AI Explainability 360: Impact and Design
- 2022
- IAAI 2022
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45 results for Michael Hind
Ai explainability 360: An extensible toolkit for understanding data and machine learning models
- Vijay Arya
- Rachel Bellamy
- et al.
- 2020
- JMLR
Experiences with improving the transparency of AI models and services
- Michael Hind
- Stephanie Houde
- et al.
- 2020
- CHI EA 2020
AI Explainability 360 Toolkit
- Vijay Arya
- Rachel Bellamy
- et al.
- 2021
- CODS-COMAD 2021
FactSheets: Increasing trust in AI services through supplier's declarations of conformity
- Matthew Arnold
- David Piorkowski
- et al.
- 2019
- IBM J. Res. Dev
Think Your Artificial Intelligence Software Is Fair? Think Again
- Rachel Bellamy
- Kuntal Dey
- et al.
- 2019
- IEEE Software
AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias
- Rachel Bellamy
- Aleksandra Mojsilovic
- et al.
- 2019
- IBM J. Res. Dev
Constructing and Compressing Frames in Blockchain-based Verifiable Multi-party Computation
- Ravi Kiran Raman
- Kush R. Varshney
- et al.
- 2019
- ICASSP 2019
Promoting Distributed Trust in Machine Learning and Computational Simulation
- Nelson Bore
- Ravi Kiran Raman
- et al.
- 2019
- ICBC 2019
TED: Teaching AI to explain its decisions
- Michael Hind
- Dennis Wei
- et al.
- 2019
- AIES 2019