When AI Goes Wrong

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Garbage in, serious garbage out. AIs have to be more than just accurate; they have to be fair.

eetasia.com, May. 14, 2019 – 

Artificial Intelligence or AI has been touted as the Holy Grail of what seems like innumerable applications for automating decision-making. Some of the more typical things AI can do better or faster than people include making movie recommendations on Netflix, detecting cancer, tuning e-commerce and retail websites for each visitor, and customizing in-car infotainment systems.

A few of the more unusual things these automated systems are doing include making better beer, converting thoughts into speech, and creating faster-than-you'd-ever-think-possible death metal.

Automated systems have also had some spectacular failures. The self-driving car, proposed as a shining example of what AI can do, failed when a self-driving Uber SUV killed a pedestrian last year. More and more AI systems are being used to make decisions about people and where they can live; what jobs they can have; whether they can be insured and how much their rates will be; what kind of mortgage loan they can get; and whether the Department of Homeland Security thinks, based on their face, that they might be a terrorist.

Most of the fairness (or not) of an AI's decision-making depends on the accuracy and completeness of the test data sets used by the AI's training algorithm. It also depends on the accuracy of the algorithm itself and how decisions are made about "success." The training algorithm's optimization strategy can actually amplify bias if it's optimizing for the maximum overall accuracy of an entire population.

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