The evolution of self-learning algorithms and sophisticated computers has posed new challenges to the enforcement of antitrust laws.
copyright by www.financialexpress.com
A decade ago, nobody would have thought that these sophisticated algorithms would become a competition concern. With activities like Internet of Things (IoTs), our everyday movements are being continuously tracked, and companies, to leverage their profitable position in the market, use our data. The purpose of this data collection is to conquer future market for Artificial Intelligence (AI). So, it is relevant to ask if AI calls for a regulatory intervention? (According to Merriam-Webster dictionary, AI means development of software and computers capable of self-learning and intelligent behaviour.)
While developing a future system for AI, it is important to bear in mind the standard-setting process to safeguard the future balance of competitive forces in the market and not to overlook the legal challenges posed by AI. It raises questions about the relationship between man and machine, the ability of humans to control the ‘deep-learning’ algorithms that are fed by data, the liability of humans, accountability for machine activities, and the antitrust liability of algorithm creators and users. Take, for example, the sophisticated pricing algorithms being used by commercial giants in online platform markets. They raise a potential risk of tacit collusion. Prima facie, they appear to be promoting information symmetry and perfect price transparency; however, they contribute to data-driven business models that aid in predicting markets. This has helped online trading platforms to process Big Data at real-time speed, thus making more accurate decisions.
As such, competition law prohibits anticompetitive agreements, abuse of dominance, and mergers that reduce competition. In the case of AI, it is difficult to establish the existence of an illicit/illegal agreement wherein each operator, aware about the development of other machines by its competitors in the market, is most likely to adopt similar pricing algorithms. This could lead to an anticompetitive agreement. Unlike the previous decade, agreements do not take place expressly between the executives in smoke-filled rooms, rather they happen in the digital world through automated algorithms, leading to more elusive forms of collusion.
Conscious parallelism behaviour by firms in the online market, leading to equilibrium prices above competitive levels, does not attract antitrust provisions. Thus, the main challenge before the competition authorities is to bring under its scanner such algorithm developers who program machines to unilaterally support tacit collusion. Competition agencies lack enforcement tools to do so. Such cases might be prosecuted under the banner of ‘unfair trade practices’. As in this case, ‘anticompetitive intent’ is a strong ground for establishing a cartel-like activity; a legislation to counter excessive transparency can do its bit when the competitors in the market abuse this transparency.[…]
read more – copyright by www.financialexpress.com
The evolution of self-learning algorithms and sophisticated computers has posed new challenges to the enforcement of antitrust laws.
copyright by www.financialexpress.com
While developing a future system for AI, it is important to bear in mind the standard-setting process to safeguard the future balance of competitive forces in the market and not to overlook the legal challenges posed by AI. It raises questions about the relationship between man and machine, the ability of humans to control the ‘deep-learning’ algorithms that are fed by data, the liability of humans, accountability for machine activities, and the antitrust liability of algorithm creators and users. Take, for example, the sophisticated pricing algorithms being used by commercial giants in online platform markets. They raise a potential risk of tacit collusion. Prima facie, they appear to be promoting information symmetry and perfect price transparency; however, they contribute to data-driven business models that aid in predicting markets. This has helped online trading platforms to process Big Data at real-time speed, thus making more accurate decisions.
As such, competition law prohibits anticompetitive agreements, abuse of dominance, and mergers that reduce competition. In the case of AI, it is difficult to establish the existence of an illicit/illegal agreement wherein each operator, aware about the development of other machines by its competitors in the market, is most likely to adopt similar pricing algorithms. This could lead to an anticompetitive agreement. Unlike the previous decade, agreements do not take place expressly between the executives in smoke-filled rooms, rather they happen in the digital world through automated algorithms, leading to more elusive forms of collusion.
Conscious parallelism behaviour by firms in the online market, leading to equilibrium prices above competitive levels, does not attract antitrust provisions. Thus, the main challenge before the competition authorities is to bring under its scanner such algorithm developers who program machines to unilaterally support tacit collusion. Competition agencies lack enforcement tools to do so. Such cases might be prosecuted under the banner of ‘unfair trade practices’. As in this case, ‘anticompetitive intent’ is a strong ground for establishing a cartel-like activity; a legislation to counter excessive transparency can do its bit when the competitors in the market abuse this transparency.[…]
read more – copyright by www.financialexpress.com
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