ABSTRACT:
Competition authorities have a challenging situation as artificial intelligence (AI) grows. AI has the potential to boost competition through flexible pricing and customised marketing, but it also raises new questions about collusion and market manipulation.
Algorithmic decision-making powered by artificial intelligence (AI) is altering market dynamics across several industries. There is a serious knowledge vacuum on the effects of AI on mergers, even as competition regulators struggle with the threat of algorithmic collusion. This study explores this little-studied area by examining the possibility that mergers driven by AI might either strengthen or establish market dominance. It incorporates the well-established worries about algorithmic collusion into merger control. We contend that evaluating AI algorithms’ competitive impact presents special difficulties due to their opaque nature. The abstract highlights the necessity of an all-encompassing framework that takes into account possibilities for algorithmic influence in mergers as well as the pro-competitive advantages brought about by AI. It highlights the significance of precise laws that tackle the possibility of algorithmic collusion without impeding AI’s pro-competitive advantages.
KEYWORDS: Artificial intelligence, competition, collusion, antitrust, market power, consumer choice, Big Data
CHAPTER 1:
1.1 Introduction:
In today’s world every industry be it small scale or largescale upto an extent driven by Technology. This technological advancements paves way for a data driven market place which employs various tactics for collection, storing and usage of data for advertising, market research and various other purposes. The integration of this data with Artificial Intelligence will fuel a more precise and sophisticated decision making and planning process aid in the overall improvement of both day-to-day decisions to long term innovations. As a result many market players employ these AI tools for data driven tasks such as price setting, predicting market dynamics and regulatory moves. The development of new AI technologies including the Large Language Model such as CHAT GPT and Co-pilot, are posed to drive this trend. In various countries this AI tools are employed in detecting and analysing anti-competitive conduct.
Even if using algorithms can surely have a major positive impact on pro-competitiveness, this isn’t always the case, especially when acquisitions and mergers are involved. The impact of algorithms on merger strategy have not yet received enough attention, either in the academic
books as well as in merger guidelines.. Furthermore, the majority of studies exclusively address algorithmic coordination, despite the fact that several academics have acknowledged the significance that algorithms play in merger strategy. In light of recent discoveries on the many ways in which algorithms impact both unilateral and coordinated anti-competitive behaviour, a more informed approach to AI in Mergers must be adopted.
1.2 Research Methodology:
This study adopts a theoretical and analytical approach, explaining each topic consisting in the paper for establishing the Impact of AI and Big Data on Antitrust Analysis in Mergers. This study employees both Qualitative and Quantitative data collection method. This paper consists of primary as well as secondary sources.
1.3 Review Of Literature:
Competition Law and Antitrust – A Global Guide by David J. Gerber: In this book the author had simplified the complicated and foreboding topic of Competition Law, examining not only the laws but also the goals, intuitions and methods that give them force in any competition law regime. Here the author has clearly pointed out how new challenges would arise out of a data driven practices and strategies.
Big Data and Competition Policy by Maurice E. Stucke and Allen P Grunes – In this book the author has attempted to define the term ‘Big Data’ by means of its volume and capacity. Moreover, the author has also laid down different categorizations of data driven mergers into those which fall outside the Competition Policy’s Conventional Category and those within by illustrating various case studies of mergers globally.
CHAPTER 2:
2.1 Challenges and Opportunities of AI & Big Data in Antitrust Analysis
Traditional antitrust analysis in mergers primarily relies on metrics like market share, concentration ratios, and barriers to entry. These metrics provide a snapshot of market structure at a particular point in time, assuming relatively transparent market dynamics with readily available information about firms’ capabilities and strategies. However, the rise of AI and Big Data introduces significant limitations.
[1] New and Dynamic Barriers to entry
AI algorithms can create dynamic capabilities that are difficult for competitors to replicate. These capabilities could include superior customer targeting through AI-powered marketing or the ability to rapidly adjust pricing and product offerings based on real-time market data and customer insights. Traditional metrics like market share may not adequately capture the potential competitive harm from such dynamic capabilities. For instance, a firm with a seemingly low market share but a powerful AI-driven pricing algorithm could effectively deter new entrants by strategically undercutting their prices whenever they attempt to gain a foothold in the market.
[2] Distorted market dynamics
Big Data gives businesses an in-depth understanding of consumer behaviour as well as market trends. This may be applied to strategic exclusion, a tactic whereby market leaders exploit their superior insights into data to disadvantage rivals. For instance, a dominant firm with a vast customer dataset could use targeted discounts or product differentiation strategies to make it unprofitable for new entrants to compete effectively.
[3] Opacity of Algorithmic decision making
Evaluating the actual competitive impact of many AI systems is difficult due to their non transparent nature. Antitrust regulators find it challenging to discern possible anti-competitive activity since they are not fully in sync with the decision-making process used by algorithms.
