Introducing a Practice-based Compliance Framework for Addressing New Regulatory Challenges in the AI Field

Sloane, Mona, and Emanuel Moss. 2022. “Introducing a Practice-Based Compliance Framework for Addressing New Regulatory Challenges in the AI Field.” TechReg Chronicle, March: 9.

9 Pages Posted: 6 Apr 2022 Last revised: 23 Jan 2023

Date Written: March 17, 2022

Abstract

Over the past years, regulatory pressure on tech companies to identify and mitigate the adverse impact of AI systems has been steadily growing. In 2022, we can expect this pressure to grow even further with transnational, national, federal, and local AI regulation kicking in. Many of these regulatory frameworks target both the design and the use of AI systems, often with a sector focus. AI practitioners and regulators alike are in need of new approaches that allow them to effectively respond to these regulations, and to enforce them competently. In this contribution, we will map out a Practice-Based Compliance Framework (PCF) for identifying existing principles and practices that are already aligned with regulatory goals, that therefore can serve as anchor points for compliance and enforcement initiatives.

Keywords: Social Practice Theory, AI, Compliance, Regulation

Suggested Citation

Sloane, Mona and Moss, Emanuel, Introducing a Practice-based Compliance Framework for Addressing New Regulatory Challenges in the AI Field (March 17, 2022). Sloane, Mona, and Emanuel Moss. 2022. “Introducing a Practice-Based Compliance Framework for Addressing New Regulatory Challenges in the AI Field.” TechReg Chronicle, March: 9. , Available at SSRN: https://ssrn.com/abstract=4060262

Mona Sloane

University of Virginia ( email )

1400 University Ave
Charlottesville, VA 22903
United States

Emanuel Moss (Contact Author)

Intel Labs ( email )

2200 Mission College Blvd.
Santa Clara, CA 95054-1549
United States

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