Johann Laux

As AI systems permeate high-stakes decision-making, the terminology of human involvement—Human-in-the-Loop (HITL), Human-on-the-Loop (HOTL), and Human Oversight—has become vexingly ambiguous. This complicates interdisciplinary collaboration between computer science, law, philosophy, psychology, and sociology and breeds regulatory uncertainty. We propose a clarification grounded in causal structure, focused on runtime involvement. The distinction between HITL and HOTL is best drawn not spatially—in terms of a human’s position “in” or “on” a loop—but causally: HITL is constitutive (a human contribution is necessary for the decision output), while HOTL is corrective (external to the primary causal chain, capable of preventing or modifying outputs). Within HOTL, we distinguish temporal modes—synchronous, asynchronous, and anticipatory—situated in a nested model of provider and deployer runtime. A second, orthogonal dimension captures cognitive integration : whether human and machine form complementary or hybrid intelligence, yielding four distinct configurations. Finally, we separate these descriptive categories from the normative requirements they serve: statutory “Human Oversight” is a normative mode of HOTL demanding not merely a corrective causal position but genuine preparedness and capacity for effective intervention. Because the same person may occupy both roles, this role duality must be treated as a design problem requiring architectural and epistemic mitigation.

Human oversight is widely held to mitigate the risks of AI systems. Even for systems that produce discrete outputs at identifiable decision points, the realisation of human oversight as a reactive measure is empirically fragile, yet increasingly well understood. However, for agentic AI-systems that plan, decompose goals, and execute multi-step actions over extended horizons-reactive oversight reaches its structural limits: intervention on individual actions defeats the autonomy that motivates the deployment, while intervention on aggregate patterns is too coarse for harms whose cumulative consequences only become legible after the fact. This paper argues that reactive oversight must be complemented by an anticipatory mode: oversight exercised before the agent acts, by specifying the normative agenda that structures the space of permissible action and refining it iteratively through specification, runtime, and inspection. The two are complements-the agenda's escalation conditions specify when reactive intervention is invoked. Drawing on Meaningful Human Control, we read anticipatory oversight as the operationalisation of distal-reason tracking. In addition, we argue that the proposed framework yields a specific responsibility architecture by design: occupying the anticipatory mode is the discharge of a role-grounded prospective obligation, 1 and backward-looking responsibility takes the form of strict moral answerabilityrationalistic, relational, and holding regardless of fault, in virtue of the principal's prior opportunity for precaution. We develop bridging failure modes, address objections including moral luck and the illusion of control, and close with regulatory, architectural, and empirical implications

The use of Artificial Intelligence (AI) in high-risk, decision-making scenarios presents technical, safety, and normative challenges; problems that may only be ameliorated by human oversight. However, notions of human oversight lack a common foundational understanding: oversight architectures are not well defined, the roles involved remain unclear, and implementation steps are opaque. Hence, researchers and practitioners struggle to determine how to design, implement, and evaluate systems that enable effective human oversight. This paper advances a practical framework for effective human oversight of AI systems, based on a cross-disciplinary perspective that draws on insights from computer science, human-computer interaction, psychology, philosophy, and law. The core contributions are: (1) a foundational framework, with a working definition, architecture and processes for effective human oversight of AI systems; (2) an initial template for documenting oversight architectures and processes, applied to diverse domains; and (3) a synthesis of open research challenges that need to be considered in the emerging field of effective human oversight of AI systems.

As AI systems increasingly permeate high-stakes decisionmaking, the terminology regarding human involvement-Human-in-the-Loop (HITL), Human-on-the-Loop (HOTL), and Human Oversighthas become vexingly ambiguous. This ambiguity complicates interdisciplinary collaboration between computer science, law, philosophy, psychology, and sociology and can lead to regulatory uncertainty. We propose a clarification grounded in causal structure, focused on human involvement during the runtime of AI systems. The distinction between HITL and HOTL, we argue, is not primarily spatial but causal: HITL is constitutive (a human contribution is necessary for the decision output), while HOTL is corrective (external to the primary causal chain, capable of preventing or modifying outputs). Within HOTL, we distinguish three temporal modes-synchronous, asynchronous, and anticipatorysituated within a nested model of provider and deployer runtime that clarifies their different capacities for intervention. A second, orthogonal dimension captures cognitive integration: whether human and machine operate as complementary or hybrid intelligence, yielding four structurally distinct configurations. Finally, we distinguish these descriptive categories from the normative requirements they serve: statutory "Human Oversight" is a specific normative mode of HOTL that demands not merely a corrective causal position, but genuine preparedness and capacity for effective intervention. Because the same person may occupy both HITL and HOTL roles simultaneously, we argue that this role duality must be treated as a design problem requiring architectural and epistemic mitigation rather than mere acknowledgment.

