Trouble with #AI Bias – Kate Crawford

This article attempts to bring our readers to Kate’s brilliant Keynote speech at NIPS 2017. It talks about different forms of bias in Machine Learning systems and the ways to tackle such problems.

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?

Source: Datahub

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.



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The idea was to help you and I make better decisions amid cognitive overload

IBM Chairman, President, and Chief Executive Officer Ginni Rometty. PHOTOGRAPHER: STEPHANIE SINCLAIR FOR BLOOMBERG BUSINESSWEEK

If I considered the initials AI, I would have preferred augmented intelligence.

It’s the idea that each of us are going to need help on all important decisions.

A study said on average that a third of your decisions are really great decisions, a third are not optimal, and a third are just wrong. We’ve estimated the market is $2 billion for tools to make better decisions.

That’s what led us all to really calling it cognitive

“Look, we really think this is about man and machine, not man vs. machine. This is an era—really, an era that will play out for decades in front of us.”

We set out to build an AI platform for business.

AI would be vertical. You would train it to know medicine. You would train it to know underwriting of insurance. You would train it to know financial crimes. Train it to know oncology. Train it to know weather. And it isn’t just about billions of data points. In the regulatory world, there aren’t billions of data points. You need to train and interpret something with small amounts of data.

This is really another key point about professional AI. Doctors don’t want black-and-white answers, nor does any profession. If you’re a professional, my guess is when you interact with AI, you don’t want it to say, “Here is an answer.”

What a doctor wants is, “OK, give me the possible answers. Tell my why you believe it. Can I see the research, the evidence, the ‘percent confident’? What more would you like to know?”

It’s our responsibility if we build this stuff to guide it safely into the world.

Source: Bloomberg



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Microsoft’s new corporate vision: artificial intelligence is in and mobile is out

Microsoft has inserted artificial intelligence into its vision for the first time, and removed references to a “mobile-first” world. That fits with Microsoft’s recent push into AI and retreat from the smartphone market.

“We believe a new technology paradigm is emerging that manifests itself through an intelligent cloud and an intelligent edge where computing is more distributed, AI drives insights and acts on the user’s behalf, and user experiences span devices with a user’s available data and information,” according to Microsoft’s vision statement.

Microsoft last year formed a new 5,000-person engineering and research team to focus on its artificial intelligence products — a major reshaping of the company’s internal structure reminiscent of its massive pivot to pursue the opportunity of the Internet in the mid-1990s.

Here is Microsoft’s full vision statement from the document:

Microsoft is a technology company whose mission is to empower every person and every organization on the planet to achieve more. We strive to create local opportunity, growth, and impact in every country around the world. Our strategy is to build best-in-class platforms and productivity services for an intelligent cloud and an intelligent edge infused with artificial intelligence (“AI”).

Source: Geekwire



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Will there be any jobs left as #AI advances?

A new report from the International Bar Association suggests machines will most likely replace humans in high-routine occupations.

The authors have suggested that governments introduce human quotas in some sectors in order to protect jobs.

We thought it’d just be an insight into the world of automation and blue collar sector. This topic has picked up speed tremendously and you can see it everywhere and read it every day. It’s a hot topic now.” – Gerlind Wisskirchen, a lawyer who coordinated the study

For business futurist Morris Miselowski, job shortages will be a reality in the future.

I’m not absolutely convinced we will have enough work for everybody on this planet within 30 years anyway. I’m not convinced that work as we understand it, this nine-to-five, Monday to Friday, is sustainable for many of us for the next couple of decades.”

“Even though automation begun 30 years ago in the blue-collar sector, the new development of artificial intelligence and robotics affects not just blue collar, but the white-collar sector,” Ms Wisskirchen. “You can see that when you see jobs that will be replaced by algorithms or robots depending on the sector.”

The report has recommended some methods to mitigate human job losses, including a type of ‘human quota’ in any sector, introducing ‘made by humans’ label or a tax for the use of machines.

But for Professor Miselowski, setting up human and computer ratios in the workplace would be impractical.

We want to maintain human employment for as long as possible, but I don’t see it as practical or pragmatic in the long-term,” he said. “I prefer what I call a trans-humanist world, where what we do is we learn to work alongside machines the same way we have with computers and calculators.

It’s just something that is going to happen, or has already started to happen. And we need to make the best out of it, but we need to think ahead and be very thoughtful in how we shape society in the future — and that’s I think a challenge for everybody.” Ms Wisskirchen.

