Search results

The study focusses on the legal issues surrounding artificial intelligence (AI), which are being investigated and debated about several European Union initiatives to manage and regulate Information and Communication Technologies. The goal is to discuss the benefits and drawbacks of adopting AI technology and the ramifications for the articulations of law and politics in democratic constitutional countries. Thus, the study aims to identify socio-legal concerns and possible solutions to protect individuals’ interests. The exploratory study is based on statutes, rules, and committee reports. The study has used news pieces, reports issued by organisations and legal websites. The study revealed computer security vulnerabilities, unfairness, bias and discrimination, and legal personhood and intellectual property issues. Issues with privacy and data protection, liability for harm, and lack of accountability will all be discussed. The vulnerability framework is utilised in this chapter to strengthen comprehension of key areas of concern and to motivate risk and impact mitigation solutions to safeguard human welfare. Given the importance of AI’s effects on weak individuals and groups as well as their legal rights, this chapter contributes to the discourse, which is essential. The chapter advances the conversation while appreciating the legal work done in AI and the fact that this sector needs constant review and flexibility. As AI technology advances, new legal challenges, vulnerabilities, and implications for data privacy will inevitably arise, necessitating increased monitoring and research.

The context of this chapter is the use of data and advanced data analytics in a commercial setting. Privacy is considered as protection from vulnerability, whereby vulnerability is understood as the state of being exposed to the possibility of being harmed, either physically or emotionally, or in fundamental rights other than privacy. Therefore, privacy's policy instruments, in particular data protection law, could be seen as a means to reduce the risk of harm resulting from data use. Such harm is probabilistic and often uncertain, which, however, does not exclude analyzing costs and benefits of regulatory data protection policies. When balancing privacy protections and opportunities for knowledge gain, regulatory policy could be viewed as superior, when it expands the range of possible trade-offs between vulnerability protection and gaining socially beneficial knowledge.

Access to personal data is key to many of the most successful recent business models. These models rely on individuals outside of traditional organizational boundaries as their product, content providers, and customers. The topic of organizational boundaries is central to organizational research, and these models raise questions about the permeability of these new forms’ boundaries. Herein I elaborate on data-based business models, the organizational field that has emerged around data governance issues, and the institutions that have formed around it at different stages, by various actors. I also explore the interplay of institutional field and organizational boundaries, to identify how field-level issues influence the permeability of organizational boundaries.

This chapter explores how data-driven methods such as Artificial Intelligence pose real concerns for individual privacy. The current paradigm of collecting data from those using online applications and services is reinforced by significant potential profits that the private sector stands to realize by delivering a broad range of services to users faster and more conveniently. Terms of use and privacy agreements are a common source of confusion, and are written in a way that dulls their impact and dopes most into automatically accepting a certain level of risk in exchange for convenience and “free” access. Third parties, including the government, gain access to these data in numerous ways. If the erosion of individual protections of privacy and the potential dangers this poses to our autonomy and democratic ideals were not alarming enough, the digital surrogate product of “you” that is created from this paradigm might one day freely share thoughts, buying habits, and your pattern of life with whoever owns these data. We use an ethical framework to assess key factors in these issues and discuss some of the dilemmas posed by Artificial Intelligence methods, the current norm of sharing one's data, and what can be done to remind individuals to value privacy. Will our digital surrogate one day need protections too?

Doxing refers to the intentional public release by a third party of personal data without consent, often with the intent to humiliate, intimidate, harass, or punish the individual concerned. Intuitively, it is tempting to condemn doxing as a crude form of cyber violence that weaponizes personal data. When it is used as a strategy of resistance by the powerless to hold the powerful accountable, however, a more nuanced understanding is called for. This chapter focuses on the doxing phenomenon in Hong Kong, where doxing incidents against police officers and their family members have skyrocketed since 2019 (a 75-fold increase over 2018). It contends that doxing for political purposes is closely related to digital vigilantism, signifying a loss of confidence in the ruling authority and a yearning for an alternative form of justice. The chapter therefore argues that public interest should be recognized as a legal defense in doxing cases when those discharging or entrusted with public duty are the targets. Equally, it is important to confine the categories of personal data disclosed to information necessary to reveal the alleged wrongdoer or wrongdoing. Only in this way can a fair balance be struck between privacy, freedom of expression, and public interest.

The chapter deliberates on research ethics and the unanticipated side effects that technological developments have brought in the past decades. It looks at data protection and privacy through the prism of ethics and focuses on the need for safeguarding the fundamental rights of the research participants in the new digital era. Acknowledging the benefits of data analytics for boosting scientific process, the chapter reflects on the main principles and specific research derogations, introduced by the EU General Data Protection Regulation. Further on, it discusses some of the most pressing ethics concerns, related to the use, reuse, and misuse of data; the distinction between publicly available and open data; ethics challenges in online recruitment of research participants; and the potential bias and representativeness problems of Big Data research. The chapter underscores that all challenges should be properly addressed at the outset of research design. Highlighting the power asymmetries between Big Data studies and individuals’ rights to data protection, human dignity, and respect for private and family life, the chapter argues that anonymization may be reasonable, yet not the ultimate ethics solution. It asserts that while anonymization techniques may protect individual data protection rights, the former may not be sufficient to prevent discrimination and stigmatization of entire groups of populations. Finally, the chapter suggests some approaches for ensuring ethics compliance in the digital era.

The attempt to establish a common European framework for core platforms' duties and responsibilities toward other actors in the digital environment is at the core of the recent scholarly debate surrounding the Digital Markets Act (DMA) proposal. In particular, the everlasting juxtaposition between the “data power” – as emerging from recent cases (Section 2) – that dominant tech companies enjoy and the concept of consumer sovereignty (Section 3) lies at the core of the proposal's attempt to identify digital core platforms as market gatekeepers. Accordingly, this chapter critically investigates the divide between power imbalance and consumer sovereignty in light of the architecture designed by the DMA, with a specific focus on its effectiveness in identifying gatekeepers' power drivers (Section 4). After highlighting the main critical aspects of the pertinent rules, opportunities for fruitful developments are then identified through the reframing of some of the notions considered in the proposal, and namely the role of “lock-in” effects and “data accumulation” (Section 5). Lastly, this chapter suggests that the DMA advancements – while desirable – are bound to be fragmentary in the absence of a wider appraisal of the nature of data power imbalance dynamics in the modern digital markets (Section 6).

We propose a simple model consisting of two separated markets: the market for good y and the market for good x. Purchasing information about consumer behavior in the former market helps the monopolist firm, in the latter market, to price-discriminate. Consumers differ in their income and in their level of myopia. Personal data market regulation could both increase consumers' awareness about the treatment of their data and allow them to have their data erased from the data holder. We find that the former aspect of the policy reduces the number of transactions, and hence tends to reduce total surplus, while the second typically boosts willingness to pay of consumers and has positive effects on surplus, provided that the share of high-income consumers is not too high. The overall effect of regulation on total welfare depends on the share of high-income and myopic consumers.