Artificial Intelligence, Machine Learning, Algorithms -- they're everywhere. Not a day goes by when there aren't emails, podcasts, newsfeeds explaining how the algorithms will change the world. But where are the humans in these equations? How will ML and AI interact with complex human systems? I don't know -- but Abeba Birhane devotes her life to just those questions. The Lounge is delighted to welcome her for a talk about her latest research on complexity and machine learning.

On the one hand, theories of critical complexity view human behavior and social systems as ambiguous, incompressible, and non-determinable and thus, impossible to capture in a single model. On the other hand, machine learning models that claim to capture and predict human behavior are currently in vogue. In this talk, Abeba explores this tension.

Abeba's Bio:
Abeba Birhane is a cognitive science PhD researcher at the Complex Software Lab in the school of computer science at University College Dublin, Ireland. Her interdisciplinary research sits at the intersection of complex adaptive systems, machine learning, and critical race studies. On the one hand, complexity science tells us that people, as complex adaptive systems, are inherently indeterminable. On the other, machine learning systems that claim to predict human behavior are becoming ubiquitous in all spheres of social life. Machine prediction, when deployed to high-stake situations, not only is erroneous but also presents real harm to those at the margins of society. She examines questions of such nature in her Ph.D.

She co-leads the Data Economies and Data Governance working group, one of the Mechanism Design for Social Good (MD4SG) working groups and is also a member of the Coalition For Critical Technology group. Abeba is currently a Research Scientist intern at DeepMind with the Ethics Research team. She has numerous ongoing projects and looks forward to sharing them as they come to completion.