Source: ABC News

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We’re so unprepared for the robot apocalypse

Industrial robots alone have eliminated up to 670,000 American jobs between 1990 and 2007

It seems that after a factory sheds workers, that economic pain reverberates, triggering further unemployment at, say, the grocery store or the neighborhood car dealership.

In a way, this is surprising. Economists understand that automation has costs, but they have largely emphasized the benefits: Machines makes things cheaper, and they free up workers to do other jobs.

The latest study reveals that for manufacturing workers, the process of adjusting to technological change has been much slower and more painful than most experts thought. 

every industrial robot eliminated about three manufacturing positions, plus three more jobs from around town

“We were looking at a span of 20 years, so in that timeframe, you would expect that manufacturing workers would be able to find other employment,” Restrepo said. Instead, not only did the factory jobs vanish, but other local jobs disappeared too.

This evidence draws attention to the losers — the dislocated factory workers who just can’t bounce back

one robot in the workforce led to the loss of 6.2 jobs within a commuting zone where local people travel to work.

The robots also reduce wages, with one robot per thousand workers leading to a wage decline of between 0.25 % and 0.5 % Fortune

.None of these efforts, though, seem to be doing enough for communities that have lost their manufacturing bases, where people have reduced earnings for the rest of their lives.

Perhaps that much was obvious. After all, anecdotes about the Rust Belt abound. But the new findings bolster the conclusion that these economic dislocations are not brief setbacks, but can hurt areas for an entire generation.

How do we even know that automation is a big part of the story at all? A key bit of evidence is that, despite the massive layoffs, American manufacturers are making more stuff than ever. Factories have become vastly more productive.

some consultants believe that the number of industrial robots will quadruple in the next decade, which could mean millions more displaced manufacturing workers

The question, now, is what to do if the period of “maladjustment” that lasts decades, or possibly a lifetime, as the latest evidence suggests.

automation amplified opportunities for people with advanced skills and talents

Source: The Washington Post

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AI to become main way banks interact with customers within three years

Four in five bankers believe AI will “revolutionise” the way in which banks gather information as well as how they interact with their clients, said the Accenture Banking Technology Vision 2017 report

More than three quarters of respondents to the survey believed that AI would enable more simple user interfaces, which would help banks create a more human-like customer experience.

“(It) will give people the impression that the bank knows them a lot better, and in many ways it will take banking back to the feeling that people had when there were more human interactions.”

“The big paradox here is that people think technology will lead to banking becoming more and more automated and less and less personalized, but what we’ve seen coming through here is the view that technology will actually help banking become a lot more personalized,” said Alan McIntyre, head of the Accenture’s banking practice and co-author of the report.

The top reason for using AI for user interfaces, cited by 60 percent of the bankers surveyed, was “to gain data analysis and insights”.

Source: KFGO

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Will Using AI To Make Loans Trade One Kind Of Bias For Another?

Digital lending is expected to double in size over the next three years, reaching nearly 10 percent of all loans in the U.S. and Europe.

Marc Stein, who runs Underwrite.AI, writes algorithms capable of teaching themselves.

The program learns from each correlation it finds, whether it’s determining someone’s favorite books or if they are lying about their income on a loan application. And using that information, it can predict whether the applicant is a good risk.

Digital lenders are pulling in all kinds of data, including purchases, SAT scores and public records like fishing licenses.

If we looked at the delta between what people said they made and what we could verify, that was highly predictive,” Stein says.

As part of the loan application process, some lenders have prospective borrowers download an app that uploads an extraordinary amount of information like daily location patterns, the punctuation of text messages or how many of their contacts have last names

“FICO and income, which are sort of the sweet spot of what every consumer lender in the United States uses, actually themselves are quite biased against people,” says Dave Girouard, the CEO of Upstart, an online lender.

Government research has found that FICO scores hurt younger borrowers and those from foreign counties because people with low incomes are targeted for higher-interest loans. Girouard argues that new, smarter data can make lending more fair.

Source: NPR

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JPMorgan software does in seconds what took lawyers 360,000 hours

At JPMorgan, a learning machine is parsing financial deals that once kept legal teams busy for thousands of hours.

The program, called COIN, for Contract Intelligence, does the mind-numbing job of interpreting commercial-loan agreements that, until the project went online in June, consumed 360,000 hours of lawyers’ time annually. The software reviews documents in seconds, is less error-prone and never asks for vacation.

