An accountability-based privacy governance model is one where organizations are charged with societal objectives, such as using personal information in a manner that maintains individual autonomy and which protects individuals from social, financial and physical harms, while leaving the actual mechanisms for achieving those objectives to the organization. This paper discusses the essential elements of accountability identified by the Galway Accountability Project, with scholarship from the Centre for Information Policy Leadership at Hunton & Williams LLP. Conceptual _Privacy by Design_ principles (...) are offered as criteria for building privacy and accountability into organizational information management practices. The authors then provide an example of an organizational control process that uses the principles to implement the essential elements. Initially developed in the ‘90s to advance privacy-enhancing information and communication technologies, Dr. Ann Cavoukian has since expanded the application of _Privacy by Design_ principles to include business processes. (shrink)

In this classic work of feminist political thought, Iris Marion Young challenges the prevailing reduction of social justice to distributive justice.

The practice of obtaining informed consent has its history in, and gains its meaning from, medicine and biomedical research. Discussions of disclosure and justified nondisclosure have played a significant role throughout the history of medical ethics, but the term “informed consent” emerged only in the 1950s. Serious discussion of the meaning and ethics of informed consent began in medicine, research, law, and philosophy only around 1972.

This essay critically examines some classic philosophical and legal theories of privacy, organized into four categories: the nonintrusion, seclusion, limitation, and control theories of privacy. Although each theory includes one or more important insights regarding the concept of privacy, I argue that each falls short of providing an adequate account of privacy. I then examine and defend a theory of privacy that incorporates elements of the classic theories into one unified theory: the Restricted Access/Limited Control (RALC) theory of privacy. Using (...) an example involving data-mining technology on the Internet, I show how RALC can help us to frame an online privacy policy that is sufficiently comprehensive in scope to address a wide range of privacy concerns that arise in connection with computers and information technology. (shrink)

In this article, I summarise the ontological theory of informational privacy (an approach based on information ethics) and then discuss four types of interesting challenges confronting any theory of informational privacy: (1) parochial ontologies and non-Western approaches to informational privacy; (2) individualism and the anthropology of informational privacy; (3) the scope and limits of informational privacy; and (4) public, passive and active informational privacy. I argue that the ontological theory of informational privacy can cope with such challenges fairly successfully. In (...) the conclusion, I discuss some of the work that lies ahead. (shrink)

The paper outlines a new interpretation of informational privacy and of its moral value. The main theses defended are: (a) informational privacy is a function of the ontological friction in the infosphere, that is, of the forces that oppose the information flow within the space of information; (b) digital ICTs (information and communication technologies) affect the ontological friction by changing the nature of the infosphere (re-ontologization); (c) digital ICTs can therefore both decrease and protect informational privacy but, most importantly, they (...) can also alter its nature and hence our understanding and appreciation of it; (d) a change in our ontological perspective, brought about by digital ICTs, suggests considering each person as being constituted by his or her information and hence regarding a breach of one’s informational privacy as a form of aggression towards one’s personal identity. (shrink)

Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used to train them. While computational techniques are emerging to address aspects of these concerns through communities such as discrimination-aware data mining and fairness, accountability and transparency machine learning, their practical implementation faces real-world challenges. For legal, institutional or commercial reasons, organisations might not hold the data on sensitive attributes such as gender, ethnicity, sexuality or disability needed to diagnose and mitigate emergent indirect discrimination-by-proxy, such (...) as redlining. Such organisations might also lack the knowledge and capacity to identify and manage fairness issues that are emergent properties of complex sociotechnical systems. This paper presents and discusses three potential approaches to deal with such knowledge and information deficits in the context of fairer machine learning. Trusted third parties could selectively store data necessary for performing discrimination discovery and incorporating fairness constraints into model-building in a privacy-preserving manner. Collaborative online platforms would allow diverse organisations to record, share and access contextual and experiential knowledge to promote fairness in machine learning systems. Finally, unsupervised learning and pedagogically interpretable algorithms might allow fairness hypotheses to be built for further selective testing and exploration. Real-world fairness challenges in machine learning are not abstract, constrained optimisation problems, but are institutionally and contextually grounded. Computational fairness tools are useful, but must be researched and developed in and with the messy contexts that will shape their deployment, rather than just for imagined situations. Not doing so risks real, near-term algorithmic harm. (shrink)

In the age of big data and a machine epistemology that can anticipate, predict, and intervene on events in our lives, the problem once again is that a few individuals possess the knowledge of how to regulate these activities. But the question we face now is not how to share such knowledge more widely, but rather of how to enjoy the public benefits bestowed by this knowledge without freely sharing it. It is not merely personal privacy that is at stake (...) but a range of unsung benefits that come from ignorance and forgetting, traits that are inherently human and integral to the functioning of our society. (shrink)

The dominant approach in privacy theory defines information privacy as some form of control over personal information. In this essay, I argue that the control approach is mistaken, but for different reasons than those offered by its other critics. I claim that information privacy involves the drawing of epistemic boundaries—boundaries between what others should and shouldn’t know about us. While controlling what information others have about us is one strategy we use to draw such boundaries, it is not the only (...) one. We conceal information about ourselves and we reveal it. And since the meaning of information is not self-evident, we also work to shape how others contextualize and interpret the information about us that they have. Information privacy is thus about more than controlling information; it involves the constant work of producing and managing public identities, what I call “social self- authorship.” In the second part of the essay, I argue that thinking about information privacy in terms of social self- authorship helps us see ways that information technology threatens privacy, which the control approach misses. Namely, information technology makes social self- authorship invisible and unnecessary, by making it difficult for us to know when others are forming impressions about us, and by providing them with tools for making assumptions about who we are which obviate the need for our involvement in the process. (shrink)

Artificial Intelligence research and regulation seek to balance the benefits of innovation against any potential harms and disruption. However, one unintended consequence of the recent surge in AI research is the potential re-orientation of AI technologies to facilitate criminal acts, term in this chapter AI-Crime. AIC is theoretically feasible thanks to published experiments in automating fraud targeted at social media users, as well as demonstrations of AI-driven manipulation of simulated markets. However, because AIC is still a relatively young and inherently (...) interdisciplinary area—spanning socio-legal studies to formal science—there is little certainty of what an AIC future might look like. This chapter offers the first systematic, interdisciplinary literature analysis of the foreseeable threats of AIC, providing ethicists, policy-makers, and law enforcement organisations with a synthesis of the current problems, and a possible solution space. (shrink)