Half-day Input

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

The eternal hype cycle of tech?

• self-driving cars, 2015
• Blockchain, 2019
• AI Sentience, 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.

“we understand the term language model (LM) to refer to systems which are trained on string prediction tasks: that is, predicting the likelihood of a token (character, word or string) given either its preceding context or (in bidirectional and masked LMs) its surrounding context.” (Bender et al., 2021, p. 611)

• 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