COIN is just the start for the biggest U.S. bank. The firm recently set up technology hubs for teams specialising in big data, robotics and cloud infrastructure to find new sources of revenue, while reducing expenses and risks.

The push to automate mundane tasks and create new tools for bankers and clients — a growing part of the firm’s $9.6 billion technology budget.

Behind the strategy, overseen by Chief Operating Officer Matt Zames and Chief Information Officer Dana Deasy, is an undercurrent of anxiety:

though JPMorgan emerged from the financial crisis as one of few big winners, its dominance is at risk unless it aggressively pursues new technologies, according to interviews with a half-dozen bank executives.

Source: Independent

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Civil Rights and Big Data

big-data-whitehouse-reportBlogger’s note: We’ve posted several articles on the bias and prejudice inherent in big data, which with machine learning results in “machine prejudice,” all of which impacts humans when they interact with intelligent agents. 

Apparently, as far back as May 2014, the Executive Office of the President started issuing reports on the potential in “Algorithmic Systems” for “encoding discrimination in automated decisions”. The most recent report of May 2016 addressed two additional challenges:

1) Challenges relating to data used as inputs to an algorithm;

2) Challenges related to the inner workings of the algorithm itself.

Here are two excerpts:

The Obama Administration’s Big Data Working Group released reports on May 1, 2014 and February 5, 2015. These reports surveyed the use of data in the public and private sectors and analyzed opportunities for technological innovation as well as privacy challenges. One important social justice concern the 2014 report highlighted was “the potential of encoding discrimination in automated decisions”—that is, that discrimination may “be the inadvertent outcome of the way big data technologies are structured and used.”

To avoid exacerbating biases by encoding them into technological systems, we need to develop a principle of “equal opportunity by design”—designing data systems that promote fairness and safeguard against discrimination from the first step of the engineering process and continuing throughout their lifespan.

Download the report here: Whitehouse.gov

References:

https://www.whitehouse.gov/blog/2016/10/12/administrations-report-future-artificial-intelligence

http://www.frontiersconference.org/

 

 

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Understand The Spectrum Of Seven Artificial Intelligence Outcomes

spectrum-of-outcomes-for-ai-768x427

Successful AI projects seek a spectrum of outcomes, says R “Ray” Wang. 

AI driven smart services will power the future business models. As with most disruptive business models, form must follow function. Just enabling AI for AI’s sake will result in a waste of time. However, applying a spectrum of outcomes to transform the business models of AI powered organizations will indeed result in a disruptive business model and successful digital transformation.

SPECTRUM OF SEVEN OUTCOMES FOR AI
  1. Perception describes what’s happening now.
  2. Notification tells you what you asked to know.
  3. Suggestion recommends action.
  4. Automation repeats what you always want.
  5. Prediction informs you what to expect.
  6. Prevention helps you avoid bad outcomes.
  7. Situational awareness tells you what you need to know right now.

Source: Software Insider

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Why Microsoft bought LinkedIn, in one word: Cortana

Know everything about your business contact before you even walk into the room.

JMicrosoft Linkedin 1eff Weiner, the chief executive of LinkedIn, said that his company envisions a so-called “Economic Graph,” a digital representation of every employee and their resume, a digital record of every job that’s available, as well as every job and even every digital skill necessary to win those jobs.

LinkedIn also owns Lynda.com, a training network where you can take classes to learn those skills. And, of course, there’s the LinkedIn news feed, where you can keep tabs on your coworkers from a social perspective, as well.

Buying LinkedIn brings those two graphs together and gives Microsoft more data to feed into its machine learning and business intelligence processes. “If you connect these two graphs, this is where the magic happens, where digital work is concerned,” Microsoft chief executive Satya Nadella said during a conference call.

Microsoft Linkedin 2

Source: PC World

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4th revolution challenges our ideas of being human

4th industrial revolution

Professor Klaus Schwab, Founder and Executive Chairman of the World Economic Forum is convinced that we are at the beginning of a revolution that is fundamentally changing the way we live, work and relate to one another

Some call it the fourth industrial revolution, or industry 4.0, but whatever you call it, it represents the combination of cyber-physical systems, the Internet of Things, and the Internet of Systems.

Professor Klaus Schwab, Founder and Executive Chairman of the World Economic Forum, has published a book entitled The Fourth Industrial Revolution in which he describes how this fourth revolution is fundamentally different from the previous three, which were characterized mainly by advances in technology.

