算法时代的垄断协议规制:挑战与应对

算法具备自主学习和大数据处理的能力,有助于达成、实施垄断协议,形成算法驱动的合谋现象。算法合谋并无明显的意思联络,缺乏明确的合意证据,因而对传统的垄断协议概念带来挑战。算法决策存在"黑箱",导致经营者排除、限制竞争的主观意图无法得到验证。"人—机"联系弱化,进而导致宽恕政策和法律制裁失灵。面对算法技术的进步,反垄断法亟待建立因应时代变化的垄断协议检测、认定和制裁规则体系。为此,垄断协议的检测应当保持前摄性,借助大数据和经济分析手段筛选市场异质信号,进而在垄断协议认定方面适当降低证明标准,发挥算法作为间接证据的证明功能,最后,通过续造法律责任督促算法设计者、提供者履行竞争义务,保持算法的可问责性。     

Constructing Legal Order in an Intelligent Society

Legal order originated in a land-centered agricultural society, having now gone through a market-centered industrial society and a network-centered information society. With the rise of the intelligent society, it is transforming into an algorithmcentered legal order of an intelligent society. The “digital” “networking” “intelligent” revolution brought by the intelligent technologies including big data, cloud computing, the Internet, blockchain, and artificial intelligence (AI), has been causing critical challenges to the current legal order, and has generated powerful momentum for construction of fresh legal order featuring scientificness, people centeredness, justice, inclusiveness and co-governance. Construction of such a new jurisprudential legal order to solve this intelligent society’s “governance deficit” is an urgent task. There is a need to set up a new foundation on which an intelligent society can build steady and lasting progress.     

Predicting Proportionality: The Case for Algorithmic Sentencing

A basic principle in sentencing offenders is proportionality. However, proportionality judgments are often left to the discretion of the judge, raising familiar concerns of arbitrariness and bias. This paper considers the case for systematizing judgments of proportionality in sentencing by means of an algorithm. The aim of such an algorithm would be to predict what a judge in that jurisdiction would regard as a proportionate sentence in a particular case. A predictive algorithm of this kind would not necessarily undermine justice in individual cases, is consistent with a particularistic account of moral judgment, and is attractive even in the face of uncertainty as to the legitimate purposes of punishment.     

Cultures of high-frequency trading: mapping the landscape of algorithmic developments in contemporary financial markets

Abstract

As part of ongoing work to lay a foundation for social studies of high-frequency trading (HFT), this paper introduces the culture(s) of HFT as a sociological problem relating to knowledge and practice. HFT is often discussed as a purely technological development, where all that matters is the speed of allocating, processing and transmitting data. Indeed, the speed at which trades are executed and data transmitted is accelerating, and it is fair to say that algorithms are now the primary interacting agents operating in the financial markets. However, we contend that HFT is first and foremost a cultural phenomenon. More specifically, both individuals and collective agents – such as algorithms – might be considered cultural entities, charged with very different ways of processing information, making sense of it and turning it into knowledge and practice. This raises issues relating to situated knowledge, distributed cognition and action, the assignment of responsibility when regulating high-speed algorithms, their history, organizational structure and, perhaps more fundamentally, their representation.     

算法自动化决策中的女性劳动者权益保障

算法自动化决策在招聘、任务分配、劳动评估甚至解雇决策中发挥着日益重要的作用,但其对于职场性别平等影响的研究尚未展开。本文尝试通过对国内外职场算法性别歧视案例的分析,探讨算法在数字经济时代就业劳动市场不同阶段发挥的重要作用,分析算法自动化决策可能嵌入既有的性别偏见,成为隐藏性别歧视的分配手段,并且造成难以觉察的性别歧视后果。应对算法自动化决策带来的职场性别歧视,应扩大传统劳动法关于性别平等制度的适用,并具体化算法自动化决策规制的相关制度。此外,还应积极构建性别平等的算法伦理,建设覆盖算法生命周期的性别平等制度,并发展针对算法性别偏见的审计与评估制度,通过制度设计和社会支持系统促进数字经济时代的职场性别平等。