Making AI systems interpretable, explainable, and accountable
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International standards
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WEF, OECD analyses
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Explores XAI's transformative opportunity for financial institutions to enhance trust, compliance, and decision-making through transparent AI systems.
Defines accountability in AI context and analyses its architecture through compliance, reporting, oversight and liability goals with practical frameworks.
Provides insights into what organisations consider important in transparency and explainability of AI systems, bridging ethics and engineering.
Comprehensive study on trustworthy AI elements and integration of explainable AI methodologies across diverse applications and domains.
Foundational guide explaining algorithmic accountability concepts, mechanisms, and implementation approaches for diverse stakeholders.
A global AI ethics certification program assessing autonomous intelligent systems across transparency, accountability, algorithmic bias, and privacy.
A comprehensive database cataloguing AI system failures and harms to inform safer AI development through lessons learned from real-world incidents.
A comprehensive 290-page framework providing recommendations for ethical AI development based on five core principles and supported by the P7000 technical standards series.
Guidelines published in 2019 establishing seven key requirements for trustworthy AI including human agency, technical robustness, and accountability.
The first intergovernmental AI principles adopted in 2019, establishing five principles for responsible AI stewardship including inclusive growth and human-centered values.
Canada's mandatory assessment tool launched in 2019 for evaluating risks associated with automated decision systems used by federal government.
An institute focused on steering transformative technologies like AI toward benefiting humanity, known for the Asilomar AI Principles and AI Safety Index.
The leading digital civil liberties organization championing user privacy, free expression, and innovation through algorithmic accountability advocacy.
Other thematic areas that may interest you
Foundational principles, values, and frameworks guiding responsible AI development
ExploreFrameworks and methodologies for identifying, assessing, and mitigating AI risks
ExploreLegal requirements, regulations, and policy frameworks governing AI systems
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