We need AI that doesn't simply accept reality "as is", but can also help us fix it. Otherwise AI will be little more than hyper-optimized reflections of our own failures and prejudices. We've had plenty of that already, we don't really need more of it.
"The reasonable man adapts himself to the world; the unreasonable one persists in trying to adapt the world to himself. Therefore all progress depends on the unreasonable man." - George Bernard Shaw
Epidemiological studies tell us some diseases disproportionally affect certain groups. Should doctors dismiss this knowledge as racist and refrain from screening patients in the affected groups because they might come off as racist?
Therefore we know that correlations with any definition of ethnicity or race are spurious, because those definitions must be socially constructed, because the gene pool simply does not have the shape that race realists claim that it does.
Think in terms of contraposition. Sure, if race were real, then maybe it might make sense to talk about racial demographics. However, since race clearly is not real, any demographic correlations must be bogus. There is a much simpler explanation for why some skin colors seem socioeconomically advantaged: Because our society itself has bigoted opinions about skin colors, and has practices like redlining [4] which systematically oppress folks.
[0] https://en.wikipedia.org/wiki/Hereditary_haemochromatosis
[1] https://en.wikipedia.org/wiki/Sickle_cell_disease
[2] https://en.wikipedia.org/wiki/Tay%E2%80%93Sachs_disease
I dont't think that's how we make our day to day decisions. I usually treat people as individuals because I expect to be treated the same, but it doesn't make the "tribe" features meaningless. For example I would not live in given neighborhood if its tribe is knwon for [something bad] (this also applies to my own tribe). If I have an object of value, the tribe of a person would certainly be a factor to decide whether to entrust him with it or not. I'm sure a lot of people would do the same. It's just how probabilities are tranlated by the brain.
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).
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 should behave according to "our" ethics (whatever those are)? Sooner or later, with the emergence of ASI (artificial super intelligence) we'll have to accept the superior form of consciousness the ASI is. Expecting it to behave according to our rules is like monkeys asking us not to take away all the bananas because for them, it's unethical.
3. Even without AI, people have all sorts of biases and prejudices against certain ethnicities/races/colors/etc. You're not happy with the results that AI provides? Fine, hire some people instead and see where that leads you. The only thing you lose is the higher performance of AI.
4. Expecting AI systems to have no bias (while employees replaced by the AI certainly were prone to bias) is equivalent to expecting technology to change our world for the better. That doesn't always happen. You think smartphones are necessarily a good thing? Think about where they produce those gadgets and how they treat the workers.
5. At the end of the day, companies will stick with whatever "works" for them, regardless of what label we put on it (good/bad/ethical/fair/...). If it adds value, then most of the time it's enough. If being ethical (according to some definition) doesn't bring in more cash, companies are unlikely to invest in it. At least, with explainable AI, there is benefits for the firm to improve the accuracy of the AI in the long term. But pushing companies to adopt some ethical/unbiased/fair AI that doesn't add value for them is itself unethical (in my book).
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.
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 the input.
Unfortunately, it does this by codifying pre-existant social outcomes which are often not free of bias.
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.
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 threshold of acceptance for the marginalized classes and raise it for the other ones. Anyone who had to do that sees how it poses a dilemma of equality vs fairness or even fairness vs cost. So indeed, ethics cannot be solved.