This article focuses on the question of why firms self-regulate to avoid more severe public regulation in the area of antitrust compliance. We distinguish the effects of an antitrust authority’s outreach and enforcement on firms’ adoption of antitrust compliance programs. Furthermore, we examine the mechanism that may drive an antitrust authority’s actions on firms’ decisions to adopt compliance programs. Using a two-year survey of 432 firms drawn from the top three hundred Taiwanese enterprises and applying mediation analysis, we find that “voluntary” self-regulation actions, encouraged by the antitrust authority to promote compliance programs via advocacy, significantly increase the creation of antitrust compliance programs. Moreover, “coercive” actions of the antitrust authority in terms of enforcement are less effective than voluntary actions for firms’ compliance programs creation. Within “coercive” actions, large fines are more likely to lead to the adoption of antitrust compliance programs relative to other forms of government prosecution.

Changes in technology and the rise of artificial intelligence (AI) and machine-learning create new possibilities both for anti-competitive behavior and to aid in detection of such algorithmic collusion. To some extent, AI collusion takes traditional ideas of collusion and simply provides a technological overlay to them. However, in some instances, the mechanisms of both collusion and detection can be transformed using AI. This handbook chapter discusses existing theoretical and empirical work and identifies research gaps as well as avenues for new scholarship on how firms or competition authorities might invest in AI compliance to improve detection of wrongdoing.