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Leniency Programmes Vs Algorithmic Collusion: An Equal Fight?




Katyayani Shekhar, Student, Amity Law School, Amity University Noida

Sonakshi Varshney, Assistant Professor, Amity Law School, Amity University Noida


ABSTRACT


The arrival of digital technologies has turned upside down the traditional competition law enforcement, challenging the effectiveness of the leniency programs that has since ages have been monitoring cartel behaviour. This paper aims to examine the growth of algorithm collusion where autonomous systems coordinate anti-competitive behaviour without explicit human involvement, and the profound implications it holds for antitrust regulation. Through careful analyses of incidents like Amazon Marketplace and European grocery markets, the learning reveals that traditional leniency frameworks, dependent on human whistleblowers, struggle to address the opacity, speed, and autonomy of algorithm-driven collusion. It analyses global responses, including the European Union’s Digital Markets Act and proposed reforms under India’s Competition Amendment Bill, 2022, highlighting the regulatory gaps and enforcement difficulties posed by algorithmic collusion. A comparative review of EU, US, and Indian competition law models further emphasizes the need for innovation-centric, dynamic regulatory approaches. The study concludes that without proactive reforms—such as algorithm auditing, enhanced transparency requirements, and cross-border cooperation—regulators risk falling behind rapidly evolving digital markets, ultimately undermining consumer welfare and fair competition.



Indian Journal of Law and Legal Research

Abbreviation: IJLLR

ISSN: 2582-8878

Website: www.ijllr.com

Accessibility: Open Access

License: Creative Commons 4.0

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Licensing: 

 

All research articles published in The Indian Journal of Law and Legal Research are fully open access. i.e. immediately freely available to read, download and share. Articles are published under the terms of a Creative Commons license which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

 

Disclaimer:

The opinions expressed in this publication are those of the authors. They do not purport to reflect the opinions or views of the IJLLR or its members. The designations employed in this publication and the presentation of material therein do not imply the expression of any opinion whatsoever on the part of the IJLLR.

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