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Regardless of whether you think Timnit Gebru is a jerk or a very fine person, bias in real-world AI is a big problem. To the extent algorithms are used to make

Regardless of whether you think Timnit Gebru is a jerk or a very fine person, bias in real-world AI is a big problem. To the extent algorithms are used to make significant, real-world decisions (whom to arrest, whom to extend credit, whom to grant parole, etc.) they had better be fair. And it seems that black-box algorithmic decisions are becoming more and more prevalent in the real world. It's been clearly demonstrated that biased training sets give biased results. It's also hard to come up with un-biased training sets. This is a problem that's worth working on.

mlthoughts2018 6y ago
The problem is that what you describe is a question of pragmatics and specific data curation, which is not sexy or marketable for political fairness and bias researchers. They need to manifest false angles of the problem, connections to climate change or #metoo or whatever is the social justice outrage du jour, so that it can win news cycle attention, TED talks and political notoriety. It leads to perverse incentives where AI fairness stops being an engineering problem that should be exclusively focused on objective criteria that need to be met to solve concrete problems, and starts being a tribalistic song and dance about wokeness, cancel culture and shoe-horning subjective values into learning systems at a strategic level. Unfortunately AI fairness is not a serious research domain yet. It’s a venue for trying to create political cottage industries around convenient social justice drama and then trying to parlay that into a career by building political moats such that if anyone questions the legitimacy of this or that aspect of “fairness research” then that skepticism alone can be used to cancel them and argue they are regressive.