Equity and Fairness
Bibliographic References tagged with Equity and Fairness
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Karusala, N., Upadhyay, S., Veeraraghavan, R. & Gajos, K. Understanding Contestability on the Margins: Implications for the Design of Algorithmic Decision-making in Public Services. in CHI Conference on Human Factors in Computing Systems (CHI ’24) (2024).
Karusala, N., Upadhyay, S., Veeraraghavan, R. & Gajos, K. Understanding Contestability on the Margins: Implications for the Design of Algorithmic Decision-making in Public Services. in CHI Conference on Human Factors in Computing Systems (CHI ’24) (2024).
Ehrmann, D. E., Joshi, S., Goodfellow, S. D., Mazwi, M. L. & Eytan, D. Making machine learning matter to clinicians: model actionability in medical decision-making. NPJ Digital Medicine 6, 7 (2023).
Ehrmann, D. E., Joshi, S., Goodfellow, S. D., Mazwi, M. L. & Eytan, D. Making machine learning matter to clinicians: model actionability in medical decision-making. NPJ Digital Medicine 6, 7 (2023).
Gowda, S., Joshi, S., Zhang, H. & Ghassemi, M. Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasing.
Gowda, S., Joshi, S., Zhang, H. & Ghassemi, M. Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasing.
Singh, H., Joshi, S., Doshi-Velez, F. & Lakkaraju, H. Learning under adversarial and interventional shifts.
Singh, H., Joshi, S., Doshi-Velez, F. & Lakkaraju, H. Learning under adversarial and interventional shifts.
Upadhyay, S., Joshi, S. & Lakkaraju, H. Towards robust and reliable algorithmic recourse. Advances in Neural Information Processing Systems 34, 16926–16937 (2021).
Upadhyay, S., Joshi, S. & Lakkaraju, H. Towards robust and reliable algorithmic recourse. Advances in Neural Information Processing Systems 34, 16926–16937 (2021).
Parbhoo, S., Joshi, S. & Doshi-Velez, F. Learning-to-defer for sequential medical decision-making under uncertainty. Proceedings of the International Conference on Machine Learning: Workshop on Neglected Assumptions in Causal Inference (ICML) (2021).
Parbhoo, S., Joshi, S. & Doshi-Velez, F. Learning-to-defer for sequential medical decision-making under uncertainty. Proceedings of the International Conference on Machine Learning: Workshop on Neglected Assumptions in Causal Inference (ICML) (2021).
Pawelczyk, M., Agarwal, C., Joshi, S., Upadhyay, S. & Lakkaraju, H. Exploring Counterfactual Explanations through the lens of Adversarial Examples: A Theoretical and Empirical Analysis. International Conference on Artificial Intelligence and Statistics (AISTATS) (2022).
Pawelczyk, M., Agarwal, C., Joshi, S., Upadhyay, S. & Lakkaraju, H. Exploring Counterfactual Explanations through the lens of Adversarial Examples: A Theoretical and Empirical Analysis. International Conference on Artificial Intelligence and Statistics (AISTATS) (2022).
Parbhoo, S., Joshi, S. & Doshi-Velez, F. Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making.
Parbhoo, S., Joshi, S. & Doshi-Velez, F. Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making.
Zhang, H. et al. An Empirical Framework for Domain Generalization in Clinical Settings. Conference for Health, Inference, and Learning (CHIL) 2021 (2021).
Zhang, H. et al. An Empirical Framework for Domain Generalization in Clinical Settings. Conference for Health, Inference, and Learning (CHIL) 2021 (2021).
Killian, T., Ghassemi & Joshi. Counterfactually Guided Off-policy Transfer in Clinical Settings. in Conference for Health, Inference, and Learning (CHIL) 2022.
Killian, T., Ghassemi & Joshi. Counterfactually Guided Off-policy Transfer in Clinical Settings. in Conference for Health, Inference, and Learning (CHIL) 2022.