中圖分類號(hào)：D922
文獻(xiàn)標(biāo)識(shí)碼：A
DOI:10.19358/j.issn.2097-1788.2023.11.011
引用格式：劉奕麟.人工智能時(shí)代反壟斷法的挑戰(zhàn)：算法共謀的競(jìng)爭(zhēng)威脅與制度構(gòu)建［J］.網(wǎng)絡(luò)安全與數(shù)據(jù)治理，2023，42（11）：58-63，79.

The challenge of Antitrust Law in the AI era: the competitive threat of algorithmic collusion and rule improvement

Abstract： Algorithmic collusion poses a threat to the marketplace in that, on the one hand, Hub and Spoke Complicity uses the same third-party platforms to determine their pricing strategies, thus creating a Hub-and-Spoke setup that facilitates the exchange of information; on the other hand, Digital Eye can enter into covert autonomous conspiracies to facilitate competition avoidance without information sharing or explicit coordination behavior. The current analytical framework of Antitrust Law is limited in addressing the issue of algorithmic conspiracy. This dilemma is mainly manifested by the technical concealment and difficulty in proving algorithmic collusion, the controversial determination of collusion, and the lack of attribution criteria for algorithmic conspiracy. Therefore, at the level of institutional improvement, it is necessary to take into account both fair competition and consumer protection as well as efficiency goals, and not to inhibit the technological innovation and development of algorithms. Secondly, it is necessary to expand and update the definition of collusion and the criteria for identifying collaborative behavior in the existing regulations, so as to technically solve the problem of the concealment of algorithmic black box and algorithmic collusion. Finally, construct and clarify the attribution of responsibility for algorithmic collusion, and the operator′s negligence in the result of algorithmic collusion can be interpreted as having the intention to restrict competition.

Key words : algorithmic collusion; cooperative behavior; Hub and Spoke Complicity; self-learning algorithm; antitrust law