From black box to glass box: algorithmic explainability as a strategic decision

32 Pages Posted: 10 Nov 2021 Last revised: 30 Nov 2022

See all articles by Adrien Raizonville

Adrien Raizonville

Institut Polytechnique de Paris

Xavier Lambin

ESSEC Business School

Date Written: September 21, 2022


The best-performing and most popular algorithms are often the least explainable. In parallel, there is growing concern and evidence that algorithms may engage, autonomously, in welfare-damaging strategies. Inspired by recent regulatory proposals, we model a firm's compliance strategy under the threat of (costly and imperfect) regulatory audits. Firms may invest in algorithmic ``explainability'' to better understand their own algorithms and reduce their cost of compliance.
We find that, when audit efficacy is not affected by explainability, audits always induce investment in explainability. Mandatory disclosure of the explainability level makes the auditing policy even more effective, because it allows firms to signal compliance.
If, instead, explainability makes audits more effective a firm may attempt to hide a potential misconduct behind algorithmic opacity, a phenomenon exacerbated by opportunistic auditing policies. In these cases, audits may stimulate the proliferation of black box algorithms and minimum explainability standards may need to be envisaged.

Keywords: Explainability, Algorithmic decision-making, Self-regulation, Audits, Output regulation.

JEL Classification: D21, D83, K24, K13, K42

Suggested Citation

Raizonville, Adrien and Lambin, Xavier, From black box to glass box: algorithmic explainability as a strategic decision (September 21, 2022). Available at SSRN: or

Adrien Raizonville (Contact Author)

Institut Polytechnique de Paris ( email )

Palaiseau Cedex

Xavier Lambin

ESSEC Business School ( email )

3 Avenue Bernard Hirsch
CS 50105 CERGY

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