Grindrod Peter
Mathematical Institute, University of Oxford, Oxford, UK
Philos Trans A Math Phys Eng Sci. 2016 Dec 28;374(2083). doi: 10.1098/rsta.2016.0132.
We discuss the governing forces for analytics, especially concerning citizens' behaviours and their transactions, that depend on which of three of operation an institution is in (corporate, public sector/government and academic). We argue that aspirations and missions also differ by sphere even as digital spaces have drawn these spheres ever closer together. We propose that citizens' expectations and implicit permissions for any exploitation of their data require the perception of a fair balance of benefits, which should be transparent (accessible to citizens) and justifiable. We point out that within the most analytics does not concern identity, targeted marketing nor any direct interference with individual citizens; but instead it supports strategic decision-making, where the data are effectively anonymous. With the three spheres we discuss the nature of models deployed in analytics, including 'black-box' modelling uncheckable by a human mind, and the need to track the provenance and workings or models. We also examine the recent evolution of personal data, where some behaviours, or tokens, identifying individuals (unique and yet non-random) are partially and jointly owned by other individuals that are themselves connected. We consider the ability of heavily and lightly regulated sectors to increase access or to stifle innovation. We also call for clear and inclusive definitions of 'data science and analytics', avoiding the narrow claims of those in technical sub-sectors or sub-themes. Finally, we examine some examples of unethical and abusive practices. We argue for an ethical responsibility to be placed upon professional data scientists to avoid abuses in the future.This article is part of the themed issue 'The ethical impact of data science'.
我们讨论了分析的主导力量，特别是关于公民行为及其交易的主导力量，这取决于机构所处的三种运营模式中的哪一种（企业、公共部门/政府和学术）。我们认为，尽管数字空间拉近了这些领域的距离，但不同领域的愿望和使命也存在差异。我们提出，公民对于任何对其数据的利用的期望和隐含许可需要对利益的公平平衡有认知，这种平衡应该是透明的（公民可获取）且合理的。我们指出，在大多数情况下，分析并不涉及身份、定向营销或对个体公民的任何直接干预；相反，它支持战略决策，在这种情况下数据是有效匿名的。针对这三个领域，我们讨论了分析中所采用模型的性质，包括人类无法检验的“黑箱”建模，以及追踪模型来源和运作方式的必要性。我们还研究了个人数据的最新演变情况，即一些识别个人的行为或标识（独特但非随机）由其他相互关联的个人部分或共同拥有。我们考虑了监管严格和宽松的部门增加获取机会或抑制创新的能力。我们还呼吁对“数据科学与分析”给出清晰且包容的定义，避免技术子领域或子主题中的狭隘主张。最后，我们审视了一些不道德和滥用行为的例子。我们主张专业数据科学家应承担道德责任，以避免未来出现滥用行为。本文是主题为“数据科学的伦理影响”的特刊的一部分。
