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Artificial Intelligence & Human Rights Law: A Thematic Synthesis Review

Abstract The interaction between Artificial Intelligence (AI) technologies and Human Rights Law (HRL) is gaining increasing attention in the literature. Scholars analyse both positive and negative interactions, with risks and harms bearing a stronger emphasis. Amid growing contributions to the field, a methodical synthesis of the literature is missing. This article fills this gap. We begin by mapping the interaction between AI and HRL, focusing on the types of AI applications, the impacted human rights, the relevant jurisdictions, and the involved actors. Then, we develop an interpretative (re)construction of the impact of AI on HRL and discuss how this impact affects the HRL mechanisms. Our mapping analysis reveals common themes that dominate academic discussions, including accountability, fair process, information provision and protection, and effective enforcement. In the final part, we explore the scope and nature of the directions of future AI & HRL literature based on this review’s identification of gaps, blind spots and newly emerging or persistent ambiguities within the literature. In that respect, four key areas emerge as priorities for consideration: the role of private actors, the use of HR impact assessment, the adoption of relational theories, and the potential redefinition of the concept of harm, including through refocusing on vulnerability as a legal concept. Addressing these areas is crucial to strengthening the framework for mitigating AI-related risks to human rights. Similar content being viewed by others 1 Introduction The growing use and application of Artificial Intelligence (AI) technologies has led to multifaceted legal and broader societal impact, with important human right implications. The interaction between AI and human rights law (HRL) encompasses both a wide scope and profound complexity (Quintavalla & Temperman, 2023). Scholars focus on various aspects of this interaction. Among many examples, privacy and data protection issues have been analysed extensively, particularly due to AI applications’ demand for data and personal information (Kosta, 2022; van der Sloot, 2018). Similarly, the right to fair trial has also been examined largely in reference to the impact of AI (Reiling, 2020). Other scholars adopt a more comprehensive approach, analysing commonalities between the legal and human rights challenges posed by AI technologies (Rodrigues, 2020). The scholarship that analyses the interaction between AI and HRL is growing rapidly. However, a comprehensive overview of this type of scholarship that would discuss its main themes and key findings is currently missing. This article fills this gap by using the ‘thematic synthesis’ methodology (Grant & Booth, 2009; Thomas & Harden, 2008). Through this method, we identify a purposive sample of contributions to the literature and review it in order to gain knowledge about the key factors that shape the interaction between AI and HRL. The findings from our review contribute to giving structure and clarity to this growing literature, which is valuable to future researchers who wish to delve deeper into one or more of these themes, and to policymakers who may be called to regulate the impact of AI on HRL. Two clarifications are important before proceeding further. First, this article treats human rights mainly from a legal perspective. The legal perspective, however, is supported by philosophical, and sociological considerations insofar as they relate directly to the legal discussion at hand. Second, we have not limited our review only to academic literature on international HRL but have included HRL in various jurisdictional levels. The article is structured as follows. In section two, we elaborate on our methodological approach. Section three maps the key elements of the interaction between AI and HRL that are commonly identified in the reviewed literature. In section four, we develop the themes that account for the impact of AI on HRL. Section five concludes the article and presents some recommendations. 2 Methodology We developed this article based on the ‘thematic synthesis review’ methodology (Grant & Booth, 2009; Thomas & Harden, 2008). The thematic synthesis is a form of systematic literature review initially used in healthcare settings but now widespread in social sciences, including legal and regulatory research (Akbar et al., 2024; Prifti et al., 2024; Willig & Rogers, 2017). Due to its thematic nature, it is particularly apt for connecting qualitative findings and analyses that seem disparate and context-specific (Thomas & Harden, 2008, pp. 2–3). The intersection of AI and HRL is still in its early stages, with many open questions arising from the disruptive impact of AI. Conducting a thematic analysis is helpful at this stage to begin mapping the main themes and the diversity of solutions proposed in the literature. Case law in this area is developing slowly, and theoretical works dominate the literature. As our goal is not to assess developments in legal practice directly, but rather to understand the responses and perspectives of scholars in the field of HRL, we believe the thematic synthesis methodology offers the right toolkit for our objective. The review is conducted in two steps. First, we identify the key elements that shape the interaction between AI and HRL. Second, we integrate and synthesise these elements to discuss common themes in the reviewed literature of AI and human rights. The review aims to answer the research question: What are the key elements and themes, in the literature, that shape the interaction between AI and HRL? To answer this question as comprehensively as possible, we conducted a search of the literature in two databases, namely Scopus and Web of Science, using the following search terms: ‘(“human rights” OR “fundamental rights”) AND (“artificial intelligence” OR ai OR “automated decision-making” OR adm) AND law*’. Given the thematic synthesis approach adopted, we chose to focus on Scopus and Web of Science to ensure a coherent and reproducible corpus of indexed, peer-reviewed literature, rather than aiming for exhaustive coverage of all legal databases. Considering that the aim of this review is to identify and synthesise overarching themes rather than to provide an exhaustive doctrinal survey, we consider that this choice does not compromise the validity of the conclusions, although it may limit the inclusion of more jurisdiction-specific discussions. The search resulted in an initial sample of 391 publications (332 in Scopus and 59 in Web of Science), limited to English language peer-reviewed articles in the Social Sciences field. We reviewed all results up until July 2025, without specifying a lower temporal bound. After excluding duplicates, inaccessible articles, conference papers, and articles that were not relevant to our study (e.g., those that used the terms ‘human rights’ and ‘fundamental rights’ not in the context of HRL), we read and reviewed a total of 128 articles (see Fig. 1). This sample may not include all potentially relevant articles, which is because the thematic synthesis review requires working with a sample that is sufficiently representative and not necessarily exhaustive (Thomas & Harden, 2008, p. 3). 