Unsupervised by any other name: Hidden layers of knowledge production in artificial intelligence on social media

By Anja Bechmann, Geoffrey C. Bowker. 2019

100 citations recorded by the source corpus.

Artificial Intelligence (AI) in the form of different machine learning models is applied to Big Data as a way to turn data into valuable knowledge. The rhetoric is that ensuing predictions work well—with a high degree of autonomy and automation. We argue that we need to analyze the process of applying machine learning in depth and highlight at what point human knowledge production takes place in seemingly autonomous work. This article reintroduces classification theory as an important framework for understanding such seemingly invisible knowledge production in the machine learning development and design processes. We suggest a framework for studying such classification closely tied to different steps in the work process and exemplify the framework on two experiments with machine learning applied to Facebook data from one of our labs. By doing so we demonstrate ways in which classification and potential discrimination take place in even seemingly unsupervised and autonomous models. Moving away from concepts of non-supervision and autonomy enable us to understand the underlying classificatory dispositifs in the work process and that this form of analysis constitutes a first step towards governance of artificial intelligence.

Subfields: Safety Research, AI Ethics, Fairness & Society, Social Sciences & Humanities

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Safety Research

1. Amanda Askell
2. Nithya Sambasivan
3. Miles Brundage

In AI Ethics, Fairness & Society

1. Amanda Askell
2. Pamela Mishkin
3. Nithya Sambasivan
4. Miles Brundage

In Social Sciences & Humanities

1. Lora Aroyo
2. Pamela Mishkin
3. Diana Akrong
4. Praveen K. Paritosh
5. Nithya Sambasivan
6. Alec Radford
7. Shivani Kapania
8. H. Highfill

Citations in the corpus

• Learning Transferable Visual Models From Natural Language Supervision (2021, background): “…decisions, training data, and choices about how classes are defined and taxonomized (which we refer to informally as “class design”) can all contribute to and amplify social biases and inequalities resulting from the use of AI systems (Noble, 2018; Bechmann & Bowker, 2019; Bowker & Star, 2000).”
• Machine Vision and Social Media Images: Why Hashtags Matter (2020, background): “Bechmann and Bowker (2019), therefore, rightly refer to the importance of user incentives that drive the diffusion of, and engagement with, images.”
• “Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI (2021, background): “Critical data studies researchers have demonstrated longstanding interest in the ‘discretionary’ [95] practices shaping data-driven systems and how they are designed and used [6, 16, 33], and the”
• Evaluating CLIP: Towards Characterization of Broader Capabilities and Downstream Implications (2021, background): “Algorithmic decisions, training data, and choices about how classes are defined and taxonomized (which we refer to as “class design”) can all contribute to and amplify social biases and inequalities resulting from the use of AI systems [14], [1], [3].”