Engineering Bias in AI Limiting representation in machine learning has the potential for harm on multiple levels

After working at Apple designing circuits and signal processing algorithms for products including the first iPad, Timnit Gebru (Figure 1) received her Ph. D. from the Stanford Artificial Intelligence Laboratory in the area of computer vision. She recently completed a postdoc with Microsoft Research in the FATE (Fairness, Transparency, Accountability, and Ethics in Artificial Intelligence (AI)) group, was a cofounder of Black in AI, and is currently working as a research scientist in the Ethical AI team at Google. Her research in algorithmic bias and the ethical implications of data mining have appeared in multiple publications, including The New York Times and The Economist. IEEE Pulse recently spoke with Gebru about the role societal bias plays in engineering AI, the deficits and dangers in the field caused by limited diversity, and the challenges inherent in addressing these complex issues.