3 AI and HRL: Mapping the Interaction In this section, we present a descriptive review of the key elements that shape the interaction between AI and HRL, namely: the types of AI applications, the human right(s) impacted by AI, and the level of jurisdiction involved (3.1), as well as the actors who, negatively and positively, experience the impact of AI and the actors who are the addressees of the recommendations from the reviewed literature (3.2). 3.1 Types of AI, Human Rights Impacted and the Jurisdictional Level AI is a contested concept and scholars often do not have the same technologies in mind when discussing the interactions between AI and HRL (Koniakou, 2023; Stahl et al., 2022). Our first step was to identify and group the types of AI applications analysed in the literature. We identify 23 types of AI applications and group them in four categories according to their level of specificity, as shown in Table 1. Scholars focusing on AI in general (36 papers), either do not specify any AI application or mention several applications occasionally as illustrations. Some scholars explore semi-specified types (11 papers), focusing on a specific aspect or function that is shared among a wide range of applications and affords a variety of possible uses. In other cases, scholars specify the field of application (38 papers). At the most granular level, scholars refer to identifiable AI applications (28 papers) such as facial recognition and content moderation. Next, we identified the main human rights impacted by the technology discussed (Fig. 1). Most papers explore human rights in general (53 papers), looking at a variety of human rights, followed by a focus on the right to privacy (20 papers), the prohibition of discrimination (15 papers) and the right to a fair trial (10). The interaction between AI and HRL depends largely on the level of specificity of the type of AI discussed. Under the heading AI in general, the disagreement on the meaning of the term AI is mirrored in the findings. Authors use the concept interchangeably with terms like ‘data-driven technologies’ (Malgieri & Niklas, 2020), ‘machine learning technologies’ (Harutyunyan & Yeremyan, 2020) ‘algorithms’ (Casini, 2023), ‘algorithmic systems’ (Lu, 2022), or allude to a variety of underlying techniques and general functions such as data processing and automated decision-making (Wulf & Seizov, 2022). Whereas some scholars explicate their understanding of AI (Nagy, 2024), others leave its definition to be inferred from examples offered (Shaelou & Razmetaeva, 2024). The ambiguity found in the meaning of term, however, calls for cautiousness when drawing broad lessons on the interaction of ‘AI in general’ and HRL. For example, a relatively wide variety of human rights is discussed as being impacted by AI, ranging from freedom of thought (Teo, 2024) to non-discrimination (Martinez-Ramil, 2021). Scholars focusing on semi-specified types identify human rights impact stemming from these specific characteristics: those examining automated decision-making may focus, for example, on the right to explanation (Selbst & Powles, 2017), while algorithmic profiling is associated with non-discrimination law (Xenidis, 2020). For authors examining several types of technologies employed in a particular field, the human rights impacted are associated with the field rather than the type of technology: AI in judicial systems (8 papers) is mostly associated with the right to a fair trial (Reiling, 2020), AI in selection and hiring procedures (4 papers) with non-discrimination rights (Carter, 2024) and AI in surveillance (6 papers) with the right to privacy (Carter, 2025). At the most specific level, AI applications tend to refer to specific human rights impact. Content moderation technologies (5 papers) are generally associated with negative impact for the freedom of expression and positive impact for non-discrimination rights (Hatano, 2023). Facial recognition technologies (7 papers) are associated with negative impact on individual privacy and positive impact on societal safety (Raposo, 2024). Ultimately, the type of application, the design, the field in which it is applied and its use in practice are all relevant to its potential positive or negative impact on specific human rights. Moreover, the articles in the reviewed sample contextualised their analysis on the human rights violations to a certain jurisdictional level, as displayed in Table 2. 3.2 The Relevant Actors We examined the actors that were identified in the literature as being either positively impacted by AI, meaning that AI enhanced human rights enjoyment and protection, or negatively impacted, meaning that AI posed a threat to effective human rights exercise and protection (Fig. 2). Here, various degrees of specificity can be distinguished, while most papers take a general approach. Scholars examined impact on individuals, on society as a whole, on marginalised communities (referring to socially, economically or politically disadvantaged or otherwise vulnerable or minority groups and people), on various characteristics of actors explicitly protected under non-discrimination law (focusing, for example, on women or people with disabilities), or on actors acting in specific capacities (e.g., as data subjects or as subject in the justice system). A large majority of the papers identify actors that are negatively impacted by AI (112 papers) while less than half of the papers identify positively impacted actors (49 papers). The difference is starker for marginalised communities: while roughly one-third of the papers identified negative impact on marginalised communities, none identified any positive impact. Society in general is identified mostly as benefitting from AI. However, these benefits are balanced against negative impact on other actors. For example, for AI applications deployed by public authorities, such as facial recognition technologies, societal benefits come at a cost to individuals and marginalised communities. Such technologies may enhance the government’s ability to effectively and efficiently protect rights of citizens through increased safety and security, while negatively impacting human rights of individuals through privacy interference and chilling effects of increased surveillance (Gabrielli, 2025). Next, we looked at the actors that were addressed in the recommendations and solutions proposed in the reviewed literature to safeguard human rights in light of the threats posed by AI (Fig. 3). Recommendations focused mainly on public actors holding legislative power (55 papers) as scholars highlight the need for revision of the existing HRL framework in the age of AI (Teo, 2025) (Fig. 4). 4 The Impact of AI on HRL This section aims to develop the overarching themes that capture the impact of AI on HRL. To facilitate the analysis, we distinguish between the functions of HRL, which is legal protection, and the mechanisms that enable the functions of HRL. The first part of this section covers the impact of AI on HRL protection, exploring whether and how AI technologies increase or reduce the amount of protection that HRL aims to offer. The second part of this section focuses on the impact of AI on the HRL doctrinal and practical mechanisms. 