Jakub Szarmach

Understanding Responsibilities in Al Practices

AI Governance Library Newsletter #2: Trust, Birds & AI Rationalist Cults

A blueprint for modern digital government

European Union Artificial Intelligence Act: a guide

AI Privacy Risks & Mitigations – Large Language Models

Incident Notification and Information Sharing Requirements: EU Digital Laws

Welcome to the AIGL

Choosing the Right Controls for AI Risks

AI Governance Controls Mega-map (Feb 2025)

MIT AI Risk Analysis Framework (AI-RAF)

AI Vendor Security and Safety Assessment Guide

A Taxonomy of Trustworthiness for Artificial Intelligence

AI Liability Along the Value Chain

From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models

Governance in the Age of Generative AI: A 360º Approach for Resilient Policy and Regulation

A Legal Framework for eXplainable Artificial Intelligence

NIST: Reducing Risks Posed by Synthetic Content An Overview of Technical Approaches to Digital Content Transparency

METHODOLOGY FOR THE RISK AND IMPACT ASSESSMENT OF ARTIFICIAL INTELLIGENCE SYSTEMS FROM THE POINT OF VIEW OF HUMAN RIGHTS, DEMOCRACY AND THE RULE OF LAW (HUDERIA METHODOLOGY)

Competences and governance practices for artificial intelligence in the public sector

Generative AI Governance Framework

Applying Data Protection Principles to Generative AI: Practical Approaches for Organizations and Regulators

Standards as a Basis for the Global Governance of Al in Research

Roles and Responsibilities Framework for Artificial Intelligence in Critical Infrastructure

Ethics in Action: From White Paper to Workplace