AI-driven educational technologies (AI-EdTech) process extensive data, raising concerns about commercial exploitation of children's data and risks to their privacy, wellbeing, agency, and legal rights. The ‘fairness principle’ in data protection law requires fair data processing that meets children's expectations and avoids unexpected, detrimental, discriminatory, or misleading practices. However, children's own perspectives on what fairness means in AI-EdTech are underexplored in design. This study bridges the gap between law and design research to contextualize what fairness means through co-design workshops with 72 children (aged 10–12) and 4 teachers (N=76) in Scotland and Türkiye. We examine how children's perspectives can inform the operationalization of ‘fairness by design’ for AI-EdTech. Our contributions include: (1) an understanding of children's perspectives on how fairness manifests (or does not) in AI-EdTech and (2) recommendations for both design and legal communities to align AI-EdTech design and data practices with children's values and rights.

The volume of personal information, accessible online about individuals is unprecedented.Such information may be pieced together by others, to create a more detailed picture of a person, exposing them to potential harms, such as employment loss, unwanted attention, fraud, and more.In this context, relevance is contextual, situational and dependent, based on the risk it poses to the subject.In this paper, we explore this risk-based notion of relevance with the following questions in mind: How well can individuals identify and judge risks associated with online personal information?And, to what extent does this change individuals' awareness of their own information-sharing practices?In a user study, 243 participants were tasked with browsing fabricated online profiles to identify potential "risky" posts in one of two scenarios regarding either Identity Theft or Reputational Damage.On average, 72.2% of participants identified at least one risky post.However, only 23.7% identified dependent posts that taken together substantially increased the risk of identity theft or reputational damage.Further, participants reported greater awareness of potential risks that could arise from their own, and/or their friends' information sharing practices.Our findings suggest that when relevance is dependent on combining separate pieces of information to reveal risk, participants struggle to identify these cumulative revelations.Moreover, our study highlights that when participants perform tasks that feature personal information, it can lead to positive and negative experiences; changing their perceptions and increasing awareness about their own information behaviours while also raising concerns around their routine online practices.

This paper focuses on the legal compliance challenges of autonomous vehicles in a transnational context. We choose the perspective of designers and try to provide supporting legal reasoning in the design process. Based on argumentation theory, we introduce a logic to represent the basic properties of argument-based practical (normative) reasoning, combined with partial order sets of natural numbers to express priority. Finally, through case analysis of legal texts, we show how the reasoning system we provide can help designers to adapt their design solutions more flexibly in the cross-border application of autonomous vehicles and to more easily understand the legal implications of their decisions.

This paper presents the design, deployment and qualitative study of a large-scale, public, generative art exhibition, through which passers-by could create artworks, and mint a non-fungible-token (NFT). Following the month-long exhibition, during which 229 anonymous participants produced artworks, 69 non-transferable NFTs were minted, we surveyed (33) and interviewed (14) expert and novice participants about their experiences. We explored contemporary challenges of owning digital things, and the extent to which NFTs, and 'Web3' technologies offer meaningful forms of ownership. Our findings describe how the inability to trade this NFT, and its unique circumstances of acquisition, made it meaningful in ways that extended beyond its immediate (limited) utility and offered participants something through which to construct identity. Reflecting on the aspirations, contradictions, and misconceptions of forms of ownership enabled by NFTs, we conclude with proposals for renewed attention in HCI to the nature of digital possessions, and the potential for 'ownership design'.

The European Union has proposed the Artificial Intelligence Act which introduces detailed requirements of transparency for AI systems. Many of these requirements can be addressed by the field of explainable AI (XAI), however, there is a fundamental difference between XAI and the Act regarding what transparency is. The Act views transparency as a means that supports wider values, such as accountability, human rights, and sustainable innovation. In contrast, XAI views transparency narrowly as an end in itself, focusing on explaining complex algorithmic properties without considering the socio-technical context. We call this difference the “transparency gap”. Failing to address the transparency gap, XAI risks leaving a range of transparency issues unaddressed. To begin to bridge this gap, we overview and clarify the terminology of how XAI and European regulation – the Act and the related General Data Protection Regulation (GDPR) – view basic definitions of transparency. By comparing the disparate views of XAI and regulation, we arrive at four axes where practical work could bridge the transparency gap: defining the scope of transparency, clarifying the legal status of XAI, addressing issues with conformity assessment, and building explainability for datasets.

Autonomous vehicles, one of the signature technologies of the rapid development of artificial intelligence, have brought about a rapid change in the relevant legal norms and legal mandates. This change makes it more challenging for manufacturers and designers of autonomous vehicles to ensure the legal compliance of their product designs in a more dynamic way. Therefore, rather than approaching the issue from the perspective of judges or the cars themselves, we propose a legal reasoning system applicable to the adjustment of autonomous vehicle design options from the designer’s perspective, building on a series of previous studies. Focusing on the circulation of autonomous vehicles between different countries, the system attempts to help designers accomplish the adjustment of design solutions between different legal systems instead of designing new prototypes.

The paper argues that protecting post-mortem privacy is not solely beneficial for the deceased and their relatives but enables intergenerational data-sharing. However, legal approaches alone are unlikely to generate the trust required and need to be supplemented with tools that assist data subjects in controlling what data they risk sharing more efficiently and, which they prefer to delete. Using the example of Dickens' “Bonfire of letters” as an example, we argue that the main challenge for law and digital technology is the cumulative risk of data breadcrumbs, which are likely to be individually harmless. Based on research within the EPSRC project “Cumulative Revelations of Personal Data”, we discuss how our findings indicate possible avenues to assist in more efficient intergenerational data sharing.

