In response to the emergence of COVID in England in 2020, the government declared a suspension of face-to-face education. To deal with the cancellation of exams, then minister of education Gavin Williamson assigned the Office of Qualifications (OfQual) with devising a method for awarding grades to students based on the standards they would have met had the examinations not been cancelled. As a response, OfQual implemented a prediction matrix based on the resources available to them: this system is wildly referred to as the OfQual algorithm. However, as the points were delivered on 13 August 2020, disputes arose leading to the cancellation of the algorithm. This paper will focus on the OfQual algorithm as a particularly relevant case to highlight the tensions around the notion of fairness in the implementation of such systems. Drawing on Science and Technology Studies, I will start by opening the black box of the OfQual algorithm. This will, in turn, allow me to identify the conflicting accounts of what is considered as fair in such system and how such accounts were inscribed within this algorithm, questioning what it means for such system to be fair in contexts marked by inequalities.

This paper examines the profound challenges posed by manipulative artificial intelligence (AI) and critically evaluates the adequacy of the EU AI Act in mitigating these threats. Modern AI technologies possess the capability to influence human cognition and behaviour imperceptibly, thus endangering cognitive freedom, the fundamental right to autonomous thought. Although the EU AI Act classifies manipulative AI as an unacceptable risk and prohibits its deployment, its current framework, characterized by imprecise definitions and regulatory gaps, undermines its efficacy in holding entities accountable and safeguarding individuals. To address these deficiencies, this paper introduces an innovative analytical method that traces the origins of manipulation, enabling a systematic understanding of the harm. Central to this discussion is the expanded concept of cognitive freedom, which transcends conventional notions of thought rights to encompass protection from covert digital influence. Through illustrative case studies, such as the use of psychographic profiling in political campaigns, the paper elucidates how data-driven methodologies can be harnessed to subtly mould public perception and decision-making. The analysis further investigates digital design strategies, including targeted advertising and algorithmic curation, which constrain user autonomy and erode independent judgment. The paper advocates for a restructured EU AI Act that incorporates precise definitions, mandatory transparency and continuous oversight by independent, multidisciplinary bodies. Such enhancements would strengthen the detection and regulation of manipulative AI practices. By embedding cognitive freedom within legal protections and proposing real-time audits and comprehensive ethical assessments, this paper outlines a strategic pathway for preserving cognitive autonomy. This approach aims to mitigate the erosion of mental sovereignty and uphold the essential principles of independent thought and informed decision-making within the rapidly evolving digital landscape.

This study investigates the discriminatory impact of artificial intelligence (AI)-driven content moderation on social media platforms (SMPs), particularly in the Global South, where cultural and linguistic diversity often clash with the Western-centric AI frameworks. Platforms like Meta increasingly rely on AI algorithms to moderate vast amounts of content, but research shows that these algorithms disproportionately restrict free expression in the Global South (European Union Agency for Fundamental Rights, 2023; De Gregorio & Stremlau, 2023). This results in “over removal” – censorship of lawful content – and “slow removal,” which fails to address harmful material, both of which perpetuate inequality and hinder free speech. Through a case study on Meta, this research examines how AI-based content moderation misunderstands local contexts and systematically marginalizes users. The contributing factors include limited financial investment, inadequate language training, and political and corporate biases. The imbalance reflects power asymmetries, as governments in the Global South lack influence over platform policies. This study uses a human rights perspective to explore solutions through multistakeholder engagement, advocating for collaboration among tech companies, governments, and civil society to reform AI governance. Ultimately, it aims to inform regulatory frameworks that ensure fairer, more inclusive content moderation and protect free expression for a globally equitable digital landscape.

The adoption of AI is pervasive, often operating behind the scenes and influencing decisions without our explicit awareness. It impacts different aspects of our lives, from personalized recommendations to crucial determinations like hiring decisions or credit approvals. Yet, even to their developers, AI algorithms’ opacity raises concerns about fairness. The biases inherent in our data further complicate matters, as current AI systems often lack moral or logical judgment, relying solely on predictive outputs derived from learned data patterns. Efforts to address fairness in AI models face significant challenges, as different definitions of fairness can lead to conflicting outcomes. Despite attempts to mitigate biases and optimize fairness criteria, achieving a universal and satisfactory solution remains elusive. The multidimensional nature of fairness, with its roots in philosophy and evolving concepts in organizational justice, underscores the complexity of the task. Technology is inherently political, shaped by various societal factors and human biases. Recognizing this, stakeholders must engage in nuanced discussions about the types of fairness relevant in specific contexts and the potential trade-offs involved. Just as in other spheres of decision-making, navigating trade-offs is inevitable, requiring a flexible approach informed by diverse perspectives.

This study acknowledges that achieving fairness in AI is not about prescribing a singular definition or solution but adapting to evolving needs and values. Embracing ambiguity and tension in decision-making can lead to more inclusive outcomes. An interdisciplinary examination of application-specific and consensus-driven frameworks is adopted to consider fairness in AI. By evaluating factors such as application nuances, procedural frameworks, and stakeholder dynamics, this study demonstrates the framework’s expansive potential applicability in understanding and operationalizing fairness by the way of two illustrations.

This article explores the human rights standards relevant to ensuring human involvement requirements in EU legislation related to automated content moderation. The opinions given by different experts and human rights bodies emphasise the human rights relevance of the way in which platforms distribute automated and human moderators in their services. EU secondary legislation establishes basic requirements for these structures that are called to be read under a human rights perspective. This article examines the justifications given for incorporating human involvement in content moderation, the different types of human involvement in content moderation, and the specific requirements for such involvement under EU secondary law. Additionally, it analyses the human rights principles concerning procedural safeguards for freedom of expression within this legal framework.

