Optimizing fairness and accuracy: a Pareto optimal approach for decision-making
In the era of data-driven decision-making, ensuring fairness and equality in machine learning models has become increasingly crucial. Multiple fairness definitions have been brought forward to evaluate and mitigate unintended fairness-related harms in real-world applications, with little research on...
Main Authors: | Nagpal, Rashmi, Shahsavarifar, Rasoul, Goyal, Vaibhav, Gupta, Amar |
---|---|
Other Authors: | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
Format: | Article |
Language: | English |
Published: |
Springer Science and Business Media LLC
2024
|
Online Access: | https://hdl.handle.net/1721.1/155737 |
Similar Items
-
A Multi-Objective Framework for Balancing Fairness and Accuracy in Debiasing Machine Learning Models
by: Nagpal, Rashmi, et al.
Published: (2024) -
A Multi-Objective Framework for Balancing Fairness and Accuracy in Debiasing Machine Learning Models
by: Rashmi Nagpal, et al.
Published: (2024-09-01) -
Elite Multi-Criteria Decision Making—Pareto Front Optimization in Multi-Objective Optimization
by: Adarsh Kesireddy, et al.
Published: (2024-05-01) -
Uncertainty and pareto optimality /
by: 225298 Tisdell, C. -
Performance Assessment of Pareto and Non-Pareto Approaches for the Optimal Allocation of DG and DSTATCOM in the Distribution System
by: Khalid Ibrahim*, et al.
Published: (2020-01-01)