Module B focuses on the risks AI poses for social fairness and trust: how the use of AI-based tools can generate inequality or dishonesty, particularly when human productions differ in nature (e.g. creative vs.

This document examines how AI-driven content curation and recommendation systems affect the quality of public deliberation.

This interactive explainer introduces the concept of AI-generated deepfake images and provides clues to help the user understand how and why they are created.

This policy brief focuses on short-term action (2026-2028) around AI governance and provides practical guidelines for experts and policymakers. It introduces a framework that embeds democratic pillars — participation, freedom, equality, transparency, knowledge, and the rule of law — directly into the entire AI lifecycle.

Companies have significant influence over public discourse in online platforms, necessitating that the algorithms that shape these online platforms should be regulated and constrained to sufficiently consider the public interest (Susskind, 2018: 350).

The policy brief published by KT4D suggests that examining culture allows for a deeper understanding of societal responses to AI development.

The Recommendation Algorithms explainer aims to demonstrate how algorithms work on social media platforms. It allows the users to simulate their experience on a social media platform, where their choices shape a personalised feed.