In the age of Big Data, companies and governments are increasingly using algorithms to inform hiring decisions, employee management, policing, credit scoring, insurance pricing, and many more aspects of our lives. Artificial intelligence systems can help us make evidence-driven, efficient decisions, but can also confront us with unjustified, discriminatory decisions wrongly assumed to be accurate because they are made automatically and quantitatively. It is becoming evident that these technological developments are consequential to people’s fundamental human rights. Despite increasing attention to (...) these urgent challenges in recent years, technical solutions to these complex socio-ethical problems are often developed without empirical study of societal context and the critical input of societal stakeholders who are impacted by the technology. On the other hand, calls for more ethically and socially aware AI often fail to provide answers for how to proceed beyond stressing the importance of transparency, explainability, and fairness. Bridging these socio-technical gaps and the deep divide between abstract value language and design requirements is essential to facilitate nuanced, context-dependent design choices that will support moral and social values. In this paper, we bridge this divide through the framework of Design for Values, drawing on methodologies of Value Sensitive Design and Participatory Design to present a roadmap for proactively engaging societal stakeholders to translate fundamental human rights into context-dependent design requirements through a structured, inclusive, and transparent process. (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)

The debate on the ethics of privacy has been mainly dominated by Western perspectives, to the exclusion of broader ethical theories and socio-cultural perspectives. This imbalance carries risks; transplanted ethical norms and values can collide with those of the communities in which they are deployed. The consequent homogenization might also represent a missed opportunity to enrich and develop the current paradigm of privacy protection so as to effectively face new technological challenges. This article introduces and discusses the sub-Saharan philosophy of (...) Ubuntu and argues how its conception of the self helps to reinterpret some of the emerging issues revolving digital information technologies. To begin with, a general overview of the debate on the ethics of privacy is provided by distinguishing between individual and relational privacy. Also, the challenges of 'group privacy' are discussed. Then, we introduce basic principles of Ubuntu focusing on how these may have affected privacy conceptions and related legal practices. By outlining opportunities and risks of intercultural information ethics, we argue how Ubuntu—similarly to other communitarian moral philosophies—strengthens the development of the concept of relational privacy and, in particular, of group privacy. (shrink)

The study of law and information technology comes with an inherent contradiction in that while technology develops rapidly and embraces notions such as internationalization and globalization, traditional law, for the most part, can be slow to react to technological developments and is also predominantly confined to national borders. However, the notion of the rule of law defies the phenomenon of law being bound to national borders and enjoys global recognition. However, a serious threat to the rule of law is looming (...) in the form of an assault by technological developments within artificial intelligence (AI). As large strides are made in the academic discipline of AI, this technology is starting to make its way into digital decision-making systems and is in effect replacing human decision-makers. A prime example of this development is the use of AI to assist judges in making judicial decisions. However, in many circumstances this technology is a ‘black box’ due mainly to its complexity but also because it is protected by law. This lack of transparency and the diminished ability to understand the operation of these systems increasingly being used by the structures of governance is challenging traditional notions underpinning the rule of law. This is especially so in relation to concepts especially associated with the rule of law, such as transparency, fairness and explainability. This article examines the technology of AI in relation to the rule of law, highlighting the rule of law as a mechanism for human flourishing. It investigates the extent to which the rule of law is being diminished as AI is becoming entrenched within society and questions the extent to which it can survive in the technocratic society. (shrink)

An issue about the privacy of the clustered groups designed by algorithms arises when attempts are made to access certain pieces of information about those groups that would likely be used to harm them. Therefore, limitations must be imposed regarding accessing such information about clustered groups. In the discourse on group privacy, it is argued that the right to privacy of such groups should be recognised to respect group privacy, protecting clustered groups against discrimination. According to this viewpoint, this right (...) places a duty on others, for example, private companies, institutions, and governments, to refrain from accessing such information. To defend the idea that the right to privacy should be recognised for clustered groups, at least two requirements must be satisfied. First, clustered group privacy must be conceived of as either a collective good or a participatory good. Since these forms of good are of the type from which no member of a group can be excluded from benefiting, the right to them is defined as a group right. Second, there must be group interests on which to base a group right. Group interests can be either the interests of those members that are a result of their being in the group or the interests of the group as a whole that transcend the interests of its members. However, this paper argues that clustered group privacy cannot be conceived of as either a collective or a participatory good because it is possible for some individuals to be excluded from benefiting from it. Furthermore, due to the lack of awareness among individuals that they are members of a clustered group and the nature of a clustered group itself, such groups cannot have the group interests necessary to establish a group right. Hence, the group right to privacy cannot be recognised for these groups, implying that the group right cannot be considered a means to protect clustered groups against discrimination. Instead, this paper suggests that moral principles need to be articulated within an ethics of vulnerability to identify the moral obligations of protecting vulnerable clustered groups. The duty owed to the vulnerable should involve refraining from accessing certain information about clustered groups in specific contexts. This duty is not engendered by the right to privacy of such groups; it is the duty owed to the vulnerable. The findings highlight the need to articulate moral principles regarding privacy and data protection to protect clustered groups in contexts in which accessing information about them could constitute a reason for discriminatory targeting. (shrink)

