The Algorithm Game
The Algorithm Game
复制标题
算法游戏
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Tal Z. Zarsky
中科院分区：
文献类型：
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作者：
Jane R. Bambauer;Tal Z. Zarsky
关于算法决策的大多数论述，无论是赞扬还是警告，都假定算法适用于一个静态的世界。但自动化决策是一个动态的过程。算法试图使用替代指标来评估某个对象一些难以衡量的特质，而这些对象反过来会改变自身行为，以便操纵系统，为自己争取更好的待遇（或者在某些情况下，是为了抗议该系统）。这些行为变化随后会促使算法进行修正。这些行动和反制行动就像一场舞蹈，对决策过程的公平性和效率具有重大影响。而且这场舞蹈可以通过法律来构建。然而，现行法律缺乏清晰的政策愿景，甚至缺乏一种连贯的语言来促进富有成效的讨论。
本文提供了基础。我们以信用评分、就业市场、刑事调查和企业声誉管理为关键例子，描述了操纵和反操纵策略。然后我们展示了法律是如何隐含地促进或抑制这些行为的，以及对准确性、分配公平性、效率和自主性的复杂影响。
Most of the discourse on algorithmic decisionmaking, whether it comes in the form of praise or warning, assumes that algorithms apply to a static world. But automated decisionmaking is a dynamic process. Algorithms attempt to estimate some difficult-to-measure quality about a subject using proxies, and the subjects in turn change their behavior in order to game the system and get a better treatment for themselves (or, in some cases, to protest the system.) These behavioral changes can then prompt the algorithm to make corrections. The moves and countermoves create a dance that has great import to the fairness and efficiency of a decision-making process. And this dance can be structured through law. Yet existing law lacks a clear policy vision or even a coherent language to foster productive debate.
This Article provides the foundation. We describe gaming and countergaming strategies using credit scoring, employment markets, criminal investigation, and corporate reputation management as key examples. We then show how the law implicitly promotes or discourages these behaviors, with mixed effects on accuracy, distributional fairness, efficiency, and autonomy.