Big Data analytics may be used to spot patterns of possibly anti-competitive activity. Despite these difficulties, AI and Big Data have additional prospects to improve antitrust studies. Antitrust regulators can find concealed agreements, collusion, or any other exclusionary methods used by corporations by evaluating large datasets of price data, communication records, and customer interactions. Techniques for network analysis may be used to find links between businesses and evaluate whether or not they have the potential for coordinated behaviour. In the digital era, when intricate network arrangements can mask anti-competitive behaviour, this can be extremely advantageous.
Due to a lack of resources, the Competition Commission of India (CCI) has difficulty analysing large volumes of data. On the other hand, programmes like the Market Research Division of the CCI work to improve data gathering and analytic skills. The CCI can use AI and Big Data capabilities for antitrust investigations by working with academic institutions and data analytics specialists. This can help overcome resource constraints and identify potential anti-competitive practices.
AI simulations can be employed to predict the long-term competitive effects of mergers involving AI and Big Data capabilities. These simulations can model how the combined entity might leverage its AI and data resources to potentially harm competition by Squeezing out smaller competitors through superior pricing strategies, foreclosing competitors’ access to crucial data or resources and Limiting consumer choice and innovation.
Although AI-powered merger simulations are not yet widely used in India, there is recognition of their potential. These technologies may be used more frequently as the CCI develops its data analytics skills to evaluate the long-term competitive implications of mergers. The competitive implications of AI and Big Data in mergers can be captured by interpreting the Competition Act of 2002. The CCI can create internal policies and procedures that are specific to this changing environment. Antitrust regulators may create a more sophisticated method of merger research in the digital era by acknowledging the benefits as well as the problems posed by AI and Big Data.
2.2 Legal and Regulatory Considerations of AI and Data on Antitrust Analysis in Mergers
The legal bases upon which to analyse AI and Big Data in antitrust cases are continuously developing. The conventional measurements that are frequently the focus of antitrust regulations today, such as market share, may not fully account for the competitive implications of artificial intelligence and big data. To address these issues, a few governments are beginning to alter their legal systems. Rules for evaluating acquisitions involving businesses with substantial data and artificial intelligence (AI) capabilities have been released by the European Union (EU). In order to improve antitrust enforcement in the digital era, the United States is presently reviewing legislative proposals.The Competition Guidelines on Horizontal Mergers (2004, revised 2017) offer a framework for evaluating acquisitions involving businesses with substantial data and artificial intelligence capabilities, while also acknowledging the possible effects of technology and data on competition. These recommendations highlight how crucial it is to take into account:
- The possibility that the combined company may use its technology and data to prevent rivals from entering markets or resources.
- The potential for algorithmic collusion, in which competition might be harmed by coordinated algorithms rather than clear communication.
The European Union’s Market Definition Notice of 2016 provides clarification on how to identify pertinent markets in relation to competition law. In the era of digitalization, data and platform accessibility may play a critical role in identifying pertinent markets, sometimes even resulting in a more expansive definition. Furthermore, in order to tackle the issues posed by AI and big data, the EU is creating sector-specific legislation. The Digital Markets Act (DMA), for instance, attempts to control the actions of gatekeepers and major internet platforms.
With particular rules and legislation addressing the convergence of AI, Big Data, and competition law, the EU has a rather well-developed legal framework. The framework is currently being developed, so it’s unclear how well it will work to address new issues.
In this regard, the Competition Act of 2002 establishes the main framework for regulating mergers and acquisitions in the Indian antitrust context. The Act permits an individualised strategy that may be tailored to the difficulties of the digital era, even though it does not specifically address AI and Big Data. Mergers that have or are likely to have any “Appreciable Adverse Effect on Competition” (AAEC) in India are forbidden by Section 19 of The Competition Act, 2002. The Competition Commission of India (CCI) evaluates mergers according to criteria such as potential for innovation, market share, and entry obstacles. When reviewing mergers, the CCI is free to take into account how AI and big data can affect competition. Big Data and AI’s competitive implications may be taken into account by interpreting India’s Competition Act, which is interpretable. Nonetheless, there is significant confusion for both businesses and regulators due to the absence of clear rules or laws. It’s possible that the obstacles presented by AI and big data cannot be fully addressed by the legal frameworks in place at the moment. Regulators may find it challenging to comprehend the decision-making processes of algorithms, which makes it challenging to detect and validate anti-competitive activity. Companies possessing a strong position in data may have an unfair advantage if rivals find it difficult to get or make use of comparable data resources.
To fill in these gaps, there are a number of global discussions. The first is the Algorithmic Transparency Aspect, which calls for businesses to provide more information about how their algorithms operate. This might assist regulators in identifying and addressing anti-competitive behaviour. and the second is the part about the Data Portability Regulations’ introduction. Although this is primarily a discussion in the West, it tackles the issue of data portability and how it may level the playing field by enabling rivals to get and use pertinent data for their own goods and services.
