TY - GEN
T1 - Algorithmic Fairness in Distribution of Resources and Tasks
AU - Hosseini, Hadi
N1 - Publisher Copyright:
© 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.
PY - 2024
Y1 - 2024
N2 - The widespread adoption of Artificial Intelligence (AI) systems has profoundly reshaped decision-making in social, political, and commercial contexts. This paper explores the critical issue of fairness in AI-driven decision-making, particularly in allocating resources and tasks. By examining recent advancements and key questions in computational social choice, I highlight challenges and prospects in designing fair systems in collective decision-making that are scalable, adaptable to intricate environments, and are aligned with complex and diverse human preferences.
AB - The widespread adoption of Artificial Intelligence (AI) systems has profoundly reshaped decision-making in social, political, and commercial contexts. This paper explores the critical issue of fairness in AI-driven decision-making, particularly in allocating resources and tasks. By examining recent advancements and key questions in computational social choice, I highlight challenges and prospects in designing fair systems in collective decision-making that are scalable, adaptable to intricate environments, and are aligned with complex and diverse human preferences.
UR - http://www.scopus.com/inward/record.url?scp=85204301345&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85204301345&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85204301345
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 8541
EP - 8546
BT - Proceedings of the 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024
A2 - Larson, Kate
PB - International Joint Conferences on Artificial Intelligence
T2 - 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024
Y2 - 3 August 2024 through 9 August 2024
ER -