Gamified tool designed to address the social and cultural implications of 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.

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.

Module C of the Toolkit has two primary objectives: First, to understand AI and big data within the context of a long history of interactions between technological affordances and cultural norms, values, and practices. This recognises that knowledge technologies—such as written language, the printing press, television, radio, etc.—have shaped culture and knowledge production. The relationship between technology and culture is fundamentally mutual and reciprocal. Second, building upon the first objective, Module C focuses on the particular definition of AI and big data as advanced knowledge technologies (AKTs). We analyse the past in this module to better understand the present and—potentially—to anticipate what may lie ahead.

We adopt a systematic approach to map the entanglement between past and present knowledge technologies and culture. Unlike many contemporary discussions that focus on specific issues or technological applications (such as deepfakes or photo manipulation), we map the entirety of past and present knowledge technologies to identify trends, general divergences, and similarities.

Sub-chapter of DDL Handbook on the tension between efficiency paradigm and democracy

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