Data analytics and data-driven approaches in Machine Learning are now among the most hailed computing technologies in many industrial domains. One major application is predictive analytics, which is used to predict sensitive attributes, future behavior, or cost, risk and utility functions associated with target groups or individuals based on large sets of behavioral and usage data. This paper stresses the severe ethical and data protection implications of predictive analytics if it is used to predict sensitive information about single individuals or treat individuals differently based on the data many unrelated individuals provided. To tackle these concerns in an applied ethics, first, the paper introduces the concept of “predictive privacy” to formulate an ethical principle protecting individuals and groups against differential treatment based on Machine Learning and Big Data analytics. Secondly, it analyses the typical data processing cycle of predictive systems to provide a step-by-step discussion of ethical implications, locating occurrences of predictive privacy violations. Thirdly, the paper sheds light on what is qualitatively new in the way predictive analytics challenges ethical principles such as human dignity and the (liberal) notion of individual privacy. These new challenges arise when predictive systems transform statistical inferences, which provide knowledge about the cohort of training data donors, into individual predictions, thereby crossing what I call the “prediction gap”. Finally, the paper summarizes that data protection in the age of predictive analytics is a collective matter as we face situations where an individual’s (or group’s) privacy is violated using data other individuals provide about themselves, possibly even anonymously.

机器学习中的数据分析和数据驱动方法现在是许多工业领域中最受欢迎的计算技术之一。一个主要应用是预测分析，它用于基于大量行为和使用数据来预测与目标群体或个人相关的敏感属性、未来行为或成本、风险和效用函数。本文强调了预测分析的严重道德和数据保护影响，如果它被用于预测有关单个个人的敏感信息或根据许多无关个人提供的数据区别对待个人。为了在应用伦理学中解决这些问题，首先，该论文引入了“预测隐私”的概念，以制定一项道德原则，保护个人和群体免受基于机器学习和大数据分析的差别待遇。其次，它分析了预测系统的典型数据处理周期，以提供对道德影响的逐步讨论，定位发生预测性隐私侵犯的事件。第三，本文阐明了预测分析在挑战道德原则（如人类尊严和（自由））个人隐私概念的方式方面的新特性。当预测系统将提供关于训练数据捐赠者队列的知识的统计推断转化为个人预测时，就会出现这些新挑战，从而跨越我所谓的“预测差距”。最后，其他人提供了关于他们自己的信息，甚至可能是匿名的。