Bell, Andrew

It’s Just Not That Simple: An Empirical Study of the Accuracy-Explainability Trade-off in Machine Learning for Public Policy

Bell, A., Solano-Kamaiko, I., Nov, O., & Stoyanovich, J. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 103

Think about the stakeholders first! Toward an algorithmic transparency playbook for regulatory compliance

Bell, A., Nov, O., & Stoyanovich, J. (2023) · Data & Policy

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 44

The Possibility of Fairness: Revisiting the Impossibility Theorem in Practice

Bell, A., Bynum, L., Drushchak, N., Zakharchenko, T., Rosenblatt, L., & Stoyanovich, J. (2023) · 2023 ACM Conference on Fairness, Accountability, and Transparency

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 21

Harnessing the power of advertising to prevent childhood obesity

Bell, A., Wolfenden, L., Sutherland, R., Coggan, L., Young, K., Fitzgerald, M., Hodder, R., Orr, N., Milat, A. J., & Wiggers, J. (2013) · International Journal of Behavioral Nutrition and Physical Activity

Obesity, Physical Activity, DietMedia Influence and Health

Cited by 14

Proactive advising: a machine learning driven approach to vaccine hesitancy

Bell, A., Rich, A., Teng, M., Orešković, T., Bras, N. B., Mestrinho, L., Golubovic, S., Pristas, I., & Zejnilovic, L. (2019) · 2019 IEEE International Conference on Healthcare Informatics (ICHI)

Vaccine Coverage and HesitancyArtificial Intelligence in Healthcare

Cited by 12