Join the community!
Drop your email to stay ahead of the rest.
Responsible
AI
CDF AI governance assessment
A comprehensive self-assessment tool to assess your organisation’s AI governance readiness from planning to deployment. Based on 7 global AI governance frameworks and resources.
From code to consequence: Interrogating gender biases in LLMs within the Indian context – Digital Futures Lab
Research document critically examining gender biases in Large Language Models (LLMs) within India’s socio-cultural context, analyzing their origins, manifestations, and societal impacts.
Readiness assessment methodology- UNESCO
A tool designed to help countries evaluate their preparedness across legal, social, economic, educational, and technical dimensions to ethically implement AI in alignment with global standards.
Google – Perspectives on issues in AI governance
This resource explores the need for governments, civil society, and AI practitioners to collaborate on developing clear guidelines for AI governance.
Germany federal office for information security – Generative AI guidelines
This resource discusses generative AI models, highlighting their potential opportunities and associated risks for both industry and authorities.
KPMG governing AI responsibly
This AI Governance resource highlights the transformative potential and associated risks of generative AI, emphasising the need for robust governance frameworks.
NIST Risk Management Framework (RMF) playbook
This playbook includes actions, references, and related guidance to achieve the outcomes for the four functions in the AI Risk Management Framework (AI RMF): Govern, Map, Measure, and Manage.
NIST Risk Management Framework (RMF)
The Risk Management Framework (RMF) provides a process that integrates security, privacy, and cyber supply chain risk management activities into the system development life cycle.
AI blindspot discovery framework
A discovery process for spotting unconscious biases and structural inequalities in AI systems.