Trustworthy Machine Learning

von Varshney, Kush R.
Veröffentlicht 2022

The Effect of Extremist Violence on Hateful Speech Online

von Olteanu, Alexandra
Veröffentlicht in International AAAI Conference on Web and Social Media (12. : 2018 : Stanford, Calif.) Proceedings of the Twelfth International AAAI Conference on Web and Social Media (2018)

Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting

von Sattigeri, Prasanna
Veröffentlicht in NeurIPS (36. : 2022 : New Orleans, La.; Online) 36th Conference on Neural Information Processing Systems (NeurIPS 2022 ; Volume 46 of 50 (2023)

On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach

von Wei, Dennis
Veröffentlicht in NeurIPS (36. : 2022 : New Orleans, La.; Online) 36th Conference on Neural Information Processing Systems (NeurIPS 2022 ; Volume 13 of 50 (2023)

Disparate Impact Diminishes Consumer Trust Even for Advantaged Users

von Draws, Tim
Veröffentlicht in PERSUASIVE (16. : 2021 : Online) Persuasive Technology (2021)

CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions

von Puri, Isha
Veröffentlicht in NeurIPS (35. : 2021 : Online) 35th Conference on Neural Information Processing Systems (NeurIPS 2021) ; Volume 26 of 36 (2022)

IS THERE A TRADE-OFF BETWEEN FAIRNESS AND ACCURACY? A PERSPECTIVE USING MISMATCHED HYPOTHESIS TESTING

von Dutta, Sanghamitra
Veröffentlicht in International Conference on Machine Learning (37. : 2020 : Online) 37th International Conference on Machine Learning (ICML 2020) ; Part 4 of 15 (2021)

Fair Transfer Learning with Missing Protected Attributes

von Coston, Amanda
Veröffentlicht in AAAI/ACM Conference on AI, Ethics, and Society (2. : 2019 : Honolulu, Hawaii) AIES'19 (2019)

Fairness of Classifiers Across Skin Tones in Dermatology

von Kinyanjui, Newton M.
Veröffentlicht in MICCAI (23. : 2020 : Online) Medical image computing and computer assisted intervention - MICCAI 2020 ; Part 6 (2020)

TED: Teaching AI to Explain its Decisions

von Hind, Michael
Veröffentlicht in AAAI/ACM Conference on AI, Ethics, and Society (2. : 2019 : Honolulu, Hawaii) AIES'19 (2019)