As AI products continue to evolve, increasingly legal problems are emerging for the engineers that design them. Current laws are often ambiguous, inconsistent or undefined when it comes to technologies that make use of AI. Engineers would benefit from decision support tools that provide engineer’s with legal advice and guidance on their design decisions. This research aims at exploring a new representation of legal ontology by importing argumentation theory and constructing a trustworthy legal decision system. While the ideas are generally applicable to AI products, our initial focus has been on Autonomous Vehicles (AVs).

Searching for people online is a common search task that most of us have performed at some point or other. With so much information about people available online it is often amazing what one can find out about someone else -- especially when information taken from different sources is pieced together to create a more detailed picture of the individual, and then used to make inferences about them (leading to cumulative revelations ). As such, the relevance of one piece of information is often conditional and dependent on other pieces of information found. This creates interesting and novel challenges in evaluating informationrelevance when searching personal profiles, posts and related information about an individual, as well as the potential risks that can arise from such revelations. In this demonstration paper, we present a tool designed to investigate how people assess and judge the relevance and potential risks ofsmall, apparently innocuous pieces of information associated with fictitious personas, such as Taylor Addison, when searching and browsing online profiles and social media. The demonstrator also comprises a cyber-safety tool, which aims to provide education and raise awareness of the potential risks of cumulative revelations. It does so by engaging participants in different scenarios where the relevance of individual information items depends on the searcher and their particular underlying motivation.

When pieces from an individual's personal information available online are connected over time and across multiple platforms, this more complete digital trace can give unintended insights into their life and opinions. In a data narrative interview study with 26 currently employed participants, we examined risks and harms to individuals and employers when others joined the dots between their online information. We discuss the themes of visibility and self-disclosure, unintentional information leakage and digital privacy literacies constructed from our analysis. We contribute insights not only into people's difficulties in recalling and conceptualising their digital traces but of subsequently envisioning how their online information may be combined, or (re)identified across their traces and address a current gap in research by showing that awareness is lacking around the potential for personal information to be correlated by and made coherent to/by others, posing risks to individuals, employers, and even the state. We touch on inequalities of privacy, freedom and legitimacy that exist for different groups with regard to what they make (or feel compelled to make) available online and we contribute to current methodological work on the use of sketching to support visual sense making in data narrative interviews. We conclude by discussing the need for interventions that support personal reflection on the potential visibility of combined digital traces to spotlight hidden vulnerabilities, and promote more proactive action about what is shared and not shared online.

The paper suggests an amendment to Principle 4 of ethical robot design, and a demand for “transparency by design”. It argues that while misleading vulnerable users as to the nature of a robot is a serious ethical issue, other forms of intentionally deceptive or unintentionally misleading aspects of robotic design pose challenges that are on the one hand more universal and harmful in their application, on the other more difficult to address consistently through design choices. The focus will be on transparent design regarding the sensory capacities of robots. Intuitive, low-tech but highly efficient privacy preserving behaviour is regularly dependent on an accurate understanding of surveillance risks. Design choices that hide, camouflage or misrepresent these capacities can undermine these strategies. However, formulating an ethical principle of “sensor transparency” is not straightforward, as openness can also lead to greater vulnerability and with that security risks. We argue that the discussion on sensor transparency needs to be embedded in a broader discussion of “fair data handling principles” for robots that involve issues of privacy, but also intellectual property rights such as copyright.

This paper shall present a new theory called Transaction Configuration that describes the main task common to contract lawyers in the performance of their work, and was developed in the course of a case study at a magic circle law firm in the City of London. It will be shown how Transaction Configuration provides a practical context for legal normative assessment, as applied with LKIF, and legal reasoning systems in commercial law firms.

The paper reports of a collaborative project between computer scientists, lawyers, police officers, medical professionals and social workers to develop a communication in infrastructure that allows information sharing while observing Data Protection law “by design”, through a formal representation of legal rules in a firewall type system.

The paper describes a collaborative project between computer scientists, lawyers, police officers, medical professionals and social workers to develop a communication infrastructure that allows information sharing while observing Data Protection law “by design”, through a formal representation of legal rules in a firewall type system.

There exist two broad approaches to information retrieval (IR) in the legal domain: those based on manual knowledge engineering (KE) and those based on natural language processing (NLP). The KE approach is grounded in artificial intelligence (AI) and case-based reasoning (CBR), whilst the NLP approach is associated with open domain statistical retrieval. We provide some original arguments regarding the focus on KE-based retrieval in the past and why this is not sustainable in the long term. Legal approaches to questioning (NLP), rather than arguing (CBR), are proposed as the appropriate jurisprudential and cognitive underpinning for legal IR. Recall within the context of precision is proposed as a better fit to law than the ‘total recall’ model of the past, wherein conceptual and contextual search are combined to improve retrieval performance for both parties in a dispute.

This paper presents a methodology for integrating two approaches to building decision support systems (DSS) for crime investigation: symbolic crime scenario abduction [16] and Bayesian forensic evidence evaluation [5]. This is achieved by means of a novel compositional modelling technique that allows for automatically generating a space of models describing plausible crime scenarios from given evidence and formally represented domain knowledge. The main benefit of this integration is that the resulting DSS is capable to formulate effective evidence collection strategies useful for differentiating competing crime scenarios. A running example is used to demonstrate the theoretical developments.