BY RANJUL MALIK, third-year student at ail, mohali

Introduction

Competition regulators across the world including places like Europe, the USA, Australia, Japan and even India are eyeing major amendments in their respective legislatures to better equip themselves in dealing with new and rising antitrust challenges. While there can be a combination of factors leading to this phenomenon, there can be no denying of the inability of regulators to control big tech as the primary factor for such global shifts. To this effect, the Competition Commission of India (‘CCI’) conventionally is likely to implement some changes along the lines of the Digital Markets Act introduced in the European Union.

It may also be of value for the CCI to draw inference with some provisions from developments taking place in rather far West, in the USA. The American Innovation and Choice Online Act (‘AICOA/the Bill’) is a proposed antitrust legislature which is touted as the tool to regulate ‘selective big tech players’. Though still to be cleared on floor by the Senate, this article aims to focus on facets of AICOA, which might be adopted by the CCI to solve some problems of the Indian antitrust ecosystem, especially with respect to big tech.

A Look Into the AICOA- Parallels With India

The bill on the back of bipartisan support in the senate provides new channels to deal with issues like the privacy of users, providing a definition for big tech players that are considered to be a threat to the competition and importantly, the issue of self-preferencing. A breakdown of these three issues vis-à-vis the Indian picture is as follows:

• Defining big tech (covered platforms)

Big tech in antitrust has often been constituted by large companies like Amazon, Apple, Google, etc. By quantifying the definition and referring to them as ‘covered platforms’, the AICOA is surely a mechanism provided against tech giants operating digitally. Most of such enterprises thrive on user data and algorithms based on user-generated data. Due to such instance, the new draft has left both telecommunication and financial services outside the ambit of the legislature, effectively applicable to only core tech enterprises dealing with user data. The AICOA shall apply to companies with either a market capitalization of $1 billion in the last one year or a user base of over 100 million users, to ensure both private and public companies are covered within the ambit.

Secondly, it restricts itself to companies with online platforms either generating user data, involving information queries basis the user, or involving transactions. The Competition Commission of India on the other hand has often failed to regulate big tech due to its inability to define relevant markets in the digital space, largely due to the fact that it still is sticking with the interchangeability or substitutability tests which do not always depict the correct picture with digital and tech-heavy cases.

Additionally, there is no definition with CCI with regards to big tech, with the Chairman of CCI once going on record to refer to big tech as “centres for entrenched and unchecked dominance“. Therefore, in case of there being difficulty in defining a relevant market for such players and a need to restrict their activities, the proposed definition within the AICOA with its quantitative nature ensures stricter compliance and regulations to such players.

At the same time, it is necessary to implement such provisions after negating their negative halves. For example, the AICOA definition will be practically limited to domestic players, with the threshold of 50 million US users or 1 lakh business users from USA, thus ousting from its realms the likes of Tencent, Huawei, etc. for USA. To elaborate further, WeChat, whose parent company happens to be Tencent, has only about 1.3 million users in USA while its global userbase is a whooping 1.3 billion. This step is a big drawback as it is pro-competitive for foreign players while impairing domestic competitiveness via restrictions.

• Selective preferencing

The issue of self-preferencing by giants like Google, Apple, Amazon has been in contention of many debates and discussions. Self-preferencing has been defined as “a digital platform giving preferential treatment to its own products and services when they are in competition with the products and services provided by other companies”. While conventionally an enterprise’s foray into a vertically related business is known to have pro-competitive and pro-consumer impact, it is only the rapid expansion by big tech and digital enterprises into multiple domains at once that has compelled policymakers to think over the practice of selective preferencing.

The example in the Senate was of Amazon Alexa promoting Amazon Basic products over its search results for queries related to products, in cases where they might be of sub-par quality to those of third-party sellers too. In the Indian regime questions of self-preferencing have been raised in cases like Matrimony.com Ltd and Google LLC and Ors, Umar Javeed and Ors v. Google LLC and Ors and in complaints received against likes of Apple. But because the self-preferencing is not per se violative under section 4 of the Competition Act, it has led to no strict actions against such enterprises.