In this fourth revolution, we are facing a range of new technologies that combine the physical, digital and biological worlds. These new technologies will impact all disciplines, economies and industries, and even challenge our ideas about what it means to be human.

It seems a safe bet to say, then, that our current political, business, and social structures may not be ready or capable of absorbing all the changes a fourth industrial revolution would bring, and that major changes to the very structure of our society may be inevitable.

Schwab said, “The changes are so profound that, from the perspective of human history, there has never been a time of greater promise or potential peril. My concern, however, is that decision makers are too often caught in traditional, linear (and non-disruptive) thinking or too absorbed by immediate concerns to think strategically about the forces of disruption and innovation shaping our future.”

Schwab calls for leaders and citizens to “together shape a future that works for all by putting people first, empowering them and constantly reminding ourselves that all of these new technologies are first and foremost tools made by people for people.”

Source: Forbes, World Economic Forum

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Obama – robots taking over jobs that pay less than $20 an hour

obama on jobsBuried deep in President Obama’s February economic report to Congress was a rather grave section on the future of robotics in the workforce.

After much back and forth on the ways robots have eliminated or displaced workers in the past, the report introduced a critical study conducted this year by the White House’s Council of Economic Advisers (CEA).

The study examined the chances automation could threaten people’s jobs based on how much money they make: either less than $20 an hour, between $20 and $40 an hour, or more than $40.

The results showed a 0.83 median probability of automation replacing the lowest-paid workers — those manning the deep fryers, call centers, and supermarket cash registers — while the other two wage classes had 0.31 and 0.04 chances of getting automated, respectively.

In other words, 62% of American jobs may be at risk.

white house AI job lossSource: TechInsider

ericschmidt2Meanwhile – from Alphabet (Google) chairman Eric Schmidt

There’s no question that as [AI] becomes more pervasive, people doing routine, repetitive tasks will be at risk,” Schmidt says.

I understand the economic arguments, but this technology benefits everyone on the planet, from the rich to the poor, the educated to uneducated, high IQ to low IQ, every conceivable human being. It genuinely makes us all smarter, so this is a natural next step.”

Source: Financial Review

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Artificial Intelligence: Toward a technology-powered, human-led AI revolution

AI gartner2Research conducted among 9,000 young people between the ages of 16 and 25 in nine industrialised and developing markets – Australia, Brazil, China, France, Germany, Great Britain, India, South Africa and the United States – showed that a striking 40 per cent think that a machine – some kind of artificial intelligence – will be able to fully do their job in the next decade.

Young people today are keenly aware that the impact of technology will be central to the way their careers and lives will progress and differ from those of previous generations.

In its “Top strategic predictions for 2016 and beyond,” Gartner expects that by 2018, 20 per cent of all business content will be authored by machines and 50 per cent of the fastest-growing companies will have fewer employees than instances of smart machines. This is AI in action. Automated systems can have measurable, positive impacts on both our environment and our social responsibilities, giving us the room to explore, research and create new techniques to further enrich our lives. It is a radical revolution in our time.

The message from the next generation seems to be “take us on the journey.” But it is one which technology leaders need to lead. That means ensuring that as we use technology to remove the mundane, we also use it to amplify the creativity and inquisitive nature only humans are capable of. We need the journey of AI to be a human- led journey.

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Google’s new AI will reply to your emails so you don’t have to

People who have the Inbox email program on their iPhones or Android devices will soon have a new option when it comes to replying to emails. Instead of coming up with their own responses on their mobile devices, they’ll get to choose between three options created by a neural network built by Google researchers.

Google claims it has built an AI that can read incoming emails, understand them, and generate a short, appropriate response that the recipient can then edit or send with just a click.

google email ai2

In the case of Smart Reply, what Google has done is combine several systems to build a neural network that can read your email, parse what the words in the email mean, and then not only generate a response, but generate three different responses. This is more than just building out rules for common words that fall in an email. This is truly teaching a computer to understand the text of an email. It uses the type of neural networks found in natural language processing to understand what a person means and also generate a reply.

Another bizarre feature of our early prototype was its propensity to respond with “I love you” to seemingly anything. As adorable as this sounds, it wasn’t really what we were hoping for. Some analysis revealed that the system was doing exactly what we’d trained it to do, generate likely responses—and it turns out that responses like “Thanks”, “Sounds good”, and “I love you” are super common—so the system would lean on them as a safe bet if it was unsure. Normalizing the likelihood of a candidate reply by some measure of that response’s prior probability forced the model to predict responses that were not just highly likely, but also had high affinity to the original message. This made for a less lovey, but far more useful, email assistant.

