Why Designing AI Should Be More Like Building Hardware

Perhaps the trick is to start small. At the EmTech Digital conference on AI in San Francisco this week, Microsoft Research Postdoctoral Researcher Timnit Gebru suggested taking cues from the field of hardware design. To understand what problems this would solve, it helps to have a clear idea of exactly how bias in AI works. A typical AI is trained on data from the past, looks for patterns in that data, then makes predictions about the future. So if your dataset is racist or sexist–and much data is–the AI will generalize from that bias and exacerbate it.