Shivanand Pandit
On 6th October 2025, the Competition Commission of India (CCI) introduced a new proposal that requires companies using Artificial Intelligence (AI) systems to regularly review and update their algorithms to identify and mitigate any potential unfair competition. This idea originates from the CCI’s Market Study on Artificial Intelligence and Competition (2025), which aims to address competition issues arising from the rapid growth of AI technologies. The report says that self-checks and compliance programmes are important to spot risks early because AI systems, being independent and constantly learning, can quickly spread harmful effects in the market.
The study marks a strategic shift in CCI’s regulatory approach—from reactive enforcement to proactive risk management. It highlights how AI-driven systems, particularly those capable of autonomous or data-driven decision-making, can unintentionally result in collusion, discriminatory practices, or exclusionary conduct. The regulator emphasises that proactive compliance mechanisms will be crucial in preventing such risks from materialising and distorting competitive dynamics in Indian markets.
This initiative fits into India’s wider plans to regulate the digital economy. In its August 2025 report, the Parliamentary Standing Committee on Finance said that the CCI is getting ready to tackle new competition issues caused by AI, such as collusion between algorithms and control over large amounts of data. The Ministry of Corporate Affairs also mentioned that the AI market study will help shape the enforcement priorities of the upcoming Digital Competition Bill (DCB), which will set clearer rules for big digital companies in India’s growing technology sector.
The AI regulatory framework may not extend to Big Tech firms. These companies usually align their compliance policies with the world’s strictest standards—those of the European Union—and are unlikely to modify their large-scale AI models specifically for India. While the market study serves as a foundation for a potential AI regulatory framework, Big Tech is unlikely to fall under its scope in the immediate future.
The Framework
The self-audit framework urges enterprises—particularly those with significant market influence or extensive consumer reach—to examine how their AI systems impact market dynamics. It encourages companies to assess their algorithms for potential risks such as collusion, price coordination, exclusionary design, or inappropriate data sharing among competitors. Recognising that AI evolves faster than traditional oversight mechanisms, the CCI emphasises the need for structured audits to identify and mitigate anti-competitive effects early.
According to the accompanying guidance note, organisations should establish clear governance structures, assign accountability to senior management, and maintain detailed documentation of every AI deployment affecting competition. They must evaluate how algorithms process data, interact with competitors, and influence market variables like pricing or rankings. Before rolling out AI systems, firms should test them under various market scenarios to uncover unintended consequences. Post-deployment, continuous monitoring, automated triggers for human review, and comprehensive audit logs are essential. Transparency is also key—companies should be able to explain critical decision parameters without revealing proprietary information.
CCI further recommends embedding competition safeguards within overall compliance frameworks and training technical teams in competition law. Competition compliance, it stresses, should be an intrinsic element of AI system design rather than a retrospective addition. In addition, enterprises must record the algorithmic decision-making process and the origin of data used. Developers should integrate verifiable safeguards to prevent anti-competitive recommendations. While AI models must avoid sharing commercially sensitive data with rivals, the regulator reserves the right to scrutinise pricing algorithms if discriminatory practices are suspected. To oversee these developments, CCI plans to establish a think tank comprising academics, technologists, and policy experts to monitor AI adoption across industries.
The Audit Process
The CCI has proposed a six-step self-audit process to help companies identify and mitigate competition risks arising from the use of artificial intelligence. It urges firms to form cross-functional teams comprising legal, technical, and business experts to map all AI systems in operation, documenting their data inputs, logic, and outputs to understand market impact. Once systems are mapped, companies should identify algorithms posing the greatest competition risks and conduct detailed audits involving code reviews, simulations, and developer interviews. After each audit, firms must document findings, pinpoint weaknesses, and propose corrective measures, prioritising high-risk issues and reassessing regularly to enhance the auditability and explainability of AI systems.
To support implementation, the CCI has provided a non-binding self-audit checklist covering governance, algorithm design, testing, risk assessment, monitoring, and transparency. The checklist advises firms to establish governance frameworks, define compliance roles, and involve senior management in reviewing high-risk systems. It recommends assessing the use of competitor data, potential price alignment, and preferential ranking of affiliated products. Additionally, companies are encouraged to maintain audit trails, conduct stress tests, document validations, and set thresholds for human oversight, ensuring algorithms that behave unpredictably are suspended. Although the checklist is not legally enforceable, it serves as a structured guide to help organisations assess their AI systems and align them with competition norms.
This self-audit framework marks CCI’s first structured effort to address AI-related competition concerns in India. However, it relies largely on voluntary compliance, without enforcement or reporting obligations, raising questions about its practical effectiveness. The move aligns with a global push toward algorithmic accountability—similar to the EU’s mandatory AI conformity assessments and the UK CMA’s transparency guidelines—though CCI’s approach emphasises internal responsibility over legal compulsion. By embedding competition compliance into AI design, the framework could lay the foundation for future regulatory mechanisms in India, provided companies adopt it sincerely and the CCI evolves its monitoring strategy accordingly.
Closing Perspective
AI is an undefined organism whose eventual form and impact remain uncertain. Yet, its rapid growth has already stirred a mix of fear, despair, and hope. Businesses are rushing to embrace AI, believing it will enhance efficiency, productivity, and customer engagement. However, many Indian firms, reluctant to invest time and money in understanding AI’s full scope, are hastily bolting it onto existing systems, expecting miraculous outcomes. Some remain oblivious to potential collateral damage, while others, though aware of AI’s risks, appear comfortable allowing automation to guide them into anti-competitive practices and price manipulation.
The CCI’s recent study on AI and its potential to disrupt fair competition offers insightful observations, the most striking of which is its call for market players to self-regulate. The global AI market already exhibits winner-takes-all tendencies, with adoption creating concentration risks. Large players with vast resources could use AI aggressively to raise entry barriers, limit competition, and exploit their dominance. The scramble for massive datasets—essential for training AI on consumer behaviour and preferences—further entrenches this imbalance, as only well-funded firms can afford the required infrastructure and expertise. The CCI has rightly noted that removing such barriers is crucial to ensure a level playing field and to encouraging innovation and new entrants. Additionally, its study highlights the danger of ‘algorithmic collusion,’ where AI systems, even without human direction, might detect patterns and tacitly coordinate to inflate prices.
Another challenge lies in the concept of explainable AI, which aims to make the inner workings of AI systems transparent to humans. Historically, explainable AI has struggled to gain traction, and mandating it for Big Tech’s complex models would be commercially and technically difficult. The CCI’s stance is to require firms using proprietary AI models to incorporate explainability into their systems. However, as AI requires massive data inputs, achieving transparency is both cumbersome and costly. While the government explores broader solutions, such as universal access to critical data resources, the CCI’s recommendation that companies “self-audit” their AI systems for compliance with competition law appears insufficient. Businesses typically respond to tangible incentives or penalties, not moral persuasion, making voluntary compliance unlikely.
Given AI’s opacity and the secrecy surrounding its data and algorithms, regulators must urgently upgrade their capabilities. With AI investment expected to multiply fivefold over the next five years, the need for effective oversight is pressing. The CCI cannot rely solely on appeals for self-regulation; it must issue clear guidelines—crafted after stakeholder consultation—specifying guardrails and penalties for violations. These rules should remain flexible to address emerging risks, but clarity is essential now, as AI may already be influencing market behaviours like dynamic pricing. Unchecked AI deployment could easily slide into exploitation—imagine airlines using personal data to charge each traveller based on their urgency to fly. To prevent such abuses, the CCI must take the lead in defining responsible AI deployment that safeguards competition and serves the interests of the common citizen.