Source: Fortune

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Toyota Invests $1 Billion in Artificial Intelligence Research Center in California

Breaking News, Nov. 6:

Gill Pratt, a roboticist who will oversee Toyota's new research laboratory in the United States, at a news conference Friday in Tokyo. (Yuya Shino/Reuters)

Gill Pratt, a roboticist who will oversee Toyota’s new research laboratory in the United States, at a news conference Friday in Tokyo. (Yuya Shino/Reuters)

Toyota, the Japanese auto giant, on Friday announced a five-year, $1 billion research and development effort headquartered here. As planned, the compound would be one of the largest research laboratories in Silicon Valley.

Conceived as a research facility bridging basic science and commercial engineering, it will be organized as a new company to be named Toyota Research Institute. Toyota will initially have a laboratory adjacent to Stanford University and another near M.I.T. in Cambridge, Mass.

Toyota plans to hire 200 scientists for its artificial intelligence research center.

The new center will initially focus on artificial intelligence and robotics technologies and will explore how humans move both outdoors and indoors, including technologies intended to help the elderly.

When the center begins operating in January, it will prioritize technologies that make driving safer for humans rather than completely replacing them. That approach is in stark contrast with existing research efforts being pursued by Google and Uber to create self-driving cars.

“We want to create cars that are both safer and incredibly fun to drive,” Dr. Pratt said. Rather than completely removing driving from the equation, he described a collection of sensors and software that will serve as a “guardian angel,” protecting human drivers.

In September, when Dr. Pratt joined Toyota, the company announced an initial artificial intelligence research effort committing $50 million in funding to the computer science departments of both Stanford and M.I.T. He said the initiative was intended to turn one of the world’s most successful carmakers into one of the world’s top software developers.

In addition to focusing on navigation technologies, the new research corporation will also apply artificial intelligence technologies to Toyota’s factory automation systems, Dr. Pratt said.

Source: NY Times

 

 

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AI predicted to impact the U.S. economy by “trillions” by 2025

Accenture Expands Global Artificial Intelligence Capabilities and R&D Agenda

“Artificial intelligence will disrupt businesses and industries on a global scale, and we see this shift going well beyond deploying analytics, cognitive computing or machine learning systems in isolation,” said Paul Daugherty, Accenture’s chief technology officer. “We are investing early to drive more innovation at Accenture, recruit top talent in every location we operate in, and infuse more intelligence across our global business to help clients accelerate the integration of intelligence and automation to transform their businesses.”

Accenture has also established the Accenture Technology Labs University Grant on Artificial Intelligence; awarding the inaugural grant to an academic research team at the Insight Centre for Data Analytics at University College Dublin. The research team will explore the interface between humans and machines, using cognitive analysis to better understand how both can collaborate and interact effectively.

Analyst firm IDC predicts that the worldwide content analytics, discovery and cognitive systems software market will grow from US$4.5 billion in 2014 to US$9.2 billion in 20191, with others citing these systems as catalyst to have a US$5 trillion – US$7 trillion potential economic impact by 2025.

Source: Businesswire

 

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The danger of tech’s far reaching tentacles

Jobs one last thing

Steve Jobs during one of his presentations of new Apple products. Photograph: Christoph Dernbach/Corbis

Excerpt from Tim Adams interview with Danny Boyle, director of Steve Jobs:

Tim Adams: We have all been complicit, I suggest, in the rise of Apple to be world’s most valuable company, in the journey that Jobs engineered from rebellion to ubiquity and all that it entails. Did Boyle want the film to comment on that complicity?

Danny Boyle: I think so. Ultimately it is about his character, and a father and a daughter. But you do want it to try and be part of the big story of our relationship with these giant corporations. All the companies that were easy to criticise, banks, oil companies, pharmaceutical companies, they have been replaced by tech guys. And yet the atmosphere around them remains fairly benign. Governments are not powerful enough any more to resist them and the law is not quick enough. One of the reasons I wanted to do this [direct the movie Steve Jobs] is that sense that we have to constantly bring these people to account. I mean, they have emasculated journalism for one thing. They have robbed it of its income. If you want to look at that malignly you certainly could do: they have made it so nobody can afford to write stories about them. Their tentacles are so far reaching in the way the world is structured that there is a danger they become author and critic at the same time. Exactly what Jobs used to accuse IBM of.”

Source: The Gaurdian

 

 

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