Beyond privacy and exposure: Ethical issues within citizen-facing analytics

Journal article

We discuss the governing forces for analytics, especially concerning citizens’ behaviours and their transactions, that depend on which of three spheres 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 corporate sphere 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 is 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.

Authors: Grindrod, P

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Royal Society
• Journal: Philosophical Transactions A
• Volume: 374
• Issue: 2083
• Pages: 20160132
• Publication date: 2016-11-01
• Acceptance date: 2016-08-08
• DOI: 10.1098/rsta.2016.0132
• EISSN: 1471-2962
• ISSN: 1364-503X
• Keywords: analytics; ethics; data science; modelling