Standard Approaches To Data And AI Ethical Risk Mitigation
- The academic approach: this means spotting ethical problems, their sources, and how to think through them. But it unfortunately tends to ask different questions than businesses. The result is academic treatments that do not speak to the highly particular, concrete uses of data and AI.
- The “on-the-ground” approach: it knows to ask the business-relevant risk-related questions precisely because they are the ones making the products, but it lacks the skill, knowledge, and experience to answer ethical questions systematically, exhaustively, efficiently and institutional support.
- The high-level AI ethics principles: Google and Microsoft, for instance, trumpeted their principles years ago. The difficulty comes in operationalizing those principles. What, exactly, does it mean to be for “fairness?” Which metric is the right one in any given case, and who makes that judgment?
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