Moral Machine” reveals deep split in autonomous car ethics

In the Moral Machine game, users were required to decide whether an autonomous car careened into unexpected pedestrians or animals, or swerved away from them, killing or injuring the passengers.

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.

Source: Cosmos

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Can machines learn to be moral?  #AI

AI works, in part, because complex algorithms adeptly identify, remember, and relate data … Moreover, some machines can do what had been the exclusive domain of humans and other intelligent life: Learn on their own.

As a researcher schooled in scientific method and an ethicist immersed in moral decision-making, I know it’s challenging for humans to navigate concurrently the two disparate arenas. 

It’s even harder to envision how computer algorithms can enable machines to act morally.

Moral choice, however, doesn’t ask whether an action will produce an effective outcome; it asks if it is good decision. In other words, regardless of efficacy, is it the right thing to do? 

Such analysis does not reflect an objective, data-driven decision but a subjective, judgment-based one.

Individuals often make moral decisions on the basis of principles like decency, fairness, honesty, and respect. To some extent, people learn those principles through formal study and reflection; however, the primary teacher is life experience, which includes personal practice and observation of others.

Placing manipulative ads before a marginally-qualified and emotionally vulnerable target market may be very effective for the mortgage company, but many people would challenge the promotion’s ethicality.

Humans can make that moral judgment, but how does a data-driven computer draw the same conclusion? Therein lies what should be a chief concern about AI.

Can computers be manufactured with a sense of decency?

Can coding incorporate fairness? Can algorithms learn respect? 

It seems incredible for machines to emulate subjective, moral judgment, but if that potential exists, at least four critical issues must be resolved:

  1. Whose moral standards should be used?
  2. Can machines converse about moral issues?
  3. Can algorithms take context into account?
  4. Who should be accountable?

Source: Business Insider David Hagenbuch



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