Regulation Games for Trustworthy Machine Learning
Mohammad Yaghini, Patty Liu, Franziska Boenisch, Nicolas Papernot
TL;DR
This paper introduces regulation games, a game-theoretic framework for trustworthy machine learning, modeling interactions between model builders and regulators to achieve socially optimal compliance solutions.
Contribution
It proposes a multi-objective multi-agent optimization framework and a ParetoPlay algorithm for finding efficient equilibria in regulation games.
Findings
Regulators can enforce privacy budgets more effectively by specifying their guarantees first.
The ParetoPlay algorithm finds Pareto-efficient solutions avoiding common equilibrium inefficiencies.
Simulation results provide policy guidance for ML regulation, exemplified by gender classification.
Abstract
Existing work on trustworthy machine learning (ML) often concentrates on individual aspects of trust, such as fairness or privacy. Additionally, many techniques overlook the distinction between those who train ML models and those responsible for assessing their trustworthiness. To address these issues, we propose a framework that views trustworthy ML as a multi-objective multi-agent optimization problem. This naturally lends itself to a game-theoretic formulation we call regulation games. We illustrate a particular game instance, the SpecGame in which we model the relationship between an ML model builder and fairness and privacy regulators. Regulators wish to design penalties that enforce compliance with their specification, but do not want to discourage builders from participation. Seeking such socially optimal (i.e., efficient for all agents) solutions to the game, we introduce…
Peer Reviews
Decision·Submitted to ICLR 2024
Strengths: - The proposed model of multi-agent multi-objective is novel and interesting to study. - The proposed scenario of SpecGame is well-defined and makes sense.
Weaknesses: - The assumption of a pre-calculated Pareto frontier as common knowledge does not have theoretical proofs and the discussion around the empirical evaluation in Appendix J is lacking. - Throughout the main body, the authors sometimes refer to notations that were not defined previously. For example, in Section 3.2, notation c_i^{(t)} and L_i^{(t)} are the first time the "(t)" superscript is used. In Equation 4, the notation \nabla_s is used without a definition. - The discussion of
-The idea seems interesting and novel and one can think of it as modeling a wide set of problems. -The paper is also concerned with an important problem (trustworthy ML).
1-I think the main contribution of the paper is to model the dynamic interaction between the model builder and the regulator. Accordingly, it is more reasonable to think of only fairness or privacy or to possibly even abstract/lump both issues into one. I don't see how having these two considerations has added to the model. One can also consider the safety of the model or its robustness to adversarial manipulations as part of the regulator's concern for example. 2-Why is the paper searching fo
- The problem of trade-off among different important criteria in trustworth ML is important. - The formulation of the interaction as a multi-agent game is intuitive. - The theoretical analysis and empirical results demonstrate the interaction among the agents in the game, which can be useful in understanding the (possible fundamental) limitations when designing new trustworth ML algorithms.
- A key assumption is the common-knowledge of a pre-calculated PF among the considered critera, specifically privacy, fairness and model utility. This can be difficult to satisfy in practice. - There are several (simplifying) assumptions made (which can take away the pratical feasibility of the work). For two examples, - > We assume that regulators are able to give penalties for violations of their respective objective which they formulate as a utility (or value) function. - > We assu
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Taxonomy
TopicsBlockchain Technology Applications and Security