, not because of any evil plan — just because they’re trying to make their algorithms work as well as possible and maximize their shareholders value and because computers are getting faster and faster and more effective algorithms —
what starts out as advertising morphs into behavior modification.
A second issue is that people who participate in a system of this time, since everything is free since it’s all being monetized, what reward can you get? Ultimately, this system creates assholes, because if being an asshole gets you attention, that’s exactly what you’re going to do. Because there’s a bias for negative emotions to work better in engagement, because the attention economy brings out the asshole in a lot of other people, the people who want to disrupt and destroy get a lot more efficiency for their spend than the people who might be trying to build up and preserve and improve.
Q: What do you think about programmers using consciously addicting techniques to keep people hooked to their products?
A: There’s a long and interesting history that goes back to the 19th century, with the science of Behaviorism that arose to study living things as though they were machines.
Behaviorists had this feeling that I think might be a little like this godlike feeling that overcomes some hackers these days, where they feel totally godlike as though they have the keys to everything and can control people
I think our responsibility as engineers is to engineer as well as possible, and to engineer as well as possible, you have to treat the thing you’re engineering as a product.
You can’t respect it in a deified way.
It goes in the reverse. We’ve been talking about the behaviorist approach to people, and manipulating people with addictive loops as we currently do with online systems.
In this case, you’re treating people as objects.
It’s the flipside of treating machines as people, as AI does. They go together. Both of them are mistakes
Source: Read the extensive interview at Business Insider
Various computer scientists, researchers, lawyers and other techies have recently been attending bi-monthly meetings in Montreal to discuss life’s big questions — as they relate to our increasingly intelligent machines.
Should a computer give medical advice? Is it acceptable for the legal system to use algorithms in order to decide whether convicts get paroled? Can an artificial agent that spouts racial slurs be held culpable?
And perhaps most pressing for many people: Are Facebook and other social media applications capable of knowing when a user is depressed or suffering a manic episode — and are these people being targeted with online advertisements in order to exploit them at their most vulnerable?
Researchers such as Abhishek Gupta are trying to help Montreal lead the world in ensuring AI is developed responsibly.
“The spotlight of the world is on (Montreal),” said Gupta, an AI ethics researcher at McGill University who is also a software developer in cybersecurity at Ericsson.
His bi-monthly “AI ethics meet-up” brings together people from around the city who want to influence the way researchers are thinking about machine-learning.
“In the past two months we’ve had six new AI labs open in Montreal,” Gupta said. “It makes complete sense we would also be the ones who would help guide the discussion on how to do it ethically.”
In November, Gupta and Universite de Montreal researchers helped create the Montreal Declaration for a Responsible Development of Artificial Intelligence, which is a series of principles seeking to guide the evolution of AI in the city and across the planet.
Its principles are broken down into seven themes: well-being, autonomy, justice, privacy, knowledge, democracy and responsibility.
“How do we ensure that the benefits of AI are available to everyone?” Gupta asked his group. “What types of legal decisions can we delegate to AI?”
Doina Precup, a McGill University computer science professor and the Montreal head of DeepMind … said the global industry is starting to be preoccupied with the societal consequences of machine-learning, and Canadian values encourage the discussion.
“Montreal is a little ahead because we are in Canada,” Precup said. “Canada, compared to other parts of the world, has a different set of values that are more oriented towards ensuring everybody’s wellness. The background and culture of the country and the city matter a lot.”
That’s the view of the Institute of Electrical and Electronics Engineers (IEEE) which this week released for feedback its second Ethically Aligned Design document in an attempt
to ensure such systems “remain human-centric”.
“These systems have to behave in a way that is beneficial to people beyond reaching functional goals and addressing technical problems. This will allow for an elevated level of trust between people and technology that is needed for its fruitful, pervasive use in our daily lives,” the document states.
“Defining what exactly ‘right’ and ‘good’ are in a digital future is a question of great complexity that places us at the intersection of technology and ethics,”
“Throwing our hands up in air crying ‘it’s too hard’ while we sit back and watch technology careen us forward into a future that happens to us, rather than one we create, is hardly a viable option.
“This publication is a truly game-changing and promising first step in a direction – which has often felt long in coming – toward breaking the protective wall of specialisation that has allowed technologists to disassociate from the societal impacts of their technologies.”
