Position: Bridge the Gaps between Machine Unlearning and AI Regulation

Bill Marino, Meghdad Kurmanji, Nicholas D. Lane

Keywords: Machine Unlearning, AI safety, AI regulation, responsible AI, Deep Learning, Large Language Models

Abstract:

The “right to be forgotten” and the data privacy laws that encode it
have motivated machine unlearning since its earliest days. Now, some
argue that an inbound wave of artificial intelligence regulations — like the
European Union’s Artificial Intelligence Act (AIA) — may offer important
new use cases for machine unlearning. However, this position paper argues,
this opportunity will only be realized if researchers proactively bridge the
(sometimes sizable) gaps between machine unlearning’s state of the art and
its potential applications to AI regulation. To demonstrate this point, we
use the AIA as our primary case study. Specifically, we deliver a “state of
the union” as regards machine unlearning’s current potential (or, in many
cases, lack thereof) for aiding compliance with various provisions of the
AIA. This starts with a precise cataloging of the potential applications of
machine unlearning to AIA compliance. For each, we flag the technical
gaps that exist between the potential application and the state of the art
of machine unlearning. Finally, we end with a call to action: for machine
learning researchers to solve the open technical questions that could unlock
machine unlearning’s potential to assist compliance with the AIA — and
other AI regulations like it.

Submission Number: 6