This is another post about the limitations of AI, both in terms of their effectiveness and in terms of their explainability. In terms of effectiveness, they depend on the data they're given (which explains racist AIs) and on the uses to which they're put (which explains selective blindness in AIs). We are also told "We can't look inside the black box that makes the decisions." But we can know a lot about it - its data sources, its algorithms, its deployment. These are covered in Europe's new General Data Protection Regulation (GDPR). What about explainability? Because there are so many input variables, we cannot understand AI in terms of simple rules. But we can understand the range of possible outcomes, whichc allows us to create a portrait of how a given AI operates.
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