Listening to Misrecognition. A Critical Data Studies talk by Thao Phan

Listening to Misrecognition. What is the sound of racialisation? How might we listen to misrecognition? What does machine error tell us about the precision of racism? And how can the tools of a racist system be used to transcribe new forms of resistance?
This presentation is a collaboration between feminist STS researcher Thao Phan and Machine Listening, an ongoing investigation and experiment in collective learning, instigated by artist Sean Dockray, legal scholar James Parker, and researcher, curator and artist Joel Stern.
Part lecture and part performance, this event brings together critical work on race and algorithmic culture with artistic and experimental techniques for dissecting and analysing automatic speech recognition, applied to personal and public archives drawn from Thao’s life and research. It features an extended demonstration of the Machine Listening Word Processor, an art-based tool developed in 2021 by the Machine Listening team and Reduct, a US-based tech company co-founded by the artist Robert Ochschorn.

Thao Phan is a feminist science and technology studies (STS) researcher who specialises in the study of gender and race in algorithmic culture. She is a Lecturer in Sociology (STS) at the Research School for Social Sciences at the Australian National University (ANU). Thao has published on topics including whiteness and the aesthetics of AI, big-data-driven techniques of racial classification, and the commercial capture of AI ethics research. She is the President of AusSTS—Australia’s largest network of STS scholars.

This talk is hosted by the EFI Critical Data Studies Cluster. Critical Data Studies offers a space for scholars who use critique to study data-driven and AI technical systems.

This talk is sponsored by the AHRC's Briding AI Divides (BRAID) programme. BRAID is a UK-wide programme dedicated to integrating Arts and Humanities research more fully into the Responsible AI ecosystem, as well as bridging the divides between academic, industry, policy and regulatory work on responsible AI.

Host or Publisher EFI Critical Data Studies Cluster