This paper examines the legal implications of the explicit mentioning of automation bias (AB) in the Artificial Intelligence Act (AIA). The AIA mandates human oversight for high-risk AI systems and requires providers to enable awareness of AB, i.e., the human tendency to over-rely on AI outputs. The paper analyses the embedding of this extra-juridical concept in the AIA, the asymmetric division of responsibility between AI providers and deployers for mitigating AB, and the challenges of legally enforcing this novel awareness requirement. The analysis shows that the AIA’s focus on providers does not adequately address design and context as causes of AB, and questions whether the AIA should directly regulate the risk of AB rather than just mandating awareness. As the AIA’s approach requires a balance between legal mandates and behavioural science, the paper proposes that harmonised standards should reference the state of research on AB and human-AI interaction, holding both providers and deployers accountable. Ultimately, further empirical research on human-AI interaction will be essential for effective safeguards.

The production of artificial intelligence (AI) requires human labour, with tasks ranging from well-paid engineering work to often-outsourced data work. This commentary explores the economic and policy implications of improving working conditions for AI data workers, specifically focusing on the impact of clearer task instructions and increased pay for data annotators. It contrasts rule-based and standard-based approaches to task instructions, revealing evidence-based practices for increasing accuracy in annotation and lowering task difficulty for annotators. AI developers have an economic incentive to invest in these areas as better annotation can lead to higher quality AI systems. The findings have broader implications for AI policy beyond the fairness of labour standards in the AI economy. Testing the design of annotation instructions is crucial for the development of annotation standards as a prerequisite for scientific review and effective human oversight of AI systems in protection of ethical values and fundamental rights.

Scanning Transmission X‐ray microscopy (STXM) is a sensitive and selective probe for the penetration of rapamycin which is topically applied to human skin ex vivo and is facilitated by skin treatment with microneedles puncturing the skin. Inner‐shell excitation serves as a selective probe for detecting rapamycin by changes in optical density as well as linear combination modeling using reference spectra of the most abundant species. The results indicate that mechanical damage induced by microneedles allows this drug to accumulate in the stratum corneum without reaching the viable skin layers. This is unlike intact skin which shows no drug penetration at all and underscores the mechanical impact of microneedle skin treatment. These results are compared to drug penetration profiles of other drugs highlighting the importance of skin barriers. High spatial resolution studies also indicate that the lipophilic drug rapamycin is observed in corneocytes. Attempts in data evaluation are reported to probe rapamycin also in the lipid layers between the corneocytes, which was not accomplished before. These results are compared to recent results on rapamycin uptake in skin where barrier impairment was induced by pre‐treatment with the enzyme trypsin and drug formulations leading to occlusion.

Human oversight has become a key mechanism for the governance of artificial intelligence (“AI”). Human overseers are supposed to increase the accuracy and safety of AI systems, uphold human values, and build trust in the technology. Empirical research suggests, however, that humans are not reliable in fulfilling their oversight tasks. They may be lacking in competence or be harmfully incentivised. This creates a challenge for human oversight to be effective. In addressing this challenge, this article aims to make three contributions. First, it surveys the emerging laws of oversight, most importantly the European Union’s Artificial Intelligence Act (“AIA”). It will be shown that while the AIA is concerned with the competence of human overseers, it does not provide much guidance on how to achieve effective oversight and leaves oversight obligations for AI developers underdefined. Second, this article presents a novel taxonomy of human oversight roles, differentiated along whether human intervention is constitutive to, or corrective of a decision made or supported by an AI. The taxonomy allows to propose suggestions for improving effectiveness tailored to the type of oversight in question. Third, drawing on scholarship within democratic theory, this article formulates six normative principles which institutionalise distrust in human oversight of AI. The institutionalisation of distrust has historically been practised in democratic governance. Applied for the first time to AI governance, the principles anticipate the fallibility of human overseers and seek to mitigate them at the level of institutional design. They aim to directly increase the trustworthiness of human oversight and to indirectly inspire well-placed trust in AI governance.

In its AI Act, the European Union chose to understand trustworthiness of AI in terms of the acceptability of its risks. Based on a narrative systematic literature review on institutional trust and AI in the public sector, this article argues that the EU adopted a simplistic conceptualization of trust and is overselling its regulatory ambition. The paper begins by reconstructing the conflation of “trustworthiness” with “acceptability” in the AI Act. It continues by developing a prescriptive set of variables for reviewing trust research in the context of AI. The paper then uses those variables for a narrative review of prior research on trust and trustworthiness in AI in the public sector. Finally, it relates the findings of the review to the EU's AI policy. Its prospects to successfully engineer citizen's trust are uncertain. There remains a threat of misalignment between levels of actual trust and the trustworthiness of applied AI.