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Trustworthy machine learning and artificial intelligence

Published: 10 April 2019 Publication History

Abstract

How can we add the most important ingredient to our relationship with machine learning?

References

[1]
IBM. Adversarial Robustness Toolbox. {November 14, 2018}; https://developer.ibm.com/code/open/projects/adversarial-robustness-toolbox
[2]
IBM Research. AI Fairness 360 Open Source Toolkit. 2018; http://aif360.mybluemix.net

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  • (2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/3652891Online publication date: 13-Apr-2024
  • (2024)An Empirical Investigation into Benchmarking Model Multiplicity for Trustworthy Machine Learning: A Case Study on Image Classification2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV57701.2024.00443(4476-4485)Online publication date: 3-Jan-2024
  • (2024)Navigating Data-Centric Artificial Intelligence With DC-Check: Advances, Challenges, and OpportunitiesIEEE Transactions on Artificial Intelligence10.1109/TAI.2023.33458055:6(2589-2603)Online publication date: Jun-2024
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      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 10 April 2019
      Published in XRDS Volume 25, Issue 3

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      Cited By

      View all
      • (2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/3652891Online publication date: 13-Apr-2024
      • (2024)An Empirical Investigation into Benchmarking Model Multiplicity for Trustworthy Machine Learning: A Case Study on Image Classification2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV57701.2024.00443(4476-4485)Online publication date: 3-Jan-2024
      • (2024)Navigating Data-Centric Artificial Intelligence With DC-Check: Advances, Challenges, and OpportunitiesIEEE Transactions on Artificial Intelligence10.1109/TAI.2023.33458055:6(2589-2603)Online publication date: Jun-2024
      • (2024)The AI Future of Emergency MedicineAnnals of Emergency Medicine10.1016/j.annemergmed.2024.01.03184:2(139-153)Online publication date: Aug-2024
      • (2023)Leveraging explanations in interactive machine learning: An overviewFrontiers in Artificial Intelligence10.3389/frai.2023.10660496Online publication date: 23-Feb-2023
      • (2023)Socially Responsible Artificial Intelligence Empowered People Analytics: A Novel Framework Towards SustainabilityHuman Resource Development Review10.1177/1534484323120093023:1(88-120)Online publication date: 11-Sep-2023
      • (2023)Designing Fiduciary Artificial IntelligenceProceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization10.1145/3617694.3623230(1-15)Online publication date: 30-Oct-2023
      • (2023)Can Querying for Bias Leak Protected Attributes? Achieving Privacy With Smooth SensitivityProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3594086(1358-1368)Online publication date: 12-Jun-2023
      • (2023)Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry PracticeProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3594037(705-716)Online publication date: 12-Jun-2023
      • (2023)Trustworthy AI: From Principles to PracticesACM Computing Surveys10.1145/355580355:9(1-46)Online publication date: 16-Jan-2023
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