CraftHub Events
2 HN recommenders · recommended in 6 months, 2019–2021
43:23 · 1.6K views · Nov 20, 2018
Chris Stucchio presents the ethical principles behind algorithmic decision making and shows with examples from lending, criminology and education that they cannot all be satisfied at once. The talk frames AI fairness as a set of quantitative tradeoffs.
HN cites it in algorithmic-bias threads for the impossibility result that an algorithm cannot be procedurally fair, group fair and maximally useful at once.
6 comments from Hacker News
In many unbalanced situations you simply can't have an unbiased decision that's fair across all measures, no matter if the decision is done by a model, a human or a deity; you have to trade off between different types of unfairness. Equality of opportunity for individuals will result in unequal results for groups; equality of outcomes requires unequal opportunities if the historical circumstances have resulted in socioeconomic unequalities. In your financial evaluations example, many biases and disadvantages would remain even if you solely reduce the decision to relevant financial facts for a specific individual, because a poorer individual with lower socioeconomic status and less opportunities actually has a higher risk of non-payment, and a disadvantaged group will have disproportionally more such individuals. Should we accept that? Should we require the other groups to subsidize their non-payment? Both options are unfair in some aspect and fair in another, you can't have your cake and eat it too, and it's not the fault of the model you use - the only difference with a human is that they can better hide the factors they use, lie about the influences (perhaps also lie to themselves) and rationalize/invent factors to justify their decision. For this topic, perhaps this talk "AI Ethics, Impossibility Theorems and Tradeoffs" this video or its slides [LINK] might be interesting for you, it has some flaws but is a decent exploration of the problem space.
I was interested in the slide deck and wanted to hear the author expand on some of his thoughts, found a video of his presentation if anyone else is interested. this video
> I think it's also important to remember a lot of human models were abandoned because of the inherent bias The industry needs to stop misusing the term bias this way. Virtually every attempt to find this supposed human bias has failed. Latest public example was Amazon and hiring[1] Bias is the tendency to consistently mis-classify towards a certain class or tendency to consistently to over or under-estimate. Somehow the term has been hijacked to mean 'discriminate on factors that are politically incorrect'. You can have a super racist model that's bias free, and most models blinded to protected factors are in fact statistically biased. It's not constructive to conflate actual bias with political incorrectness. Operational decision making, whether AI or human or statistical, faces an inherent trilemma: it's impossible to simultaneously treat everyone the same way, to have a useful model, and to have uniformly distributed 'bias'-free outcomes. At best a model can strive to achieve two of these factors. See: this video [1] [LINK]
This 'algorithm has bias' kind of headline needs to die. Every system has '''bias''' in the colloquial sense so long as the inputs to that system are not uniformly distributed. The only thing the designer of the system can do is make a conscious choice about which way to '''bias''' the system and be able to justify that choice. In practical terms it means that systems that deal with diverse populations are always going to be 'unfair' towards some group of people. The only control we have is who that group of people will be. This is inherent and unavoidable. We need to accept this fact and keep it in consideration when designing such systems (or choosing not to design them in the first place). {procedural fairness, group fairness, utility}. Pick two. this video
See also Stucchio's impossibility theorem: it is impossible for a given algorithm to be procedurally fair, representationally fair, and utilitarian at the same time: Video: this video Slides: [LINK]