TY - GEN
T1 - Socially Responsible Machine Learning
T2 - 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023
AU - Moraffah, Raha
AU - Karimi, Amir Hossein
AU - Raglin, Adrienne
AU - Liu, Huan
N1 - Publisher Copyright:
© 2023 Owner/Author.
PY - 2023/8/6
Y1 - 2023/8/6
N2 - The evergrowing reliance of humans and society on machine learning methods has raised concerns about their trustworthiness and liability. As a response to these concerns, Socially Responsible Machine Learning (SRML) aims at developing fair, transparent, and robust machine learning algorithms. However, traditional approaches to SRML do not incorporate human perspectives, and therefore are not sufficient to build long-lasting trust between machines and human being. Causality as the key to human intelligence plays a vital role in achieving socially responsible machine learning algorithms which are compatible with human notions. Bridging the gap between traditional SRML and causality, in this tutorial, we aim at providing a holistic overview of SRML through the lens of causality. In particular, we will focus on state-of-the-art techniques on causal socially responsible ML in terms of fairness, interpretability, and robustness. The objectives of this tutorial are as follows: (1) we provide a taxonomy of existing literature on causal socially responsible ML from fairness, interpretability, and robustness perspective; (2) we review the state-of-the-art techniques for each task; and (3) we elucidate open questions and future research directions. We believe this tutorial is beneficial to researchers and practitioners from the areas of data mining, machine learning, and social sciences.
AB - The evergrowing reliance of humans and society on machine learning methods has raised concerns about their trustworthiness and liability. As a response to these concerns, Socially Responsible Machine Learning (SRML) aims at developing fair, transparent, and robust machine learning algorithms. However, traditional approaches to SRML do not incorporate human perspectives, and therefore are not sufficient to build long-lasting trust between machines and human being. Causality as the key to human intelligence plays a vital role in achieving socially responsible machine learning algorithms which are compatible with human notions. Bridging the gap between traditional SRML and causality, in this tutorial, we aim at providing a holistic overview of SRML through the lens of causality. In particular, we will focus on state-of-the-art techniques on causal socially responsible ML in terms of fairness, interpretability, and robustness. The objectives of this tutorial are as follows: (1) we provide a taxonomy of existing literature on causal socially responsible ML from fairness, interpretability, and robustness perspective; (2) we review the state-of-the-art techniques for each task; and (3) we elucidate open questions and future research directions. We believe this tutorial is beneficial to researchers and practitioners from the areas of data mining, machine learning, and social sciences.
KW - causality
KW - fairness
KW - interpretability
KW - responsible ml
KW - robustness
UR - http://www.scopus.com/inward/record.url?scp=85171322736&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85171322736&partnerID=8YFLogxK
U2 - 10.1145/3580305.3599571
DO - 10.1145/3580305.3599571
M3 - Conference contribution
AN - SCOPUS:85171322736
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 5819
EP - 5820
BT - KDD 2023 - Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
Y2 - 6 August 2023 through 10 August 2023
ER -