AI GOVERNANCE REFERENCES
COMPREHENSIVE COLLECTION OF ACADEMIC PAPERS, INDUSTRY REPORTS, AND REGULATORY DOCUMENTS ON TRUSTWORTHY AI AND RESPONSIBLE AI DEVELOPMENT.
ALL REFERENCES
12 REFERENCES FOUND
AI FAIRNESS 360: AN EXTENSIBLE TOOLKIT FOR DETECTING, UNDERSTANDING, AND MITIGATING UNWANTED ALGORITHMIC BIAS
Comprehensive toolkit for detecting and mitigating algorithmic bias in machine learning models.
FAIRNESS IN MACHINE LEARNING: A SURVEY
Comprehensive survey of fairness definitions, metrics, and mitigation strategies in machine learning.
EXPLAINABLE AI: FROM BLACK BOX TO GLASS BOX
Critical analysis of explainable AI methods and their importance for trustworthy AI systems.
A SURVEY ON BIAS AND FAIRNESS IN MACHINE LEARNING
Comprehensive survey of bias types, detection methods, and fairness metrics in machine learning.
THE MYTHOS OF MODEL INTERPRETABILITY
Critical examination of model interpretability and its role in building trustworthy AI systems.
TOWARDS TRUSTWORTHY AI DEVELOPMENT: MECHANISMS FOR SUPPORTING VERIFIABLE CLAIMS
Comprehensive framework for developing trustworthy AI systems with verifiable claims.
MODEL CARDS FOR MODEL REPORTING
Standardized framework for documenting machine learning models with transparency and accountability.
DATASHEETS FOR DATASETS
Standardized documentation framework for datasets to improve transparency and accountability.
A UNIFIED APPROACH TO INTERPRETING MODEL PREDICTIONS
Introduction of SHAP (SHapley Additive exPlanations) for model interpretability.
WHY SHOULD I TRUST YOU? EXPLAINING THE PREDICTIONS OF ANY CLASSIFIER
Introduction of LIME (Local Interpretable Model-agnostic Explanations) for model interpretability.
THE AI ACT: A GUIDE TO THE EU'S ARTIFICIAL INTELLIGENCE REGULATION
Comprehensive regulation framework for AI systems in the European Union.
RESPONSIBLE AI: A FRAMEWORK FOR GOVERNING MACHINE LEARNING SYSTEMS
Google's framework for developing responsible AI systems with fairness, safety, and privacy.
ADDITIONAL RESOURCES
EXPLORE ADDITIONAL RESOURCES FOR AI GOVERNANCE, TRUSTWORTHY AI, AND RESPONSIBLE AI DEVELOPMENT.
ACADEMIC CONFERENCES
- • FACCt (FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY)
- • AIES (AI, ETHICS AND SOCIETY)
- • NEURIPS (MACHINE LEARNING AND AI)
- • ICML (INTERNATIONAL CONFERENCE ON MACHINE LEARNING)
INDUSTRY ORGANIZATIONS
- • PARTNERSHIP ON AI
- • AI NOW INSTITUTE
- • ALGORITHMIC JUSTICE LEAGUE
- • CENTER FOR HUMAN-COMPATIBLE AI
REGULATORY BODIES
- • EUROPEAN COMMISSION AI ACT
- • NIST AI RISK MANAGEMENT FRAMEWORK
- • OECD AI PRINCIPLES
- • UNESCO AI ETHICS FRAMEWORK
READY TO IMPLEMENT TRUSTWORTHY AI?
USE THESE RESEARCH-BACKED APPROACHES TO BUILD FAIR, TRANSPARENT, AND ACCOUNTABLE AI SYSTEMS WITH FAIRMIND.