MH
Michael Hind
cs.AIcs.CYcs.LGstat.MLcs.HCcs.CRcs.CVcs.DCcs.CLcs.IT
On Valency
published · living versionsW_ebbegyeg·v1 · currentpublished
Post-Training Large Language Models with LLMON
with Basel Shbita, Chad DeLuca, Nathan Fulton, Shubhi Asthana +6
1 version
Preprints & journals
20 papers in the corpus · 2018–2026LLMON: An LLM-native Markup Language to Leverage Structure and Semantics at the LLM Interface2603.22519v4 · Michael Hind, Basel Shbita, Bo Wu et al.2026 · 0 citationsarXiv
Developing a Risk Identification Framework for Foundation Model Uses2506.02066v1 · David Piorkowski, Michael Hind, John Richards et al.2025 · 0 citationsarXiv
BenchmarkCards: Standardized Documentation for Large Language Model Benchmarks2410.12974v3 · Anna Sokol, Elizabeth Daly, Michael Hind et al.2024 · 0 citationsarXiv
Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations2403.06009v3 · Swapnaja Achintalwar, Adriana Alvarado Garcia, Ateret Anaby-Tavor et al.2024 · 5 citationsarXiv
Quantitative AI Risk Assessments: Opportunities and Challenges2209.06317v3 · David Piorkowski, Michael Hind, John Richards2022 · 12 citationsarXiv
Evaluating a Methodology for Increasing AI Transparency: A Case Study2201.13224v2 · David Piorkowski, John Richards, Michael Hind2022 · 2 citationsarXiv
AI Explainability 360: Impact and Design2109.12151v1 · Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen et al.2021 · 20 citationsIAAI 2022
A Methodology for Creating AI FactSheets2006.13796v2 · John Richards, David Piorkowski, Michael Hind et al.2020 · 28 citationsarXiv
Trust and Transparency in Contact Tracing Applications2006.11356v1 · Stacy Hobson, Michael Hind, Aleksandra Mojsilovic et al.2020 · 11 citationsarXiv
Consumer-Driven Explanations for Machine Learning Decisions: An Empirical Study of Robustness2001.05573v1 · Michael Hind, Dennis Wei, Yunfeng Zhang2020 · 0 citationsarXiv
Experiences with Improving the Transparency of AI Models and Services1911.08293v1 · Michael Hind, Stephanie Houde, Jacquelyn Martino et al.2019 · 44 citationsarXiv
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques1909.03012v2 · Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen et al.2019 · 269 citationsarXiv
Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning1906.02299v1 · Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy et al.2019 · 2 citationsarXiv
TED: Teaching AI to Explain its Decisions1811.04896v2 · Michael Hind, Dennis Wei, Murray Campbell et al.2018 · 30 citationsarXiv
Promoting Distributed Trust in Machine Learning and Computational Simulation via a Blockchain Network1810.11126v1 · Nelson Kibichii Bore, Ravi Kiran Raman, Isaac M. Markus et al.2018 · 3 citationsarXiv
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias1810.01943v1 · Rachel K. E. Bellamy, Kuntal Dey, Michael Hind et al.2018 · 437 citationsarXiv
Trusted Multi-Party Computation and Verifiable Simulations: A Scalable Blockchain Approach1809.08438v1 · Ravi Kiran Raman, Roman Vaculin, Michael Hind et al.2018 · 19 citationsarXiv
FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformity1808.07261v2 · Matthew Arnold, Rachel K. E. Bellamy, Michael Hind et al.2018 · 434 citationsarXiv
Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images1805.12234v3 · Noel C. F. Codella, Chung-Ching Lin, Allan Halpern et al.2018 · 51 citationsarXiv
Teaching Meaningful Explanations1805.11648v2 · Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy et al.2018 · 8 citationsarXiv
Career total: 108 works. 20 are in this corpus.
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