4.1 AI and HRL Protection AI impacts HRL protection by reinforcing existing processes or by enabling novel opportunities. Both forms of impact are subject to the double-charged thesis (Floridi, 2023), which posits that technologies, like AI, may have positive or negative effects, but they are never neutral. This understanding relies on long-standing arguments on the politics of technology, particularly about how technology reinforces and augments existing political and sociological problems (Arthur, 2009; Kranzberg, 1986). We adopt this conceptual and normative starting point to analytically distinguish reinforcing positive/negative effects of AI on HRL. In addition to that, philosophical approaches to technology remind us that the eventual impacts of technologies occur by virtue of them mediating our relationship with reality (Ihde, 2012; Verbeek, 2010) and with other social actors (Floridi, 2013). By so doing, at least in certain cases, they trigger a re-engineering of reality and consequently our perception of it (Dreyfus & Spinosa, 2006; Heidegger, 2013; Vallor, 2022, Chapter 16), thereby providing novel access to the external world. Our thematic analysis of the literature reveals a similar difference, that is, between reinforcing and novel impacts of AI technologies in relation to HRL. In certain cases, AI seems to make existing processes more efficient, i.e., faster, easier, and requiring less effort, or, on the negative dimension, exacerbate existing legal and societal problems. That may entail, for instance, amplifying existing biases, or turning them more opaque. We may understand this impact as reinforcing existing processes, whether positively or negatively. However, the literature also recognises the capability of AI technologies to pose novel impacts, exhibiting a sort of re-engineering of reality (Vallor, 2022, Chapter 16). Novel impacts, unlike the reinforcing kind, pose eventualities that would have otherwise not existed without, and are not merely augmented by, technology. For instance, analysing millions of dormant medical images to dissect patterns that can signal disease is not within the current range of human capabilities or other existing technologies. Neither is the chance to conduct non-personal, large-scale mass surveillance. In the following text, we distinguish between reinforcing (positive/negative) and novel (positive/negative) effects of AI on HRL because these types of impact have different regulatory implications, as we also discuss below. We structure the analysis in this subsection based on these distinctions, starting with AI’s positive effects. 4.1.1 Reinforcing Positive Impact AI applications can reinforce existing HRL functions by enhancing the effectiveness of human rights related processes. This effect, referred to as the ‘reinforcing positive impact’, can be observed across several critical domains. In the realm of justice, AI plays a pivotal role in improving access to legal resources and facilitating more equitable legal outcomes. For example, AI-powered tools enable efficient legal research, providing lawyers and judges with access to relevant case law and legal precedents (Collenette et al., 2023; Re & Solow-Niederman, 2019). This capability can help streamline the legal process, making justice more accessible and reducing the likelihood of prolonged legal disputes, which tend to disadvantage marginalised communities. In healthcare, AI supports decision-making processes that lead to better patient outcomes and overall improvements in care. For instance, AI can enhance patient care by optimising treatment plans based on comprehensive analyses of medical records, thereby ensuring that patients receive the most effective treatments tailored to their specific conditions (van Kolfschooten & Shachar, 2023). Furthermore, AI-driven diagnostic tools can reduce human error by providing a second opinion on complex cases, thus improving diagnostic accuracy and patient safety (Sariyar & Schlünder, 2019). AI applications not only streamline healthcare delivery but also enhance the quality of care, reinforcing the right to health by making medical services more reliable and effective (Evans, 2023). Similarly, in the sphere of democracy, AI can enhance citizen participation by supporting the organisation and analysis of public opinions (Casini, 2023). AI tools can manage large volumes of data from public consultations, enabling governments to better understand and respond to the needs and preferences of their citizens. This, in turn, strengthens democratic processes by fostering more inclusive and responsive governance, reinforcing the right to political participation (Palmiotto & González, 2023). Finally, AI technologies can monitor environmental changes, predict natural disasters, and optimise resource usage, thereby aiding efforts to protect the environment (Victorio et al., 2024). The enhancement of environmental protection by AI applications has thus a twofold benefit from a human rights perspective. First, it contributes to the protection of the right to a healthy environment, which has recently been recognised in international HRL and in some regional jurisdictions. Second, it can reinforce those human rights that are associated with greater environmental protection (Boyd & UN. Human Rights Council. Special Rapporteur, 2019). Overall, AI's reinforcing positive impact lies in its ability to make existing human rights protection processes more effective, thereby strengthening the realisation of this protection across various human rights. 4.1.2 Novel Positive Impact AI introduces ‘novel positive impacts’ on the functions of HRL, creating opportunities that might not have been possible without this technology. This impact emerges as AI enables the recognition and utilisation of data patterns at complexities beyond human capability, enhancing various domains of HRL. The literature offers several examples in this regard. In healthcare, AI's capacity to process vast amounts of information enables the detection of patterns that are imperceptible to humans. For instance, AI algorithms can analyse millions of medical images to identify early signs of diseases such as cancer, which may be undetectable by radiologists (Hosny et al., 2018). This ability to recognise subtle indicators of illness can lead to earlier interventions, potentially saving lives and significantly improving patient outcomes (Sariyar & Schlünder, 2019; Shaheen, 2021). This represents a novel form of protection for the right to health, as it extends the boundaries of what is possible in medical diagnostics and treatment. In the realm of justice, AI offers unprecedented predictive capabilities by analysing vast datasets of past legal decisions. For example, AI can predict the outcome of a trial by statistically modelling previous decisions made by the judge. This can provide valuable insights for legal professionals, helping them to better prepare their cases and anticipate potential outcomes (Collenette et al., 2023; Queudot & Meurs, 2018). The implications of such technologies are still being debated, as they tend to undermine human judgement and risk replicating existing biases (Hildebrandt, 2019a). However, their potential to inform and enhance legal strategies introduces a new dimension to the right to a fair trial. In environmental protection, AI is enabling new approaches to conservation. By processing satellite imagery and sensor data, AI can detect illegal deforestation activities in real-time, allowing for swift interventions (Hasan et al., 2024). This capability not only helps protect the environment as such, but also supports the rights of indigenous communities whose livelihoods are tied to these ecosystems. 