Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.
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Updated
Nov 19, 2019 - TeX
Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.
Paper for 2018 Joint Statistical Meetings: https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/AbstractDetails.cfm?abstractid=329539
Code for the paper EXPLORA: AI/ML EXPLainability for the Open RAN Claudio Fiandrino, Leonardo Bonati, Salvatore d'Oro, Michele Polese, Tommaso Melodia, Joerg Widmer CoNEXT ’23, December 5–8, 2023, Paris, France DOI: 10.1145/3629141
An end-to-end, research-grade AI system for measuring human cognition. HCMS models mastery, confidence, learning stability, and adaptability through analysis, inference, validation, robustness testing, and explainability — bridging human-centered AI research and applied systems.
AI Explainability 360 Toolkit for Time-Series and Industrial Use Cases
Official implementation of 'Bootstrap Wasserstein Alignment for Stable Feature Attribution in Low-Data Regimes'
Research paper exploring the use of argumentation theory in AI. This paper explores the optimal methodologies for the decision making of an AI agent.
🧠 Measure human understanding and cognitive stability with HCMS, an AI-driven framework for evaluating confidence and reasoning consistency.
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