《大数据世界:利益、威胁和伦理挑战

Marina Da Bormida
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Likewise, it is necessary to prevent the so-called ‘social cooling’. This represents the long-term negative side effects of the data-driven innovation, in particular of such scoring systems and of the reputation economy. It is reflected in terms, for instance, of self-censorship, risk-aversion and lack of exercise of free speech generated by increasingly intrusive Big Data practices lacking an ethical foundation. Another key ethics dimension pertains to human-data interaction in Internet of Things (IoT) environments, which is increasing the volume of data collected, the speed of the process and the variety of data sources. It is urgent to further investigate aspects like the ‘ownership’ of data and other hurdles, especially considering that the regulatory landscape is developing at a much slower pace than IoT and the evolution of Big Data technologies. 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引用次数: 3

摘要

大数据、人工智能、数据驱动创新等领域的发展,为全社会、各行各业带来巨大利益。相比之下,它们的滥用可能导致数据工作流程绕过隐私和数据保护法的意图,以及道德要求。这可能被称为大数据的“蠕变因素”,需要立即解决,特别是考虑到我们正在走向“数据化”的社会,在这个社会中,用于捕获、收集、存储和处理数据的设备变得越来越便宜、越来越快,而计算能力也在不断提高。如果在道德健全和社会关注的框架内以真正匿名的方式使用大数据能够成为可持续发展的推动者,那么在这样的框架之外使用大数据会带来许多威胁、潜在障碍和多重道德挑战。一些例子是新的监视工具和数据收集技术对隐私造成的影响,包括群体隐私、高科技分析、自动化决策和歧视性做法。在我们的社会中,每件事都可以打分,改变生活的关键机会越来越多地由这样的评分系统决定,这些评分系统通常是通过应用于数据的秘密预测算法来确定谁有价值的。因此,有必要保证这种评分系统的公平性和准确性,并确保基于这些评分系统的决策是在合法和道德的方式下实现的,避免可能影响个人机会的污名化风险。同样,有必要防止所谓的“社会降温”。这代表了数据驱动创新的长期负面影响,特别是这种评分系统和声誉经济。这反映在一些方面,例如,由于缺乏道德基础的大数据实践日益侵入性,导致自我审查、风险规避和言论自由缺乏行使。另一个关键的伦理维度与物联网(IoT)环境中的人-数据交互有关,这增加了收集的数据量、处理速度和数据源的多样性。迫切需要进一步调查数据的“所有权”和其他障碍等方面,特别是考虑到监管环境的发展速度远慢于物联网和大数据技术的发展。这些只是大数据引发的问题和后果的一些例子,这需要采取适当的措施来应对“数据信任赤字”,而不是朝着禁止收集数据的方向发展,而是朝着识别和禁止滥用数据和不公平的行为和待遇的方向发展,一旦政府和公司拥有这些数据。与此同时,辩论应进一步探讨“数据利他主义”,深化我们社会中不断增加的数据如何具体用于公共利益和最佳实施模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Big Data World: Benefits, Threats and Ethical Challenges
Advances in Big Data, artificial Intelligence and data-driven innovation bring enormous benefits for the overall society and for different sectors. By contrast, their misuse can lead to data workflows bypassing the intent of privacy and data protection law, as well as of ethical mandates. It may be referred to as the ‘creep factor’ of Big Data, and needs to be tackled right away, especially considering that we are moving towards the ‘datafication’ of society, where devices to capture, collect, store and process data are becoming ever-cheaper and faster, whilst the computational power is continuously increasing. If using Big Data in truly anonymisable ways, within an ethically sound and societally focussed framework, is capable of acting as an enabler of sustainable development, using Big Data outside such a framework poses a number of threats, potential hurdles and multiple ethical challenges. Some examples are the impact on privacy caused by new surveillance tools and data gathering techniques, including also group privacy, high-tech profiling, automated decision making and discriminatory practices. In our society, everything can be given a score and critical life changing opportunities are increasingly determined by such scoring systems, often obtained through secret predictive algorithms applied to data to determine who has value. It is therefore essential to guarantee the fairness and accurateness of such scoring systems and that the decisions relying upon them are realised in a legal and ethical manner, avoiding the risk of stigmatisation capable of affecting individuals’ opportunities. Likewise, it is necessary to prevent the so-called ‘social cooling’. This represents the long-term negative side effects of the data-driven innovation, in particular of such scoring systems and of the reputation economy. It is reflected in terms, for instance, of self-censorship, risk-aversion and lack of exercise of free speech generated by increasingly intrusive Big Data practices lacking an ethical foundation. Another key ethics dimension pertains to human-data interaction in Internet of Things (IoT) environments, which is increasing the volume of data collected, the speed of the process and the variety of data sources. It is urgent to further investigate aspects like the ‘ownership’ of data and other hurdles, especially considering that the regulatory landscape is developing at a much slower pace than IoT and the evolution of Big Data technologies. These are only some examples of the issues and consequences that Big Data raise, which require adequate measures in response to the ‘data trust deficit’, moving not towards the prohibition of the collection of data but rather towards the identification and prohibition of their misuse and unfair behaviours and treatments, once government and companies have such data. At the same time, the debate should further investigate ‘data altruism’, deep-ening how the increasing amounts of data in our society can be concretely used for public good and the best implementation modalities.
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