4.1.3 Reinforcing Negative Impact AI can exacerbate existing problems, a phenomenon we refer to as ‘reinforcing negative impact’. In this context, AI technologies entrench, perpetuate, and magnify underlying issues such as discrimination, employment inequalities, and threats to democratic processes. Discrimination is one of the main areas where the reinforcing negative impact of AI is observed. AI systems, particularly those used in decision-making processes, can perpetuate and amplify biases present in the data on which they are trained. For instance, in criminal justice, predictive policing algorithms have been shown to disproportionately target minority communities, leading to biased policing practices (Blount, 2021; Kaplina et al., 2023). Similarly, in financial services, AI-driven credit scoring systems may disadvantage certain demographic groups based on biased data, reinforcing systemic discrimination in access to credit (Addy et al., 2024). These examples demonstrate how AI can worsen existing inequalities by embedding and legitimising biased outcomes. In employment, AI's use in automated decision-making for recruitment and hiring processes has also raised concerns about reinforcing negative impacts (Carter, 2024; Mihaljević et al., 2023) Algorithms designed to streamline candidate selection may inadvertently perpetuate biases present in historical hiring data. For example, if a company's previous hiring practices favoured a particular gender, ethnicity, or educational background, AI systems trained on this data are likely to replicate these patterns, thus excluding qualified candidates from underrepresented groups (Fritts & Cabrera, 2021). This situation, which exacerbates existing disparities in employment opportunities, would violate the right to be protected from discrimination. AI's role in reinforcing negative impacts is also evident with regards to democratic processes, impacting the freedom of expression and freedom of assembly and association. Authoritarian regimes have adopted AI technologies to enhance censorship and surveillance, curtailing freedoms and stifling dissent (Koniakou, 2023). AI-driven monitoring tools can be used to track and suppress political opposition, limit access to information, and manipulate public opinion through disinformation campaigns (Bacalu, 2022; Molnar, 2019). 4.1.4 Novel Negative Impact AI causes ‘novel negative impact’, by which we refer to new categories of harm emerging from the AI’s capacity to operate autonomously, at scale, and with profound societal impact. These novel negative impacts manifest in various contexts. In what follows we focus on mass surveillance, indirect discrimination, and the use of deepfakes and Generative AI. Mass surveillance exemplifies this novel negative impact. Unlike traditional surveillance, which focused on tracking specific individuals, AI-driven mass surveillance processes information from entire populations. This non-personal, large-scale analysis allows for the identification of patterns, behaviours, and trends across groups, without regard for individual identity (Kosta, 2022). AI can analyse social media activity, public camera feeds, and other data sources to predict group behaviours, detect dissent, or influence public sentiment. This capability enables governments or corporations to monitor and manipulate entire populations, posing risks to privacy, freedom of expression, and democratic processes (van der Sloot, 2016). The sheer scale and non-personal nature of AI-powered mass surveillance represent a new level of intrusion, where entire communities can be subtly controlled or suppressed. Another novel negative impact is the emergence of indirect or multiple forms of discrimination. AI systems can combine various data points—such as browsing history, geographic location, or purchase behaviour—to make decisions that inadvertently discriminate against certain groups. For example, an AI system used for loan approvals might deny applications from residents of specific neighbourhoods due to statistical correlations with higher default rates, even if individual applicants have strong credit histories (Martinez-Ramil, 2021; Zuiderveen Borgesius, 2020). This type of discrimination is unique to AI because it arises from complex data interactions that are difficult to detect and address. It represents a shift from overt discrimination to more subtle, systemic biases that can disproportionately affect marginalised communities (Krupiy, 2021; Zuiderveen Borgesius, 2020). Deepfakes and Generative AI pose novel challenges to human rights by distorting truth, privacy, and security in unprecedented ways. Deepfakes create realistic but false content that can be used for blackmail, discrediting public figures, and spreading misinformation, infringing on rights to human dignity, expression, and fair elections (Lyu, 2024; Verma, 2024). Similarly, generative AI can produce synthetic content that blurs the line between reality and fabrication, contributing to fake news, online harassment, and privacy violations. These technologies enable large-scale manipulation and deception, undermining trust and infringing on the right to access accurate information (Floridi, 2024). 4.2 AI and HRL Mechanisms HRL mechanisms are the legal doctrinal and practical (implementation and enforcement) ways to ensure well-functioning HRL frameworks. They enable and support the functions of HRL. The reviewed literature discusses the impact of AI in HRL mechanisms extensively. We identify 4 main themes, namely accountability, fair process, information provision and protection, and effective enforcement. We will analyse each theme individually. 4.2.1 Accountability The involvement of AI introduces significant challenges in determining the agents that should be held accountable when harm occurs. This complexity has sparked intense ethical and philosophical debates. At the heart of this discussion lies the question of whether AI systems, as they become increasingly agentic, can be regarded as possessing a form of autonomy that warrants their recognition as moral agents (Floridi & Sanders, 2004; Gunkel, 2012; Wallach & Allen, 2008). Such recognition depends on whether these systems exercised genuine causal control over their actions or consequences and whether they knew—or should have known—their actions were wrong or would lead to morally unacceptable outcomes (Hart, 2008). Opinions on whether an artificial agent should be ascribed moral responsibility remain divided. Some contend that a form of agency alone—regardless of consciousness—is sufficient to confer moral status (Kammerer, 2022; Semler, 2024). Others take a more cautious stance, emphasising that mere goal-directed behaviour is inadequate for moral agency without the presence of intentionality (Bernáth, 2021; Sebastián, 2021). Meanwhile, another perspective warns that assigning moral responsibility to AI risks eroding human accountability (Bryson, 2010). While this academic debate remains unsettled (Veluwenkamp & Hindriks, 2024), the multidimensional nature of artificial agency is highlighted, reflecting different layers of autonomy and decision-making (Dung, 2025). This perspective underscores the importance of distinguishing between operational autonomy and the moral cognition involved in deliberately intended actions. It reveals how decisions by artificial agents can give rise to an accountability (or