Predictive privacy: towards an applied ethics of data analytics

• Published in: Ethics and information technology 2021-12, Vol.23 (4), p.675-690
• Main Author: Mühlhoff, Rainer
• Format: Article
• Language: English
• Subjects: Big Data; Computer Science; Data analysis; Data processing; Ethics; Innovation/Technology Management; Library Science; Machine learning; Management of Computing and Information Systems; Mathematical analysis; Original Paper; Predictive analytics; Privacy; Risk taking; User Interfaces and Human Computer Interaction
• ISSN: 1388-1957; 1572-8439
• DOI: 10.1007/s10676-021-09606-x

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.