“It will demand that future tech leaders begin to take responsibility for and think deeply about the non-technical impact on disempowered groups, on privacy and justice, on physical and mental health, right down to unpacking hidden biases and moral implications. It represents a positive step toward ensuring the technology we build as humans genuinely benefits us and our planet,” [University of Sydney software engineering Professor Rafael Calvo.]
“We believe explicitly aligning technology with ethical values will help advance innovation with these new tools while diminishing fear in the process” the IEEE said.
Source: Computer World
These days, people want more intelligent answers: Maybe they’d like to gather the pros and cons of a certain exercise plan or figure out whether the latest Marvel movie is worth seeing. They might even turn to their favorite search tool with only the vaguest of requests, such as, “I’m hungry.”
When people make requests like that, they don’t just want a list of websites. They might want a personalized answer, such as restaurant recommendations based on the city they are traveling in. Or they might want a variety of answers, so they can get different perspectives on a topic. They might even need help figuring out the right question to ask.
At a Microsoft event in San Francisco on Wednesday, Microsoft executives showcased a number of advances in its Bing search engine, Cortana intelligent assistant and Microsoft Office 365 productivity tools that use artificial intelligence to help people get more nuanced information and assist with more complex needs.
“AI has come a long way in the ability to find information, but making sense of that information is the real challenge,” said Kristina Behr, a partner design and planning program manager with Microsoft’s Artificial Intelligence and Research group.
Microsoft demonstrated some of the most recent AI-driven advances in intelligent search that are aimed at giving people richer, more useful information.
Another new, AI-driven advance in Bing is aimed at getting people multiple viewpoints on a search query that might be more subjective.
For example, if you ask Bing “is cholesterol bad,” you’ll see two different perspectives on that question.
That’s part of Microsoft’s effort to acknowledge that sometimes a question doesn’t have a clear black and white answer.
“As Bing, what we want to do is we want to provide the best results from the overall web. We want to be able to find the answers and the results that are the most comprehensive, the most relevant and the most trustworthy,” Ribas said.
“Often people are seeking answers that go beyond something that is a mathematical equation. We want to be able to frame those opinions and articulate them in a way that’s also balanced and objective.”
The rise of Machine Learning is every bit as far reaching as the rise of computing itself.
A vast new ecosystem of techniques and infrastructure are emerging in the field of machine learning and we are just beginning to learn their full capabilities. But with the exciting things that people can do, there are some really concerning problems arising.
Forms of bias, stereotyping and unfair determination are being found in machine vision systems, object recognition models, and in natural language processing and word embeddings. High profile news stories about bias have been on the rise, from women being less likely to be shown high paying jobs to gender bias and object recognition datasets like MS COCO, to racial disparities in education AI systems.
What is bias?
Bias is a skew that produces a type of harm.
Where does bias come from?
Commonly from Training data. It can be incomplete, biased or otherwise skewed. It can draw from non-representative samples that are wholly defined before use. Sometimes it is not obvious because it was constructed in a non-transparent way. In addition to human labeling, other ways that human biases and cultural assumptions can creep in ending up in exclusion or overrepresentation of subpopulation. Case in point: stop-and-frisk program data used as training data by an ML system. This dataset was biased due to systemic racial discrimination in policing.
Harms of allocation
Majority of the literature understand bias as harms of allocation. Allocative harm is when a system allocates or withholds certain groups, an opportunity or resource. It is an economically oriented view primarily. Eg: who gets a mortgage, loan etc.
Allocation is immediate, it is a time-bound moment of decision making. It is readily quantifiable. In other words, it raises questions of fairness and justice in discrete and specific transactions.
Harms of representation
It gets tricky when it comes to systems that represent society but don’t allocate resources. These are representational harms. When systems reinforce the subordination of certain groups along the lines of identity like race, class, gender etc.
It is a long-term process that affects attitudes and beliefs. It is harder to formalize and track. It is a diffused depiction of humans and society. It is at the root of all of the other forms of allocative harm.
What can we do to tackle these problems?
