I am a Computer Science PhD’s student at the University of Toronto studying Computational Social Science, my work is in applying machine learning to social science questions.

My current work is studying how to make deep reinforcement learning systems behave in a more human like way, studying how the language on social networks change over time and how programing languages affect their users thought and decisions.

Recent Publications:

Figure from the paper

Aligning Superhuman AI with Human Behavior: Chess as a Model System

Reid McIlroy-Young, Siddhartha Sen, Jon Kleinberg & Ashton Anderson

KDD 2020

Abstract pdf arΧiv code Lichess

As artificial intelligence becomes increasingly intelligent---in some cases, achieving superhuman performance---there is growing potential for humans to learn from and collaborate with algorithms. However, the ways in which AI systems approach problems are often different from the ways people do, and thus may be uninterpretable and hard to learn from. A crucial step in bridging this gap between human and artificial intelligence is modeling the granular actions that constitute human behavior, rather than simply matching aggregate human performance. We pursue this goal in a model system with a long history in artificial intelligence: chess. The aggregate performance of a chess player unfolds as they make decisions over the course of a game. The hundreds of millions of games played online by players at every skill level form a rich source of data in which these decisions, and their exact context, are recorded in minute detail. Applying existing chess engines to this data, including an open-source implementation of AlphaZero, we find that they do not predict human moves well. We develop and introduce Maia, a customized version of Alpha-Zero trained on human chess games, that predicts human moves at a much higher accuracy than existing engines, and can achieve maximum accuracy when predicting decisions made by players at a specific skill level in a tuneable way. For a dual task of predicting whether a human will make a large mistake on the next move, we develop a deep neural network that significantly outperforms competitive baselines. Taken together, our results suggest that there is substantial promise in designing artificial intelligence systems with human collaboration in mind by first accurately modeling granular human decision-making.

Figure from the paper

From “Welcome New Gabbers" to the Pittsburgh Synagogue Shooting: The Evolution of Gab

Reid McIlroy-Young & Ashton Anderson

ICWSM 2019 (poster paper)

Abstract pdf arΧiv dataset

Gab, an online social media platform with very little content moderation, has recently come to prominence as an alt-right community and a haven for hate speech. We document the evolution of Gab since its inception until a Gab user carried out the most deadly attack on the Jewish community in US history. We investigate Gab language use, study how topics evolved over time, and find that the shooters' posts were among the most consistently anti-Semitic on Gab, but that hundreds of other users were even more extreme.

The full list is in Publications and my CV.