I2SC Lecture Series (Recording): Ruta Binkyte (AI Fairness & Privacy, CISPA Helmholtz Center for Information Security) From Humans to Machines and Back: Fairness, Causality, and the Role of Social Science
Date: January 9, 2026
Abstract:
Fairness in machine learning is not only a technical property of algorithms but a deeply social question: who benefits, who is harmed, and whose perspectives are embedded in our systems. While computational methods can surface biases and propose adjustments, addressing fairness requires more than optimization. It demands an understanding of social structures, power dynamics, and human behavior—areas where social science offers critical background knowledge, especially when applying causal approaches that aim to explain and intervene.
As we move into the era of large language models (LLMs) and increasingly agentic AI systems, these challenges grow more complex. Fairness concerns now span not just data and predictions, but dialogue, reasoning, and decision-making processes in systems that simulate human-like behavior. Traditional methods fall short, and novel approaches—bridging mechanistic insights from computer science with behavioral perspectives from the social sciences—are urgently needed.
At the same time, the relationship is not one-way. Studying LLMs and agentic AI can illuminate how human-like reasoning emerges (or fails to emerge) in artificial systems, offering new tools and sparking fresh questions for the social sciences. In this way, fairness research becomes a two-way street: moving from human to ML and back, with each side informing the other.
You can watch the recording of the talk below.
