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If you need a refresher on the perils of ethical AI, Asimov's "Three laws of robotics" stories are as fresh as 60 years ago [1]. I'd like to remind that humans themselves are not that good at ethics, both in philosophy and in daily life; they are worse yet on agreeing what ethics to adhere to. You can imagine how well it could be automated then. [1]: https://en.wikipedia.org/wiki/Three_Laws_of_Robotics
Also see Ian Banks' "Culture" novels.
Earlier quoted context omitted.
What if it turns out there is reason people discriminate using those features (for example that people of race X tend to fail repaying their loans), and the classifier just learned that reason from the data. Why should we be expected to "fix" the reality for the classifier?
Because now people start to get rejected for loans because of their race, and that’s wrong for many obvious reasons.
Earlier quoted context omitted.
What if it turns out there is reason people discriminate using those features (for example that people of race X tend to fail repaying their loans), and the classifier just learned that reason from the data. Why should we be expected to "fix" the reality for the classifier?
Because now people start to get rejected for loans because of their race, and that’s wrong for many obvious reasons.
If you need a refresher on the perils of ethical AI, Asimov's "Three laws of robotics" stories are as fresh as 60 years ago [1]. I'd like to remind that humans themselves are not that good at ethics, both in philosophy and in daily life; they are worse yet on agreeing what ethics to adhere to. You can imagine how well it could be automated then. [1]: https://en.wikipedia.org/wiki/Three_Laws_of_Robotics
Edit: on the contrary
Earlier quoted context omitted.
There always seems to be a disconnect between tech philosophers and the technology they write about. Maybe they should try to actually work on 'AI' before preaching about it. To confirm your point a simple example is when working on an automated classifier for loan applications. One way to make the system more ethical, that is less descriminating based on certain features like race or gender, is to often lower the th…
A few points: 1. I mean I'm all in favor of ethics, but it's like saying "I believe in God/god". Everyone seems to have their own interpretation of it. When people are talking about ethics/God/etc., how can they be sure that they're talking about the same thing? Centuries of debating over what is "ethical" has got us no where. Kant got close, but then there are caveats to his definition of ethics, too. 2. Who says AI…
On what definition of "have to"?
Large numbers of people will not only refuse to accept the ASI's interpretation of ethics, they will also refuse to acknowledge that the purported ASI is any better than ourselves/themselves when it comes to ethics. If you try to settle the matter by giving the ASI the force of law, there will be armed revolts and violent crackdowns.
A univeraally useful question: What does it take to make it work? In this case the AI will need to convince all of us to follow the ethical code it designs. It can do this by suppressing information or by making morally defensible decisions. Having a good track-record of moral decisions key, so AIs need to be public persons and not subject to corporate secrecy.
I'd for sure love to be her friend.
Earlier quoted context omitted.
There always seems to be a disconnect between tech philosophers and the technology they write about. Maybe they should try to actually work on 'AI' before preaching about it. To confirm your point a simple example is when working on an automated classifier for loan applications. One way to make the system more ethical, that is less descriminating based on certain features like race or gender, is to often lower the th…
> One way to make the system more ethical, that is less descriminating based on certain features like race or gender, is to often lower the threshold of acceptance for the marginalized classes and raise it for the other ones. Or don't input those values at all. Is it even legal for a human to take those things into consideration? Regardless, the AI morally shouldn't, so there's no reason for the data to even be in th…
The Amazon recruiting AI failure is another great example of this, I think it didn’t take gender into account, but discriminated against activities like ‘head of women’s association’ etc.
Finally, what’s ethical is often up for discussion. Depending on context, people will optimize for equality (equal % of credits approved for both genders) vs helping those behind (Higher % approved for those historically disadvantaged) vs helping those that ‘deserve’ it of groups were unknown (finance such that default rates of both groups are expected to be equal, likely benefiting those with already good credit scores). I don’t think any of these answers is wrong, but they are mutually exclusive.
One challenge I don’t see discussed that often is that the current system of many humans evaluating loan applications leads to ‘noise‘ in outcomes so that we (on average) don’t see that much bias. (Eg a few people of certain ethnicity not getting loans because a few people are biased against them)
A single AI system scoring all applications however is consistent in its bias against a group, leading to more obvious effects (eg most people in a certain area not receiving loans because the algorithm is biased against it)
In my opinion, an important part is the right on human review of meaningful automated decisions. Not mentioned that often, but it’s also part of the GDPR.
Earlier quoted context omitted.
There always seems to be a disconnect between tech philosophers and the technology they write about. Maybe they should try to actually work on 'AI' before preaching about it. To confirm your point a simple example is when working on an automated classifier for loan applications. One way to make the system more ethical, that is less descriminating based on certain features like race or gender, is to often lower the th…
What if it turns out there is reason people discriminate using those features (for example that people of race X tend to fail repaying their loans), and the classifier just learned that reason from the data. Why should we be expected to "fix" the reality for the classifier?
The good point is reality always comme back. It takes decades for the USSR to crumble, but it finally collapsed. In the same way, organization that twisted reality will pay it back one day, for example banks that grant more loans to people that statistically default more will lose more money, thus creating opportunities for companies that has a reality-based strategy to outcompete them.
Another example: let’s imagine a TLA is building a system to mine potential mass shooters. The AI output is 99% male, so the ethics comity step in and imposes 50% of women profiles in output. The next shooting happens. It would have been predicted by the initial system, yet the modified one didn’t because the agency was too busy investing really low probability cases. The problem seems obvious in this case, but when a few selected group keywords are thrown in most people will put aside any logic and approve anything (either by conviction or for virtue signaling).
Earlier quoted context omitted.
Because now people start to get rejected for loans because of their race, and that’s wrong for many obvious reasons.
I agree with you that it is generally wrong, i.e. that other factors should come first. But what if the data suggests it? For example if is known that if you lend to someone from race X, there is 80% chance he would not pay back. Would you not incorporate that factor into your decision?
Earlier quoted context omitted.
There always seems to be a disconnect between tech philosophers and the technology they write about. Maybe they should try to actually work on 'AI' before preaching about it. To confirm your point a simple example is when working on an automated classifier for loan applications. One way to make the system more ethical, that is less descriminating based on certain features like race or gender, is to often lower the th…
What if it turns out there is reason people discriminate using those features (for example that people of race X tend to fail repaying their loans), and the classifier just learned that reason from the data. Why should we be expected to "fix" the reality for the classifier?