responsibility) gap: when an artificial agent’s action warrants blame, it becomes difficult to identify who bears the corresponding moral and legal responsibilities (Matthias, 2004). This accountability gap, widely debated in ethics and philosophy, also gives rise to two significant legal challenges. First, it undermines individuals’ ability to seek redress for human rights violations (Kosta, 2022; Lane, 2022). The core issue lies in the limited foreseeability of AI actions, which threatens to break the chain of causation—a fundamental legal principle that establishes liability and, consequently, access to redress (Karnow, 1996; van Kolfschooten & Shachar, 2023). The issue is further compounded by the unclear allocation of the burden of proof when AI systems cause harm, particularly when states withhold information about the AI system involved (Rachovitsa & Johann, 2022). Second, the innovative nature of AI technologies tends to disrupt the notion of the ‘duty of care’, which refers to a set of clearly defined obligations that, in case the defendant has not fulfilled, enable the victim to claim redress for human rights violations. Consider the case of (semi)autonomous vehicles. If the duty of care obligations for the ‘driver’ of the vehicle are not yet clearly outlined, this complicates the allocation of responsibilities in the event of an accident (Kochupillai et al., 2020). In response to these legal challenges, some scholars have proposed granting legal personhood to artificial agents (Teubner, 2018; Tzimas, 2020). The rationale is that recognising AI systems as legal persons might help close the accountability gap. However, such proposal may encounter resistance grounded in both philosophical concerns and practical legal considerations. From a philosophical standpoint, granting legal personhood implies not only assigning obligations to AI but also recognising duties owed to AI itself. In other words, it would compel a thorough examination of the nature and scope of any rights that AI systems might possess (Gunkel, 2012; Müller, 2021; Schwitzgebel & Garza, 2015). From a practical legal perspective, precedents exist where accountability has been established through indirect agency, without the necessity of granting full legal personality (Heine & Quintavalla, 2024; Pagallo, 2010). Such examples suggest that granting full legal personhood to AI systems may not be necessary to ensure proper accountability. Lastly, the unique nature of human rights law presents an additional challenge in the context of accountability: since AI technologies are typically developed by private actors, questions arise about the extent to which human rights law can be applied effectively (Lane, 2023). International HRL in particular is often seen as outdated because accountability for human rights violations largely revolves around state responsibility (Razmetaeva et al., 2022). While concepts of corporate social responsibility, accountability mechanisms under domestic law, and due diligence obligations for both states and non-state actors may remedy some of this legal deficit, the limited direct accountability of companies under international law provides an important legal background to many AI and HRL scholarly investigations (Hatano, 2023; McGregor et al., 2019). 4.2.2 Information Provision and Protection AI introduces several challenges to information-related mechanisms in HRL. These mechanisms serve either information protection functions, e.g., ensuring that personal or confidential information is not accessible, or information provision functions, e.g., providing users with sufficient information to enable their agency and ability to protect their rights. Information-related mechanisms are usually mentioned in reference to data protection, but their relevance spans a variety of human rights. For instance, ensuring the anonymity of protesters (i.e., information protection) is important for the freedom of assembly (Dokmanović & Cvetićanin, 2023; Gabrielli, 2025). The examined literature, however, focuses extensively on the personal data component of information-related mechanisms, with transparency and user consent being central to the discussion. The impact of AI systems complicates these processes due to inherent issues with transparency and explainability (Felzmann et al., 2020; Grochowski et al., 2021). One of the primary challenges stems from the aforementioned ‘black box’ problem, where the decision-making processes of AI systems are often opaque and difficult to understand, even for experts (Hacker & Neyer, 2023; Mazur, 2021; Selbst & Powles, 2017). Opacity in AI systems can have various reasons. For one, opacity can be created strategically for positive outcomes. Reasons for this might be the protection of intellectual property rights, to secure a system, or to protect the data that the system uses (Burrell, 2016; Carabantes, 2020; de Laat, 2018; Innerarity, 2021). Opacity can thus help to protect human rights. However, opacity can also be implemented strategically for more nefarious reasons, such as hiding illegal processes (Carabantes, 2020; Pasquale, 2015). Strategically creating opacity is thus not always justified, and can even interfere with human rights. Another reason for why AI systems become opaque is their technical complexity. Since specific technical knowledge is required to understand an AI system, it is difficult to gain an understanding of how an AI systems works in its entirety (Burrell, 2016; Carabantes, 2020; de Laat, 2018; Durán & Jongsma, 2021; Innerarity, 2021; Pasquale, 2015). Such opacity can stem from a lack of technical skills, but also from the limited epistemic understanding that humans can have of the workings of a technology (Durán & Jongsma, 2021; Humphreys, 2009; Koskinen, 2024). This is particularly the case concerning AI systems that generate their own knowledge, as these systems can create opacity by itself (Innerarity, 2021). Opacity can thus lead to problems related to human rights in society. Particularly in the context of information provision and protection, opacity can make it difficult to fulfil the transparency requirements mandated by data protection laws, such as the GDPR (Ferretti et al., 2018). For instance, when AI processes personal data, the underlying logic of the system may be too complex or obscure to be easily explained to users (Grochowski et al., 2021; Wolf, 2020). This creates a hurdle in providing clear and understandable information that is necessary for users to be fully informed about how their data is being used. The transparency and explainability issues have a direct impact on the concept of consent, which is a cornerstone of data protection mechanisms. Consent is meant to empower individuals to make informed decisions about their personal data. However, when the information provided is insufficient, unclear, or overly complex, it becomes difficult for users to give meaningful consent (Kayaalp, 2018; van Kolfschooten & Shachar, 2023). In some cases, the sheer volume of information can overwhelm users, further hindering their ability to make informed choices (Prifti et al., 2023). Scholars emphasise the critical role of human rights impact assessment (IA) in addressing the challenges AI poses to information-related mechanisms. IA is an essential tool for anticipating, preventing, and mitigating the potential harms associated with AI. Additionally, it provides a framework for taking corrective measures to address any damage that AI systems may cause (Landman, 2020). By incorporating impact assessments into the development and deployment of AI, it is possible to integrate a wide range of considerations, including fundamental rights (Hacker & Neyer, 2023). Moreover, impact assessments can introduce and address emerging concepts such as vulnerability, ensuring a more comprehensive approach to safeguarding against AI's negative impacts (Malgieri & Niklas, 2020). 