- Start working on fairness forensics
- Test our systems: eg: build pre-release trials to see how a system is working across different populations
- How do we track the life cycle of a training dataset to know who built it and what the demographics skews might be in that dataset
- Start taking interdisciplinarity seriously
- Working with people who are not in our field but have deep expertise in other areas Eg: FATE (Fairness Accountability Transparency Ethics) group at Microsoft Research
- Build spaces for collaboration like the AI now institute.
- Think harder on the ethics of classification
The ultimate question for fairness in machine learning is this.
Who is going to benefit from the system we are building? And who might be harmed?
Kate Crawford is a Principal Researcher at Microsoft Research and a Distinguished Research Professor at New York University. She has spent the last decade studying the social implications of data systems, machine learning, and artificial intelligence. Her recent publications address data bias and fairness, and social impacts of artificial intelligence among others.
“Our machines can very easily recognise you among at least 2 billion people in a matter of seconds,” says chief executive and Yitu co-founder Zhu Long, “which would have been unbelievable just three years ago.”
Its platform is also in service with more than 20 provincial public security departments, and is used as part of more than 150 municipal public security systems across the country, and Dragonfly Eye has already proved its worth.
On its very first day of operation on the Shanghai Metro, in January, the system identified a wanted man when he entered a station. After matching his face against the database, Dragonfly Eye sent his photo to a policeman, who made an arrest.
In the following three months, 567 suspected lawbreakers were caught on the city’s underground network.
Whole cities in which the algorithms are working say they have seen a decrease in crime. According to Yitu, which says it gets its figures directly from the local authorities, since the system has been implemented, pickpocketing on Xiamen’s city buses has fallen by 30 per cent; 500 criminal cases have been resolved by AI in Suzhou since June 2015; and police arrested nine suspects identified by algorithms during the 2016 G20 summit in Hangzhou.
“Chinese authorities are collecting and centralising ever more information about hundreds of millions of ordinary people, identifying persons who deviate from what they determine to be ‘normal thought’ and then surveilling them,” says Sophie Richardson, China director at HRW.
Research and advocacy group Human Rights Watch (HRW) says security systems such as those being developed by Yitu “violate privacy and target dissent”.
The NGO calls it a “police cloud” system and believes “it is designed to track and predict the activities of activists, dissidents and ethnic minorities, including those authorities say have extreme thoughts, among others”.
Zhu says, “We all discuss AI as an opportunity for humanity to advance or as a threat to it. What I believe is that we will have to redefine what it is to be human. We will have to ask ourselves what the foundations of our species are.
“At the same time, AI will allow us to explore the boundaries of human intelligence, evaluate its performance and help us understand ourselves better.”
Source: South China Morning Post
“I think we have created tools that are ripping apart the social fabric of how society works”
Palihapitiya’s criticisms were aimed not only at Facebook, but the wider online ecosystem.
“The short-term, dopamine-driven feedback loops we’ve created are destroying how society works,” he said, referring to online interactions driven by “hearts, likes, thumbs-up.” “No civil discourse, no cooperation; misinformation, mistruth. And it’s not an American problem — this is not about Russians ads. This is a global problem.”
He went on to describe an incident in India where hoax messages about kidnappings shared on WhatsApp led to the lynching of seven innocent people.
“That’s what we’re dealing with,” said Palihapitiya. “And imagine taking that to the extreme, where bad actors can now manipulate large swathes of people to do anything you want. It’s just a really, really bad state of affairs.”
In his talk, Palihapitiya criticized not only Facebook, but Silicon Valley’s entire system of venture capital funding.
He said that investors pump money into “shitty, useless, idiotic companies,” rather than addressing real problems like climate change and disease.
Source: The Verge
UPDATE: FACEBOOK RESPONDS
Chamath has not been at Facebook for over six years. When Chamath was at Facebook we were focused on building new social media experiences and growing Facebook around the world. Facebook was a very different company back then and as we have grown we have realised how our responsibilities have grown too. We take our role very seriously and we are working hard to improve. We’ve done a lot of work and research with outside experts and academics to understand the effects of our service on well-being, and we’re using it to inform our product development. We are also making significant investments more in people, technology and processes, and – as Mark Zuckerberg said on the last earnings call – we are willing to reduce our profitability to make sure the right investments are made.
“We want the UAE to become the world’s most prepared country for artificial intelligence,” UAE Vice President and Prime Minister and Ruler of Dubai His Highness Sheikh Mohammed bin Rashid Al Maktoum said during the announcement of the position.
