Hacking the Human Bias in Robotics

Abstract

Many of us, roboticists and those who collaborate with them, experience delight, excitement, and sometimes deep-seated, but rarely unvoiced, fears as we witness our robotic systems begin to impact human lives in countless ways. From automating driving to reshaping various facets of health care delivery, robotic systems are growing in their prevalence and intrusiveness into our daily lives. In combination with our siblings in the Artificial Intelligence (AI) community, scholars continue to predict a wide range of benefits from robotics and AI systems but also serious harms, including potential existential threats to humanity. Recognized pillars of science and engineering, including Elon Musk and the late great Stephen Hawking, have given voice to the apocalyptic kinds of fears that the public may have about an increasingly automated future. Whether these fears should be taken seriously is an issue that has divided scholars for awhile now, as illustrated by debates between Bill Joy [7] and Ray Kurzweil [8] at the beginning of the 21st century. On a different scale of granularity, a category of harms that users and others are more likely to experience on a day-to-day basis is from the various types of bias encoded in, or learned by, AI systems. This category of harms is especially troublesome in the world of physical robotics.