4.2.3 Access to Justice and Fair Trial AI systems pose a range of doctrinal challenges that impact the access to justice and fair trial under HRL. The issues combine difficulties with legal standing, access to court, the right to effective remedies, the equality of arms, and the presumption of innocence. Access to court, legal standing, and the right to effective remedies are hindered by admissibility obstacles. Activities like mass surveillance and big data processing through AI systems do not focus on the actions or the personal data of one individual. Instead, their main operative locus is relational data, which produces information not on a given individual but on a group of people (Kosta, 2022). As a result, the harm cannot always be traced to an individual. This, from a legal perspective, presents an admissibility obstacle because HRL relies on the existence of individual harm and the fulfilment of the ‘victim requirement’ (van der Sloot, 2016). This legal, doctrinal tension has produced an increasingly growing body of scholarship that aims to reconceptualise the notion of individual rights, to account for the relational and collective nature of AI and big data processing, including mass surveillance (Helm, 2016). We may discern two important strands which, although both aim for a shift away from ‘individualistic’ conceptions of rights, they do so in theoretically distinct ways. One develops a collective dimension of rights; the other construct a concept of group rights (Vecellio Segate, 2022). Their differences are worth unpacking. The idea of collective rights emerged as a response to the difficulties faced by HRL to adequately incorporate public goods, such as environmental protection, under its operational logic (Jones, 1999; Réaume, 1988). Facing admissibility issues, scholars contended that harm to public goods exceeded ‘individual’ harm—and consequently individual rights—therefore justifying the need for a collective approach to rights, at least insofar as they pertained to public goods (Hartney, 1991). Theories on collective rights develop a relational notion of harm but restrain from expanding this relationality to rights. Although they recognise that, for example, environmental harm has a collective, rather than individual impact, collective rights are still understood as a simple bundling of individual rights (Vecellio Segate, 2022). In other words, collective rights are no bigger than the sum of individual rights that are included in it. The distinction between collective and group rights lies precisely in this aspect. Group rights, a concept gaining increasing legal and philosophical traction in the recent decade, stems from a similar dissatisfaction as that underlying collective rights: the individualistic nature of rights cannot capture all forms of human rights harm (Taylor et al., 2017). However, these theories go a step further and argue that groups can be understood as a new ontological category in human rights, as both subjects of harm and holders of rights. The underlying metaphysical inspiration may be attributed to Gestelt, the idea that a group is more than the sum of its parts (Vecellio Segate, 2022). As a result, these strands advance a new level of analysis for rights protection. Group rights are largely absorbed by group privacy theories (Helm, 2016; Mittelstadt, 2017; Puri, 2020; Taylor et al., 2017), which may be partly due to the fact that AI and big data processing are significantly more disruptive to the right to privacy, as our literature synthesis in section III also confirms. Thus far, the literature on group privacy is occupied with constructing the theoretical and conceptual ground for the notion of group privacy (Helm, 2016; Vecellio Segate, 2022), focusing less on concrete regulatory governance mechanisms that would implement this concept into HRL and beyond. As a result, the concept of group rights remains a conceptual artifact with limited doctrinal impact. From a jurisprudential perspective, in some mass surveillance cases, the European Court of Human Rights has been willing to accept in abstracto claims (Case of Klass & others v. Germany, 1978; Case of Roman Zakharov v. Russia, 2015), thereby relaxing the ‘victim requirement’ criterion. However, this judicial perspective can be more accurately understood as a jurisprudential bypass, applicable and relevant in particular cases, then an application of collective or group rights. Fair trial rights like the equality of arms and the presumption of innocence are impacted by transparency problems that exist with machine learning AI systems, a problem that we highlighted in the previous subsection in the context of information provision. The lack of transparency in decision-making processes affects the equality of arms in the right to a fair trial, as it prevents claimants from effectively confronting and challenging the evidence against them (Blount, 2021; Sachoulidou, 2023). Without access to the underlying logic and data used by AI, defendants are disadvantaged and undermined in their ability to mount a comprehensive defence (Carter, 2024; Rudolf & Kovač, 2024). The use of inexplicable AI systems in judicial decision-making processes may also prevent subjects from meaningfully contesting judicial decisions (Bayamlioglu, 2018; Hendrickx, 2025). If it is unclear to litigants how they were profiled or which arguments, facts, or cases affected a decision, they cannot evaluate and contest whether the decision relied on legally correct and justifiable grounds (Hoven, 2021; Metikos & Domselaar, 2025). To safeguard the right to a fair trial, scholars have argued for explainability requirements of AI used in justice systems (Metikos, 2024). In addition, the use of AI systems in law enforcement poses a threat to the presumption of innocence, a cornerstone of fair HRL processes. This principle is meant to protect individuals not only before a charge is made but also after an acquittal or the expiration of the statute of limitations for a particular crime. However, techniques like predictive policing, which rely on AI to forecast criminal behaviour, often utilise data from individuals who have been arrested, even if they were not charged or were later acquitted. Predictive policing tools would suggest that these individuals are likely to engage in future harmful behaviour, effectively treating them as guilty despite a lack of formal charges or convictions (Blount, 2021; Kaplina et al., 2023). The effects of these practices tend to disproportionately harm marginalised and vulnerable groups of individuals, leading to an unequal protection (Vermeulen & Bellanova, 2012). The interaction between AI and fair trial rights relates to theories of procedural justice Dworkinian theories of procedural justice take an instrumental view on procedural rights for safeguarding the accuracy of a decision (Dworkin, 1985). The right to contest a decision enables the revision of inaccurate decisions, and