2.3 SUGGESTIONS
Competition regulators need precise instructions on how to evaluate acquisitions incorporating AI and Big Data, both internationally and in India. Data-driven exclusionary behaviours and the possibility of algorithmic collusion should be taken into account in these guidelines. the requirement for openness in algorithmic decision-making processes utilised by combining businesses. Requirements for data portability to provide rivals an even playing field. Invest in The Competition Commission of India’s resources and knowledge to examine complicated data and comprehend the operation of AI algorithms.
Working along with data analysts and artificial intelligence specialists may be necessary for this. Algorithmic audits should be mandatory for companies undergoing mergers to make sure AI decision-making procedures are impartial, equitable, and do not stifle competition. Promote cooperation amongst competition authorities in other nations to exchange best practices and tackle new issues brought forth by AI and big data in an international marketplace. By putting these solutions into practice, we can establish a setting where AI and big data are ethically used in merger due diligence to analyse large volumes of data, resulting in better informed decisions and lower risk. We can also forecast the long-term effects of mergers on competition, enabling competition authorities to approve mergers that benefit consumers by promoting innovation and lowering prices.
2.4 CONCLUSION:
The influence of AI and Big Data on mergers calls for a multifaceted strategy. We can reduce the dangers of misuse and maximise the potential benefits of these technologies to support fair competition and a thriving digital economy by creating clear legal frameworks, encouraging openness in AI, and encouraging teamwork. The emergence of Big Data and artificial intelligence (AI) creates a difficult obstacle for merger control antitrust investigation. Although conventional measures such as market share could not fully convey the situation, new technologies present both chances and threats for rivalry.
Leveraging the possible advantages for a more sophisticated approach is just as important as understanding the potential hazards. Big Data and AI may raise further entrance obstacles. Better consumer targeting skills might make it difficult for new competitors to establish a presence. Competition can be further stifled by strategic exclusion strategies, in which incumbents use data to disfavour rivals. Furthermore, it might be difficult to determine how various AI algorithms will affect competitiveness because to their “black box” nature.Antitrust regulators have several instruments at their disposal despite these obstacles. Big Data analysis may be used to spot covert anti-competitive actions such as exclusionary or collusive behaviour. The long-term competitive impact of mergers incorporating AI-powered enterprises can be predicted through the use of AI simulations. These instruments must, however, be modified to comply with antitrust laws. In response to the issues posed by AI and big data in mergers, legal frameworks are changing. Leading the charge in the development of policies and rules that take into account the possible effects of technology and data on competition is the European Union (EU). India, on the other hand, must create internal guidelines and recommended procedures for AI and Big Data research because of its more lenient Competition Act.
Competition regulators need precise instructions on how to evaluate acquisitions powered by AI and big data, both internationally and in India. Algorithmic transparency, data portability, and algorithmic collusion should all be covered by these rules. It is essential to allocate resources and expertise to competition authorities. Working together with AI and data scientists can improve their skills. During mergers, algorithmic audits can guarantee impartiality and absence of prejudice in AI decision-making. Levelling the playing field and promoting data interoperability are two benefits of standardised data formats and protocols. Promoting cooperation among national competition authorities and enabling mergers that promote innovation are two strategies that can support a robust digital economy. We can establish a setting where AI and Big Data are used positively in the context of mergers by putting these technologies into practice.
Future implications of AI and big data on mergers are still being felt. Antitrust authorities can create a competitive digital environment that supports innovation and the welfare of consumers by seeing the potential and challenges and taking a proactive approach to legislative frameworks and cooperation. To guarantee that AI and Big Data are used for the benefit of society, constant communication between legislators, competition authorities, the IT sector, and academic institutions will be necessary.
REFERENCES:
- European Commission. (2016). Notice on the definition of relevant market for the purposes of Community competition law.
- Big Data and Competition Policy by Maurice E. Stucke and Allen P Grunes
- Wu, L. (2017). Machine learning and algorithmic fairness. Columbia Journal of Law and Economics, 40(4), 1151-1222 [5]
- European Commission. (2017). White Paper on Artificial Intelligence – A European approach to excellence and trust
- Calvani, A., & Virgillito, M. (2020). Algorithmic collusion in the digital age: A survey on theory and evidence. International Journal of Industrial Organization, 67, 282-315.
- The Competition Commission of India. https://www.cci.gov.in/
- Competition Law and Antitrust – A Global Guide by David J. Gerber
- Egerton-Doyle, Verity, and Jonathan Ford. “Algorithms, Big Data, and Merger Control.” In Algorithmic Antitrust, edited by Aurelien Portuese, 87. Cheltenham, UK: Edward Elgar Publishing, 2022.
By
ARJUN P
PRESIDENCY UNIVERSITY, BANGALORE