规范AI：我们需要新的工具吗？

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摘要翻译：
人工智能范式（以下简称“AI”）建立在数据分析的基础上，除其他外，能够捕捉个人的行为和偏好。这些数据代表了数字生态系统中最有价值的货币，它们的价值源于它们是训练机器以开发人工智能应用程序的基本资产。在这种环境下，在线提供商通过向用户提供免费服务和交换通过使用这些服务生成的数据来吸引用户。鉴于这种互换可能带来的不平衡和市场失灵，这种互换具有隐含的性质，构成了本文件的重点。我们使用移动应用程序和相关的许可系统作为一个理想的环境，通过计量经济学工具来探索这些问题。来自一百万多个观察数据集的结果表明，买方和卖方都意识到，获得数字服务隐含着数据交换，尽管这对下载量（需求）和价格水平（供应）都没有相当大的影响。换句话说，这种交易的隐含性质不允许市场指标有效地发挥作用。我们得出结论，当前的政策（例如透明度规则）可能存在固有的偏见，并提出了一种新的方法的建议。
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英文标题：
《Regulating AI: do we need new tools?》
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作者：
Otello Ardovino, Jacopo Arpetti, Marco Delmastro
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最新提交年份：
2019
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分类信息：

一级分类：Economics        经济学
二级分类：General Economics        一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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英文摘要：
  The Artificial Intelligence paradigm (hereinafter referred to as "AI") builds on the analysis of data able, among other things, to snap pictures of the individuals' behaviors and preferences. Such data represent the most valuable currency in the digital ecosystem, where their value derives from their being a fundamental asset in order to train machines with a view to developing AI applications. In this environment, online providers attract users by offering them services for free and getting in exchange data generated right through the usage of such services. This swap, characterized by an implicit nature, constitutes the focus of the present paper, in the light of the disequilibria, as well as market failures, that it may bring about. We use mobile apps and the related permission system as an ideal environment to explore, via econometric tools, those issues. The results, stemming from a dataset of over one million observations, show that both buyers and sellers are aware that access to digital services implicitly implies an exchange of data, although this does not have a considerable impact neither on the level of downloads (demand), nor on the level of the prices (supply). In other words, the implicit nature of this exchange does not allow market indicators to work efficiently. We conclude that current policies (e.g. transparency rules) may be inherently biased and we put forward suggestions for a new approach.
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PDF链接：
https://arxiv.org/pdf/1904.12134

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关键词：Intelligence Applications Presentation Transparency Contribution exchange environment 隐含 digital nature