equality of arms has value because imbalances in power may obstruct the pursuit of accuracy. The right to fair trial then only imposes a requirement of explainability if doing so benefits the overall accuracy of the decision (Metikos & Domselaar, 2025). In contrast, dignitarian theories hold that procedural rights have non-instrumental value (Waldron, 2011). The right to contestation is inherently valuable, as it enables legal subjects to meaningfully participate in the legal procedures that affect their life, granting them agency (Kaminski & Urban, 2021). In dignitarian theories, the inscrutability of the decision-making process may cause harm regardless of the accuracy of the decision, as it obstructs the ability of subjects to participate in the justice system as autonomous and responsible agents (Rebera et al., 2025). AI systems may constitute ‘hermeneutic injustice’ as they deprive disadvantages parties from the interpretive tools needed to comprehend their experiences in the justice systems (Fricker, 2017; Hoven, 2021). Opaque AI systems may then be incompatible with the right to a fair trial. The conception of legal subjects as autonomous and responsible agents has been criticised in relational theories of procedural justice for ignoring the relational capabilities and needs of differently situated subjects (Meyerson et al., 2021). Dignity is constituted through (institutionalised) interactions within social and political contexts, and the fairness, accuracy and intelligibility of a decision should be understood in relation to the unique contextual position of the person involved in the decision. The right to a fair trial requires that parties are heard and cared for by the authorities that hold power over them (Meyerson et al., 2021). The use of AI systems may impact the right to a fair trial negatively if they contribute to de facto imbalances of power, marginalisation or oppression (Naudts, 2024). As such, AI systems are incompatible with procedural fairness if the use of these systems prevents institutions from recognising and accounting for the unique positionality, capabilities, and needs of people affected by their decision, in reducing them to datapoints, for example (Dao, 2020; Hildebrandt, 2019b). 4.2.4 Effective Enforcement AI systems present substantial challenges to the effective enforcement of existing HRL. One of the primary issues is the sheer scale of AI activity possibly impacting human rights, and this impact is anticipated to grow exponentially. This vast expansion makes it increasingly difficult for public institutions to monitor and oversee potential AI-related violations of human rights. This challenge is not new; similar difficulties have been observed in the context of data processing activities that have been conducted without the use of AI. However, with AI, the problem is magnified due to the technology’s complexity and widespread use (Lazcoz & de Hert, 2023). Additionally, regulatory gaps appear because AI is a relatively new and rapidly evolving field that tests the limits of current legal frameworks. These gaps in regulation complicate enforcement efforts, particularly concerning accountability and redress, which we have previously discussed as critical issues. For example, not all AI decisions are subject to a right to explanation, leaving individuals without recourse to understand or challenge decisions that may affect their rights (Wachter et al., 2017). This lack of clarity undermines the enforcement of existing HRL and creates uncertainty for both regulators and the public. Lastly, scholars question the adequacy of the framing of concepts in current HRL frameworks. For example, as AI enables platforms to modulate users’ thoughts in new way, this necessitates a re-conceptualisation of the freedom of thought and the role of manipulation therein (Teo, 2024). Furthermore, AI may introduce new types of harm that fall beyond the scope of the IHRL framework. AI in surveillance may exert chilling effects on individuals’ ability to develop their identity, highlighting the inadequacy of current HRL frameworks in protecting free identity development (Rautenberg & Murray, 2024). 5 AI and HRL: Proposals for Future Developments In this concluding section, we aim to outline some recommendations for scholars and policymakers, informed by the gaps and blind spots identified in our thematic analysis. While Sect. 3 and 4 mapped the main interactions between AI and HRL and analysed reinforcing and novel impacts—both positive and negative—on human rights, our analysis also reveals that four recurring core issues dominate academic discussions: accountability, information provision and protection, fair process and effective enforcement. While these issues are deeply intertwined with the legal challenges, they do not constitute an exhaustive framework for understanding the full impact of AI's impact on human rights. For example, existing scholarship has already highlighted that AI poses challenges to virtually all first, second and third generation rights (Quintavalla & Temperman, 2023). Yet, scholarship on substantive rights issues beyond the flagged four does clearly not compare. This gap underscores the need for further exploration into how AI intersects with a broader range of rights. Relatedly, more investigation of other types of correlations and dynamics is necessary. Take positive effects on human rights, for instance. In the preceding sections we have observed the overwhelming focus on negative effects, understandable for a newly emerging body of scholarship catching up with developments that may be harmful from a HRL perspective. Positive effects are reported very concretely only in such areas as health rights (patient rights), among others, in our review. On the one hand, this finding neatly resonates with a recent comprehensive and comparative work that sought to chart AI’s negative and positive effects on all (or most) first, second and third generation rights (Quintavalla & Temperman, 2023). There, it has been noted that negative effects are reported essentially across the board, i.e. under most if not all human rights, whereas positive effects are distinctively more frequently and more comprehensively reported under the headings of specific economic, social or cultural rights (e.g. housing, food, health, among others). On the other hand, the current review covers human rights standards that have been codified as part of HRL indicatively, and not exhaustively, which makes it difficult to move beyond informed hypotheses in this respect. As such, further research is needed to move beyond informed hypotheses and provide a more complete understanding of AI’s broader impact. Beyond this broader and more cross-cutting recommendation, translating our findings into actionable academic and policy recommendations is critical. Four key areas emerge as priorities for consideration: the role of private actors, the use of HR impact assessment, the adoption of relational theories, and the potential redefinition of the concept of harm. The first two are rooted in the thematic analyses on accountability, and information provision and protection, while the latter two are tied to fair process and effective enforcement. Addressing these areas would strengthen the framework for mitigating AI-related risks to human rights. To begin with, the thematic analysis reveals a robust discourse on determining accountability when harm