The first person to occupy the state minister for AI post is H.E. Omar Bin Sultan Al Olama. The 27-year-old is currently the Managing Director of the World Government Summit in the Prime Minister’s Office at the Ministry of Cabinet Affairs and the Future,
“We have visionary leadership that wants to implement these technologies to serve humanity better. Ultimately, we want to make sure that we leverage that while, at the same time, overcoming the challenges that might be created by AI.” Al Olama
The UAE hopes its AI initiatives will encourage the rest of the world to really consider how our AI-powered future should look.
“AI is not negative or positive. It’s in between. The future is not going to be a black or white. As with every technology on Earth, it really depends on how we use it and how we implement it,”
“At this point, it’s really about starting conversations — beginning conversations about regulations and figuring out what needs to be implemented in order to get to where we want to be. I hope that we can work with other governments and the private sector to help in our discussions and to really increase global participation in this debate.
With regards to AI, one country can’t do everything. It’s a global effort,” Al Olama said.
Kate Crawford … urged attendees to start considering, and finding ways to mitigate, accidental or intentional harms caused by their creations. “
“Amongst the very real excitement about what we can do there are also some really concerning problems arising”
“In domains like medicine we can’t have these models just be a black box where something goes in and you get something out but don’t know why,” says Maithra Raghu, a machine-learning researcher at Google. On Monday, she presented open-source software developed with colleagues that can reveal what a machine-learning program is paying attention to in data. It may ultimately allow a doctor to see what part of a scan or patient history led an AI assistant to make a particular diagnosis.
“If you have a diversity of perspectives and background you might be more likely to check for bias against different groups” Hanna Wallach a researcher at Microsoft
“If you have a diversity of perspectives and background you might be more likely to check for bias against different groups” Hanna Wallach a researcher at Microsoft
Others in Long Beach hope to make the people building AI better reflect humanity. Like computer science as a whole, machine learning skews towards the white, male, and western. A parallel technical conference called Women in Machine Learning has run alongside NIPS for a decade. This Friday sees the first Black in AI workshop, intended to create a dedicated space for people of color in the field to present their work.
Towards the end of her talk Tuesday, Crawford suggested civil disobedience could shape the uses of AI. She talked of French engineer Rene Carmille, who sabotaged tabulating machines used by the Nazis to track French Jews. And she told today’s AI engineers to consider the lines they don’t want their technology to cross. “Are there some things we just shouldn’t build?” she asked.
[Timnit] Gebru, 34, joined a Microsoft Corp. team called FATE—for Fairness, Accountability, Transparency and Ethics in AI. The program was set up three years ago to ferret out biases that creep into AI data and can skew results.
“I started to realize that I have to start thinking about things like bias. Even my own Phd work suffers from whatever issues you’d have with dataset bias.”
Companies, government agencies and hospitals are increasingly turning to machine learning, image recognition and other AI tools to help predict everything from the credit worthiness of a loan applicant to the preferred treatment for a person suffering from cancer. The tools have big blind spots that particularly effect women and minorities.
“The worry is if we don’t get this right, we could be making wrong decisions that have critical consequences to someone’s life, health or financial stability,” says Jeannette Wing, director of Columbia University’s Data Sciences Institute.
AI also has a disconcertingly human habit of amplifying stereotypes. Phd students at the University of Virginia and University of Washington examined a public dataset of photos and found that the images of people cooking were 33 percent more likely to picture women than men. When they ran the images through an AI model, the algorithms said women were 68 percent more likely to appear in the cooking photos.
Researchers say it will probably take years to solve the bias problem.
The good news is that some of the smartest people in the world have turned their brainpower on the problem. “The field really has woken up and you are seeing some of the best computer scientists, often in concert with social scientists, writing great papers on it,” says University of Washington computer science professor Dan Weld. “There’s been a real call to arms.”
Training an AI platform on social media data, with the intent to reproduce a “human” experience, is fraught with risk. You could liken it to raising a baby on a steady diet of Fox News or CNN, with no input from its parents or social institutions. In either case, you might be breeding a monster.
