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.

Recipe series. 1st describing DITL

Designing tech with friction

This recipe is about designing an entire democratic process—not just the AI tool within it. When AI is introduced into a deliberative setting, the surrounding process needs to change too: not just to make the AI work, but to make sure the democracy works.

Being a facilitator in a deliberative process using AI

This section analyses how different knowledge technologies impact people’s attention and, consequently, their decisions regarding which information is worth storing and remembering, and which is instead forgotten or not even registered in the first place.

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.