arises in the context of AI. While discussions have spanned a range of considerations—from assigning moral responsibility to exploring the concept of legal personhood for AI systems—a recurring theme in this debate is the dominant role played by private actors in AI development and deployment. At its core, the issue stems from the lack of direct accountability of these private entities under international (human rights) law. This gap not only complicates efforts to address harms but also risks shifting the burden of responsibility away from those best positioned to mitigate risks. Given this context, it is critical to re-focus scholarly and policy attention on identifying practical mechanisms to hold private actors accountable more effectively—especially given the field’s disproportionate focus on recommendations for public actors alone. A recommendation commonly proposed is that HRL (and, in particular, international HRL) requires a re-orientation for a system that would react more promptly to human rights violations caused by AI (Lane, 2023). Such a re-orientation could involve introducing new legal instruments or establishing dedicated oversight bodies to address AI-related human rights impacts. While the first approach faces significant hurdles due to the inherent limitations of traditional HRL, the latter is emerging as a more viable solution. For example, the establishment of the European AI Office aims to enforce the EU AI Act by monitoring compliance with human rights safeguards embedded in the regulation (Lütz, 2024). Specifically, it conducts audits and investigations, which may include penalties for non-compliance, to ensure corporations adhere to the (human rights) requirements of the EU AI Act. In doing so, the establishment of oversights bodies under regional or domestic laws may bridge the gap between traditional human rights law and the unconventional challenges posed by AI, ensuring that human rights are protected even when violations stem from private-sector behaviour rather than state action. Another critical step in addressing AI’s negative impacts by corporations would be the mandatory establishment of a human rights IA for the entire AI life cycle. This regulatory suggestion, closely tied to the involvement of oversight bodies outlined earlier, would shift the focus from reactive responses to proactive measures. It would also address the second key thematic challenge on AI’s impact on information-related mechanisms in HRL. The IA would be a sensible instrument to help address the informational gap raised in the development and deployment of AI for taking corrective measures to address any damage that AI systems may cause. While human rights IA would not necessarily address the issue of explainability of AI, it can signal the threat of possible human rights violations. The prompt identification of these violations would help developers and users of AI applications, most of which are corporations, to prevent and mitigate these risks. Yet, discussions and applications of IA require deeper focus on charting the AI’s interplay with more specific types of human rights (going beyond the usual suspects of privacy, free speech, fair trial) as well as the relationship between different human rights affected by AI. The reason lies in the tendency of current literature to focus on an individual or a limited number of human rights, thereby overlooking a more relational approach to affected human rights. The benefits of this more expansive view are twofold. First, this approach would enrich the knowledge about what is the actual extent of the impact of AI on human rights. Second, it would provide a better compass to those who are called to develop a more comprehensive human rights IA for AI applications. Finally, there is a need for further investigation into how traditional international HRL can evolve to address the novel challenges posed by AI. The thematic analyses on access to justice and fair trial, and effective enforcement point to two key areas where academic literature must focus: first, the need to explore the adoption of a relational perspective on AI and human rights, and second, the potential redefinition of the concept of harm itself. These shifts are essential for ensuring that human rights frameworks remain responsive to the complexities introduced by AI. A relational perspective on AI and human rights moves beyond merely examining the impact of AI on individual rights. It also calls for a deeper exploration of how AI affects individuals in varied and interconnected ways—whether as autonomous agents and as members of communities. The thematic analyses on access to justice and fair trial reveals how contemporary HRL requirements such as the admissibility criterion often make it difficult to address the challenges introduced by AI. These limitations highlight the need for adopting a more relational lens, one that acknowledges the collective and interconnected nature of AI’s impact on human rights. For these reasons, scholars have increasingly questioned whether traditional frameworks rooted in individualistic paradigms and static notions of harm can adequately capture the multidimensional and often systemic harms introduced by AI (Taylor et al., 2017; Vecellio Segate, 2022). This has sparked discussions about the feasibility of adopting a relational notion of harm or rights, one that fundamentally challenges the atomised view of the individual as the sole unit of rights-bearing. A relational approach would acknowledge that harm is not experienced in isolation but is often cumulative, thereby pushing for collective rights or group rights. Moving forward, scholars must focus their attention on developing relational theories that can serve as both a critique of existing frameworks and a blueprint for reform. This would most likely integrate philosophical debates into legal discussions about responsibilities. The concept of harm, as traditionally understood in HRL, often proves too rigid to address the nuanced and sometimes indirect negative impacts caused by AI technology effectively. This rigidity makes it difficult to render these harms justiciable within existing HRL frameworks. 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International Journal of Human Rights, 24(10), 1572–1593. https://doi.org/10.1080/13642987.2020.1743976 Acknowledgements This work is funded by the research initiative on ‘Rebalancing of Public Interests in Private Relationships’, the sector plan for law funding of the Dutch Ministry of Education, Culture and Research. This work is also supported by the Sectorplan SSH-Breed ‘The Influence of Digitalization on Work, Prosperity and Entrepreneurship’ and the Convergence Center for Responsible AI in Healthcare. Author information Authors and Affiliations Corresponding author Ethics declarations Conflict of interest The authors declare no conflict of interest. Additional information Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Rights and permissions Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. About this article Cite this article Prifti, K., Hoppenbrouwers, J., de Leeuw, E. et al. Artificial Intelligence & Human Rights Law: A Thematic Synthesis Review. Minds & Machines 36, 40 (2026). https://doi.org/10.1007/s11023-026-09793-w Received: Accepted: Published: Version of record: DOI: https://doi.org/10.1007/s11023-026-09793-w

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