Dr. Ayanna Howard

Professor, Georgia Institute of Technology

Tackling the Human Bias in AI

 People tend to overtrust sophisticated computing devices, including AI systems. As these systems become more fully interactive with humans during the performance of day-to-day activities, the role of bias in these human-AI interaction scenarios must be more carefully investigated.  Bias, as a feature of human life, has often been encoded in and can manifest itself through AI algorithms, which humans then take guidance from, resulting in the phenomenon of excessive trust. Bias further impacts this potential risk for trust, or overtrust, in that these systems are learning by mimicking our own thinking processes, inheriting our own implicit gender and racial biases, for example. Consequently, the propensity for trust and the potential of bias may have a direct impact on the overall quality of the interaction between humans and machines, whether the interaction is in the domains of healthcare, job-placement, or other high-impact life scenarios. In this talk, we will discuss this phenomenon of integrated trust and bias through the lens of AI systems that interact with people in scenarios that are realizable in the near-term.