Tag: data studies

Article on “Engaged research-led teaching: composing collective inquiry with digital methods and data”

Posted on | by Public Data Lab

A new article on “Engaged research-led teaching: composing collective inquiry with digital methods and data” co-authored by Jonathan Gray, Liliana Bounegru, Richard Rogers, Tommaso Venturini, Donato Ricci, Axel Meunier, Michele Mauri, Sabine Niederer, Natalia Sánchez-Querubín, Marc Tuters, Lucy Kimbell and Anders Kristian Munk has just been published in Digital Culture & Education.

The article is available here, and the abstract is as follows:

This article examines the organisation of collaborative digital methods and data projects in the context of engaged research-led teaching in the humanities. Drawing on interviews, field notes, projects and practices from across eight research groups associated with the Public Data Lab (publicdatalab.org), it provides considerations for those interested in undertaking such projects, organised around four areas: composing (1) problems and questions; (2) collectives of inquiry; (3) learning devices and infrastructures; and (4) vernacular, boundary and experimental outputs. Informed by constructivist approaches to learning and pragmatist approaches to collective inquiry, these considerations aim to support teaching and learning through digital projects which surface and reflect on the questions, problems, formats, data, methods, materials and means through which they are produced.

“Algorithm Trouble” entry in A New AI Lexicon

Posted on | by Public Data Lab

 A short piece on “Algorithm Trouble” for AI Now Institute‘s A New AI Lexicon, written by Axel Meunier (Goldsmiths, University of London), Jonathan Gray (King’s College London) and Donato Ricci (médialab, Sciences Po, Paris). The full piece is available here, and here’s an excerpt:

“For decades, social researchers have argued that there is much to be learned when things go wrong.¹ In this essay, we explore what can be learned about algorithms when things do not go as anticipated, and propose the concept of algorithm trouble to capture how everyday encounters with artificial intelligence might manifest, at interfaces with users, as unexpected, failing, or wrong events. The word trouble designates a problem, but also a state of confusion and distress. We see algorithm troubles as failures, computer errors, “bugs,” but also as unsettling events that may elicit, or even provoke, other perspectives on what it means to live with algorithms — including through different ways in which these troubles are experienced, as sources of suffering, injustice, humour, or aesthetic experimentation (Meunier et al., 2019). In mapping how problems are produced, the expression algorithm trouble calls attention to what is involved in algorithms beyond computational processes. It carries an affective charge that calls upon the necessity to care about relations with technology, and not only to fix them (Bellacasa, 2017).”