Ultimately, social data — alone — represents neither who we actually are nor who we should be. Deeper still, as useful as the social graph can be in providing a training set for AI, what’s missing is a sense of ethics or a moral framework to evaluate all this data. From the spectrum of human experience shared on Twitter, Facebook and other networks, which behaviors should be modeled and which should be avoided? Which actions are right and which are wrong? What’s good … and what’s evil?
Here’s where science comes up short. The answers can’t be gleaned from any social data set. The best analytical tools won’t surface them, no matter how large the sample size.
But they just might be found in the Bible. And the Koran, the Torah, the Bhagavad Gita and the Buddhist Sutras. They’re in the work of Aristotle, Plato, Confucius, Descartes and other philosophers both ancient and modern.
AI, to be effective, needs an ethical underpinning. Data alone isn’t enough. AI needs religion — a code that doesn’t change based on context or training set.
In place of parents and priests, responsibility for this ethical education will increasingly rest on frontline developers and scientists.
As emphasized by leading AI researcher Will Bridewell, it’s critical that future developers are “aware of the ethical status of their work and understand the social implications of what they develop.” He goes so far as to advocate study in Aristotle’s ethics and Buddhist ethics so they can “better track intuitions about moral and ethical behavior.”
On a deeper level, responsibility rests with the organizations that employ these developers, the industries they’re part of, the governments that regulate those industries and — in the end — us.
Source: Recode Ryan Holmes is the founder and CEO of Hootsuite.
One of the biggest puzzles about our current predicament with fake news and the weaponisation of social media is why the folks who built this technology are so taken aback by what has happened.
We have a burgeoning genre of “OMG, what have we done?” angst coming from former Facebook and Google employees who have begun to realise that the cool stuff they worked on might have had, well, antisocial consequences.
Put simply, what Google and Facebook have built is a pair of amazingly sophisticated, computer-driven engines for extracting users’ personal information and data trails, refining them for sale to advertisers in high-speed data-trading auctions that are entirely unregulated and opaque to everyone except the companies themselves.
The purpose of this infrastructure was to enable companies to target people with carefully customised commercial messages and, as far as we know, they are pretty good at that.
It never seems to have occurred to them that their advertising engines could also be used to deliver precisely targeted ideological and political messages to voters. Hence the obvious question: how could such smart people be so stupid?
My hunch is it has something to do with their educational backgrounds. Take the Google co-founders. Sergey Brin studied mathematics and computer science. His partner, Larry Page, studied engineering and computer science. Zuckerberg dropped out of Harvard, where he was studying psychology and computer science, but seems to have been more interested in the latter.
Now mathematics, engineering and computer science are wonderful disciplines – intellectually demanding and fulfilling. And they are economically vital for any advanced society. But mastering them teaches students very little about society or history – or indeed about human nature.
As a consequence, the new masters of our universe are people who are essentially only half-educated. They have had no exposure to the humanities or the social sciences, the academic disciplines that aim to provide some understanding of how society works, of history and of the roles that beliefs, philosophies, laws, norms, religion and customs play in the evolution of human culture.
We are now beginning to see the consequences of the dominance of this half-educated elite.
Source: The Gaurdian – John Naughton is professor of the public understanding of technology at the Open University.
A discussion between OpenAI Director Shivon Zilis and AI Fund Director of Ethics and Governance Tim Hwang, and both shared perspective on AI’s progress, its public perception, and how we can help ensure its responsible development going forward.
Hwang brought up the fact that artificial intelligence researchers are, in some ways, “basically writing policy in code” because of how influential the particular perspectives or biases inherent in these systems will be, and suggested that researchers could actually consciously set new cultural norms via their work.
Zilis added that the total number of people setting the tone for incredibly intelligent AI is probably “in the low thousands.”
She added that this means we likely need more crossover discussion between this community and those making policy decisions, and Hwang added that currently, there’s
“no good way for the public at large to signal” what moral choices should be made around the direction of AI development.
Zilis concluded that she has three guiding principles in terms of how she thinks about the future of responsible artificial intelligence development:
- First, the tech’s coming no matter what, so we need to figure out how to bend its arc with intent.
- Second, how do we get more people involved in the conversation?
- And finally, we need to do our best to front load the regulation and public discussion needed on the issue, since ultimately, it’s going to be a very powerful technology.