In this virtual presentation, Dr. Abeba Birhane will address the ways that individuals and groups at the margins of society pay the highest price when AI systems fail, while the most privileged and powerful corporations benefit.

Complex adaptive systems (e.g., human behavior and social systems) are inherently dynamic, messy, ambiguous, incompressible, non-determinable, and non-predictable. Due to their incompressibility, neither datasets nor models can capture complex systems in their entirety. Instead, large scale datasets and predictive models pick up societal and historical stereotypes and injustices and are marked with various failures. Yet discussions of AI ethics tend to be abstract, far-fetched, sci-fi based, and devoid of current concrete realities.

In this talk, Dr. Birhane will: 1) emphasize the challenges of modeling complex behavior, 2) argue that equitable algorithmic systems need looking beyond technical solutions and require broader structural rethinking, and 3) highlight that visions of alternative realities need to be informed by and grounded in current realities.

Please note that this presentation engages with sensitive topics, such as visual examples of algorithmic racism.