Research Interests

I study a variety of topics to do with social choice, machine learning, multiagent systems, game theory, and computational social science. Broadly speaking I focus on empirical and theoretical connections between novel social choice techniques and machine learning. My interests also include the various ethical and social issues arising from modern AI development and use.

Recently, I have been investigating potential uses for liquid democracy. This has included considering whether the delegative aspect of liquid democracy can be used to improve the performance of classifier ensembles in a machine learning context and how factors such as network connectivity and voter accuracy distribution affect the ability of liquid democracy to identify ground truth. I am also considering potential applications to machine learning for other understudied social choice mechanisms such as sortition.

Publications

My primary research is technical but, as evidenced by my publications, I am also very interested in the ethical and legal considerations of how AI interacts with society.

Alouf-Heffetz, S., Armstrong, B., Larson, K., Talmon, N. (2022) How Should We Vote? – A Comparison of Voting Systems within Social Networks. Proceedings of The 31st International Joint Conference on Artificial Intelligence (IJCAI).

Armstrong, B., Alouf-Heffetz, S., Talmon, N., Larson, K. (2022) Seeking Ground Truth: A Simulation-Based Exploration of Liquid Democracy and Sortition on Social Networks. 4th Games, Agents, and Incentives Workshop at 20th International Conference on Autonomous Agents and Multiagent Systems. (superseded by above IJCAI paper)

Armstrong, B., Larson, K. (2021) On the Limited Applicability of Liquid Democracy. 3rd Games, Agents, and Incentives Workshop at 20th International Conference on Autonomous Agents and Multiagent Systems.

Armstrong, B. (2021) Exploring the Relationship Between Social Choice and Machine Learning. Doctoral Consortium at the 20th International Conference on Autonomous Agents and Multiagent Systems.

Armstrong, B., Larson, K. (2019) Machine Learning to Strengthen Democracy. Joint Workshop on AI for Social Good at 33rd Conference on Neural Information Processing Systems.

Armstrong, B., Beretta, M., Crothers, E., Karlin, M., Kim, D., Longo, J., Powell L., and Sanders, T., (2019) Siri Humphrey: Design Principles for an AI Policy Analyst. 2019 Summer Institute on AI and Society.

Parson, E.T.A., Lempert, R., Armstrong, B., Crothers, E., DeChant, C. and Novelli, N., (2019). Could AI Drive Transformative Social Progress? What Would This Require?. 2019 Summer Institute on AI and Society.

Armstrong, B., Larson, K. (2017) Approval in the Echo Chamber. 4th Workshop on Exploring Beyond the Worst Case in Computational Social Choice at 16th International Conference on Autonomous Agents and Multiagent Systems.

Teaching Experience

I am fortunate to have had the opportunity to further explore some of my research interests through my positions as a Teaching Assistant both at the University of Waterloo, and at my undergraduate institution, the University of Northern British Columbia. As a TA I have generally helped to teach courses aimed at showing computer science students how to fully consider the ethical considerations of their work. Often these courses have had a specific focus on ethical and legal effects of AI systems.

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