In contrast, the AICOA proposes to impose a fine of 10% of the total revenue of the past year generated within the USA, in case enterprises were to be found engaged in self-preferencing. This thereby ensures that the referred covered platforms need to be neutral in their regular conduct so as not be discriminating against other third-party service providers present on their platforms. While the bill proposes standard exceptions like that for cybersecurity, the more interesting addition is two-layered criteria as an exception. The criteria check an activity vis-à-vis its importance to the core function of the company and comparing it with any less harmful method which could have been used to achieve a similar outcome. The two points as given are:

1. If an activity seems to be anti-competitive, then to check whether or not  such an act is being done to maintain or substantially enhance the core functionality of the covered platform.
2. And that the conduct could not be achieved through materially less discriminatory means, meaning there is no alternative which provides equal or effective results while being less discriminatory.

Hence the bill provides a strong framework which is mechanised with craft to treat unfair self-preferencing.

• Data and Privacy

The bill has introduced many terms which remain undefined and abstract and might be responsible for some high stake litigation in the upcoming times if it were to be enacted. Terms like ‘materially harm competition’, ‘generated data’, or ‘core functionality’ might not be defined, yet anyone can join the dots to point out the direction being aimed at is user data and its privacy. Lummis for example highlights how big tech companies use data generated on their platforms to their unfair advantage. This has explained by FTC and Privacy International in two ways, one by big tech having large data sets about consumers in different markets which help them design products of the future, and acting as gatekeepers to other companies who are in need of such user data and are ultimately made to adhere to unfair conditions to acquire such data.  The Google Fitbit merger at its helm was again a timely importance of such data to Big Tech, with google looking to grab hold of the data aquired by Fitbit over the years

The bill hands down the regulatory powers in entirety to the Federal Trade Commission (‘FTC’). While the powers given to the FTC are an effective step, the term ‘data’ has been broadly and vaguely used and hence the scope of the powers remains undefined and will require regulation in continuance with section 5 of the FTC Act, but the bottom-line is that the FTC is tasked to restrict commercialisation of sensitive user data to unfair advantage and ensuring building of user interfaces which facilitate sharing of such data with the FTC itself, effectively chartering the path to what might be USA’s first central data regulator.

In India, CCI has often recognized the role of data and IT as an evolving factor to analyze anti-competitive behavior, with the WhatsApp suo moto order talking of need to regulate collection and allocation of excessive data and in the context of platforms like Facebook having ability to process significant data. While data has been recognized by the CCI to be a non-price competitive parameter and even proposes a theory of harm for the same, the powers of the CCI have been at loggerheads with jurisdictional powers of the proposed Data Protection Authority (‘DPA’). A provision solidifying data regulating powers of the CCI in competition pretext would accordingly be a step in the right direction for regulation of this important little space at the hinge of data protection and competition law.

Conclusion

The AICOA is not perfect and is far from it, but that should not stop us from implementing some of the unconventionally creative solutions it poses. The bill falters and is criticized for its vague and unclear terminology which can be defined better for perusal in India. In fact, there can be a combination of provisions from a combination of foreign legislatures. For example- on the front of data privacy, creating a data regulatory body and allocating it powers similar to those given to th FTC while properly defining the term data similar to the Digital Markets Act and ultimately reaching a more concrete and multidimensional provision shall ensure better compliance. The possibilities are endless, and while the Competition (Amendment) Act of 2022 is awaited, the amendments across the world seem focused to put fair restrictions upon unfair practices which have been carried out by ‘the big tech’ for a considerable time. From this uniform complexity come uniform new solutions, which if implemented selectively, fit well into the scheme of things of various nations, as is the case of AICOA and implementation in the Indian regime on the front of data privacy, selective preferencing, and demarcating big tech.