Critical Perspectives on Governance Mechanisms for AI/ML Systems

By Luke Stark, Daniel Greene, Anna Lauren Hoffmann. 2020

26 citations recorded by the source corpus.

This chapter provides a critical overview of proposed mechanisms for the ethical design and governance of contemporary artificial intelligence (AI) and machine learning (ML) systems. We describe various proposed mechanisms for AI/ML governance, paying particular attention to the possibilities and limits of these mechanisms for realizing truly just and equitable societal outcomes. In doing so we argue each category of intervention has reinforced and supported the broader regimes of corporate and state power under which AI/ML technologies are being developed, and that reformist initiatives relying on these mechanism risk cooptation and failure. We conclude by highlighting the abolitionist approach to AI-driven surveillance technology taken by the Movement for Black Lives and the workplace democracy approach taken by the #TechWontBuildIt technology workers campaign as alternative paradigms for AI/ML governance.

Subfields: Cognitive Neuroscience, Safety Research, Sociology and Political Science, AI Ethics, Fairness & Society

How researchers see this paper

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

In Cognitive Neuroscience

1. Irene Solaiman

In Safety Research

1. Irene Solaiman
2. James D. Herbsleb
3. Laura A. Dabbish
4. Dawn Nafus
5. David Gray Widder

In Sociology and Political Science

1. Irene Solaiman
2. Laura A. Dabbish
3. Dawn Nafus

In AI Ethics, Fairness & Society

1. Irene Solaiman
2. Dawn Nafus

Citations in the corpus

• The Gradient of Generative AI Release: Methods and Considerations (2023, background): “Instead of relying on existing fora, conversations can take lessons from social and abolitionist movements in how to include underrepresented and affected communities [93].”
• Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes (2022, background): “The Tech Won’t Build It movement “holds that workers developing AI/ML should have a say in how such technologies are deployed” [73], and the Tech Workers Coalition advocates (among other things) for workers to have a say in how the products of their labor serve “people, communities, and the environm”