Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
R. Mothilal; A. Sharma; C. Tan
Research Track - FAT*2020, Barcelona, January 27th to 30th, 2020
https://dl.acm.org/doi/abs/10.1145/3351095.3372850
Видео Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations канала ACM FAccT Conference
R. Mothilal; A. Sharma; C. Tan
Research Track - FAT*2020, Barcelona, January 27th to 30th, 2020
https://dl.acm.org/doi/abs/10.1145/3351095.3372850
Видео Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations канала ACM FAccT Conference
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
Regulating Facial Processing Technologies: Tensions Between Legal and Technical Considerations ...
Garbage In, Garbage Out: Do Machine Learning Application Papers in Social Computing ...
FAT* 2018: Michael Ekstrand & Hoda Mehrpouyan - Privacy for All
FAccT'23 Keynote: "Thick Alignment"
Keynote: Health, Technology, and Race - Yeshimabeit Milner
The concept of fairness in the GDPR: a linguistic and contextual interpretation
How Platform-User Power Relations Shape Algorithmic Accountability: A Case Study of Instant Loan...
Fairness Violations and Mitigation under Covariate Shift
Multi-disciplinary fairness considerations in machine learning for clinical trials
Fairness, Equality, and Power in Algorithmic Decision-Making
Implications Tutorial: Responsible AI in Industry: Lessons Learned in Practice
The AI Ethics Money Problem: It's @ FAccT
How Can I Choose an Explainer? An Application-Grounded Evaluation of Post-hoc Explanations
Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness
Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data
Onward for the freedom of others: Marching beyond the AI Ethics
FlipTest: Fairness Testing via Optimal Transport
Can You Fake It Until You Make It?: Impacts of Differentially Private Synthetic Data on Downstream C
Regulating Transparency? Facebook, Twitter and the German Network Enforcement Act
Price Discrimination with Fairness Constraints
Algorithmic Recourse: from Counterfactual Explanations to Interventions