Critical Perspectives on Large Language Models (LLM) such as ChatGPT

Half-day Input

Z-Modul Kunst und Künstliche Intelligenz by Andreas Kohli, 12-17.02.2023

Main Sources:

Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜.” In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–23. Virtual Event Canada: ACM.

Bender, Emily. 2022. Resisting dehumanization in the age of AI. Talk at CogSci: Interdisciplinary Study of the Mind (07.29), 62 Min

Mozilla Internet Health Report. 2022. Who Has Power Over AI?

Avoidable and Unavoidable Bias in the AI Pipeline by Felix Stalder, 2022

Training Data for ChatGPT

Colossal Clean Crawled Corpus

Common Crawl Data Set

Dodge, Jesse, Maarten Sap, Ana Marasović, William Agnew, Gabriel Ilharco, Dirk Groeneveld, Margaret Mitchell, and Matt Gardner. 2021. “Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus.” arxiv.org

Simple example of bias in machine translation:

Assumption of gender, statistical vs linguistic view

Exploring Bias by artistic means

Mediengruppe Bitnik, State of Reference, 2017

Group Work

create groups of ~4 people

play around with ChatGPT to document a specific bias /missinformation (30 minutes)

brainstorm an application by which to make use of this bias/missinformation at scale (30 minutes)

Make a short (5-minute) presentation on the bias and it application for (ab)use