Amid the rapidly evolving landscape of artificial intelligence (AI) regulation, a significant concern has emerged regarding the predominant focus on preemptive measures aimed at preventing or mitigating potential AI-related harms. While these preemptive measures are undeniably important, they must be complemented by effective redress mechanisms to address and remedy materialized harms. This paper highlights the crucial role of redress in empowering individuals to challenge and rectify the adverse effects of AI systems, emphasizing that access to redress is, in fact, access to justice. We critically evaluate whether current AI governance sufficiently address the need for remedies for AI-related harms, arguing that they fall short in protecting individuals’ rights. To address this gap, we outline four key steps in the redress process: (1) initiating the redress process, (2) determining appropriate avenues for redress, (3) collecting evidence to support claims and (4) receiving and responding to decisions. Each step is explored in detail, presenting distinct challenges and requirements, illustrated with real-world examples. Our findings underscore the urgent need to integrate robust redress pathways into AI governance frameworks to safeguard individual rights as AI technologies become increasingly embedded in society.

As judges increasingly rely on algorithmic and AI systems in their decision-making process, concerns have been raised about their impact on the judicial duty to state reasons – a duty that is essential to the right to a fair trial and the rule of law. This paper examines whether existing legal frameworks adequately ensure this duty and its underlying normative goals in the age of automation and explores how best to safeguard this duty. Central to the analysis is the question of whether the duty to state reasons itself should be redefined and impose stricter reasoning requirements on judges using AI, requiring them to articulate both ‘pragmatic’ and technical explanations of the systems’ role in their decisions. While this approach may enhance the duty’s normative goals of legitimacy, transparency and accountability, the paper also considers the potential drawbacks of imposing such a more robust reasoning obligation in AI-assisted judicial processes.

An emerging field of AI, namely Fair Machine Learning (ML), aims to quantify different types of bias (also known as unfairness) exhibited in the predictions of ML algorithms, and to design new algorithms to mitigate them. Often, the definitions of bias used in the literature are observational, i.e. they use the input and output of a pre-trained algorithm to quantify a bias under concern. In reality, these definitions are often conflicting in nature and can only be deployed if either the ground truth is known or only in retrospect after deploying the algorithm. Thus, there is a gap between what we want Fair ML to achieve and what it does in a dynamic social environment. Hence, we propose an alternative dynamic mechanism, “Fair Game”, to assure fairness in the predictions of an ML algorithm and to adapt its predictions as the society interacts with the algorithm over time. “Fair Game” puts together an Auditor and a Debiasing algorithm in a loop around an ML algorithm. The “Fair Game” puts these two components in a loop by leveraging Reinforcement Learning (RL). RL algorithms interact with an environment to take decisions, which yields new observations (also known as data/feedback) from the environment and in turn, adapts future decisions. RL is already used in algorithms with pre-fixed long-term fairness goals. “Fair Game” provides a unique framework where the fairness goals can be adapted over time by only modifying the auditor and the different biases it quantifies. Thus, “Fair Game” aims to simulate the evolution of ethical and legal frameworks in the society by creating an auditor which sends feedback to a debiasing algorithm deployed around an ML system. This allows us to develop a flexible and adaptive-over-time framework to build Fair ML systems pre- and post-deployment.

Fairness in service robotics is a complex and multidimensional concept shaped by legal, social and technical considerations. As service robots increasingly operate in personal and professional domains, questions of fairness – ranging from legal certainty and anti-discrimination to user protection and algorithmic transparency – require systematic and interdisciplinary engagement. This paper develops a working definition of fairness tailored to the domain of service robotics based on a doctrinal analysis of how fairness is understood across different fields. It identifies four key dimensions essential to fair service robotics: (i) furthering legal certainty, (ii) preventing bias and discrimination, (iii) protecting users from exploitation and (iv) ensuring transparency and accountability. The paper explores how developers, policymakers and researchers can contribute to these goals. While fairness may resist universal definition, articulating its core components offers a foundation for guiding more equitable and trustworthy human–robot interactions.

In this article, we investigate the relationship between human oversight and fairness within the evolving framework of EU AI regulatory governance. We address two core research questions: (1) How are human oversight and fairness related? and (2) To what extent does the AI Act establish a framework for human oversight that effectively supports the implementation of the various dimensions of fairness? Based on a review of interdisciplinary literature, the article identifies three normative claims linking human oversight to fairness: first, that human oversight can help mitigate bias and error in AI systems; second, that it can function as a mechanism of accountability by assigning oversight to natural persons where AI systems lack legal liability; and third, that it can introduce human empathy and contextual sensitivity into decision-making processes, enabling a substantive notion of fairness that takes into account individual circumstances. A critical analysis of the AI Act reveals that while these normative aspirations are acknowledged, the Act only partially operationalises them, leaving several aspects of fairness insufficiently supported.

This paper begins with a brief technical explanation of generative AI and how only a small subset of entities – the landlords of creativity – have access to the computational resources and expertise needed to create foundation models that enable audio deepfakes. It then examines how regulatory regimes in America, the European Union (EU), and China address the misuse of generative AI. Although each framework seeks to regulate generative AI in different ways, the paper argues that none effectively assigns liability to the landlords of creativity. Finally, the paper proposes holding these landlords responsible for their renters’ malicious usage. This proposal not only is technically feasible but also is conceptually aligned with established legal doctrines in the American, EU, and Chinese frameworks.