Differential privacy (DP) aims to confer data processing systems with inherent privacy guarantees, offering strong protections for personal data. But DP’s approach to privacy carries with it certain assumptions about how mathematical abstractions will be translated into real-world systems, which—if left unexamined and unrealized in practice—could function to shield data collectors from liability and criticism, rather than substantively protect data subjects from privacy harms. This article investigates these assumptions and discusses their implications for using DP to govern data-driven systems. In (...) Parts 1 and 2, we introduce DP as, on one hand, a mathematical framework and, on the other hand, a kind of real-world sociotechnical system, using a hypothetical case study to illustrate how the two can diverge. In Parts 3 and 4, we discuss the way DP frames privacy loss, data processing interventions, and data subject participation, arguing it could exacerbate existing problems in privacy regulation. In part 5, we conclude with a discussion of DP’s potential interactions with the endogeneity of privacy law, and we propose principles for best governing DP systems. In making such assumptions and their consequences explicit, we hope to help DP succeed at realizing its promise for better substantive privacy protections. (shrink)

Digital data help data scientists and epidemiologists track and predict outbreaks of disease. Mobile phone GPS data, social media data, or other forms of information updates such as the progress of epidemics are used by epidemiologists to recognize disease spread among specific groups of people. Targeting groups as potential carriers of a disease, rather than addressing individuals as patients, risks causing harm to groups. While there are rules and obligations at the level of the individual, we have to reach a (...) stage in the development of data analytics where groups are protected as entities. This chapter offers a deeper examination of harms to the groups. (shrink)

The distinction between distributive and non-distributive profiles figures prominently in current evaluations of the ethical and epistemological risks that are associated with automated profiling practices. The diagnosis that non-distributive profiles may coincidentally situate an individual in the wrong category is often perceived as the central shortcoming of such profiles. According to this diagnosis, most risks can be retraced to the use of non-universal generalisations and various other statistical associations. This article develops a top-down analysis of non-distributive profiles in which this (...) fallibility of non-distributive profiles is no longer central. Instead, it focuses on how profiling creates various asymmetries between an individual data-subject and a profiler. The emergence of informational, interest, and perspectival asymmetries between data-subject and profiler explains how non-distributive profiles weaken the epistemic position of a profiled individual. This alternative analysis provides a more balanced assessment of the epistemic risks associated with non-distributive profiles. (shrink)

ABSTRACTThe 2016 US presidential election witnessed the development of ethically dubious methods of political persuasion built and executed on social networking sites. The following analyzes the ethics of behavioral microtargeting practices such as those of Cambridge Analytica, a company that claims to have played a central role in Donald Trump’s successful candidacy through its use of social networking sites such as Facebook. After describing Cambridge Analytica’s method of data manipulation and identifying several threats to individual autonomy posed by such practices, (...) this research explores whether Immanuel Kant’s categorical imperative could be used by individuals to effectively guard democratic processes. (shrink)

Mature information societies are characterised by mass production of data that provide insight into human behaviour. Analytics has arisen as a practice to make sense of the data trails generated through interactions with networked devices, platforms and organisations. Persistent knowledge describing the behaviours and characteristics of people can be constructed over time, linking individuals into groups or classes of interest to the platform. Analytics allows for a new type of algorithmically assembled group to be formed that does not necessarily align (...) with classes or attributes already protected by privacy and anti-discrimination law or addressed in fairness- and discrimination-aware analytics. Individuals are linked according to offline identifiers and shared behavioural identity tokens, allowing for predictions and decisions to be taken at a group rather than individual level. This article examines the ethical significance of such ad hoc groups in analytics and argues that the privacy interests of algorithmically assembled groups in inviolate personality must be recognised alongside individual privacy rights. Algorithmically grouped individuals have a collective interest in the creation of information about the group, and actions taken on its behalf. Group privacy is proposed as a third interest to balance alongside individual privacy and social, commercial and epistemic benefits when assessing the ethical acceptability of analytics platforms. (shrink)

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 dataotherindividuals provide about themselves, possibly even anonymously. (shrink)

Purpose Privacy has been understood as about one’s own information, information that is not one’s own is not typically considered with regards to an individual’s privacy. This paper aims to draw attention to this issue for conceptualizing privacy when one’s privacy is breached by others’ information. Design/methodology/approach To illustrate the issue that others' information can breach one's own privacy, this paper uses real-world applications of forensic genealogy and recommender systems to motivate the discussion. Findings In both forensic genealogy and recommender (...) systems, the individual’s privacy is breached by information that is not one’s own. The information that breached one’s privacy, by its nature, is beyond the scope of an individual, which is a phenomenon that has already been captured by emerging discussions about group privacy. This paper further argues that the underlying issue reflected by the examples of forensic genealogy is an extreme case even under the consideration of group privacy. This is because, unlike recommender systems that rely on large amounts of data to make inferences about an individual, forensic genealogy exposes one’s identity by using only one other individual’s information. This paper echoes existing discussions that this peculiar situation where others’ information breaches one’s own privacy reveals the problematic nature of conceptualizing privacy relying only on individualistic assumptions. Moreover, this paper suggests a relational perspective as an alternative for theorizing privacy. Originality/value This situation that others’ information breached one’s own privacy calls for an updated understanding of not only privacy but also the relationship between the person and their information. Privacy scholars need to renew their ethical language and vocabularies to properly understand the issue, which recent privacy conceptualizations are already doing (e.g. group privacy). (shrink)