RL-EPISTEMIC Responsibility tracks epistemic access

Responsibility/liability allocation depends on what an actor understands or can foresee about the system - interpretability and explanation tools therefore REDISTRIBUTE responsibility (legal analogue of the knowledge condition on moral responsibility) analytical emergent

Co-occurs with
TU-HEALTH ×1 RL-USER ×1 RL-DEV ×1

Node view — 6 coded passages across the corpus

Democratizing value alignment: from authoritarian to democratic AI ethics · Linus Ta-Lun Huang; Gleb Papyshev; James K. Wong · 2024

“This interface will display the high-scoring options to the users, along with explanations regarding some of the most relevant moral principles supporting or undermining them [...] Importantly, this process contributes to the necessary 'knowledge conditions' of responsibility for their decisions.”
why coded: Explanations satisfy the knowledge condition of responsibility - responsibility-aware design, explicit · unit #5, pp. 16

Misalignment or misuse? The AGI alignment tradeoff · Max Hellrigel-Holderbaum; Leonard Dung · 2025

“A crucial factor is the ease of misusing a given alignment technique. [...] This depends on access to the model [...] The form of deployment that is most vulnerable to catastrophic misuse is making model weights generally accessible since it gives all interested people the most comprehensive form of access to a model, thus thwarting most options for implementing effective obstacles to misuse.”
why coded: Misuse risk tracks ACCESS level (inference/API/weights) - responsibility follows access, cf. Kästner's epistemic condition · unit #8, pp. 12

Responsible Black Boxes: How Virtue Ethics Can Bridge the Responsibility Gap in AI (Palgr… · Hasse J. Hällström; Steven S. Gouveia · 2026

“By highlighting moral dispositions that guide engineering organisations, VE ensures that accountability does not hinge on model transparency. Instead, conscientious engineering organisations demonstrate honesty, responsibility, and courage to address emerging harm, correct biases, and openly acknowledge uncertainties. [...] XAI is not necessary for responsibility, provided that engineers act virtuously.”
why coded: Direct rival to the epistemic-access condition: responsibility WITHOUT interpretability · unit #2, pp. 336

Responsibility Attribution for AI-Mediated Damages with Mechanistic Interpretability · Lena Kästner; Johann Cordes; Herbert Zech · 2026

“provided that the system's components work as anticipated, liability for damages attributable to type (ii) difference-makers may actually shift from system providers to deployers; at least insofar as MI gives deployers a certain grasp of how a given AI system works and role specific components play in its overall functioning. [...] if deployers understand the functional organization of a system, they can reasonably infer where and how to use it safely; if they consciously deviate from that use and this incurs damages, they should be held liable even if providers might have specified a different scope of safe use.”
why coded: The central move: MI-conferred understanding shifts liability provider-to-deployer · unit #7, pp. 195

“This suggestion relates to worries about humans in the loop becoming scapegoats [40]: When deployers work with black box AI systems they do not understand, and just (blindly) confirm or follow AI recommendations, they run the risk of becoming mere scapegoats for AI-mediated damages; they are held responsible although they do not have any understanding or control over the damages incurred. With our analysis in mind, scapegoating can be avoided: for if the deployer utilizes an opaque AI system in the ways it is intended to be used, they are not responsible for potential damages [...]; if, by contrast, the system is rendered interpretable, they might be responsible but no longer unknowingly so.”
why coded: Scapegoat avoidance: blind confirmation without understanding defeats responsibility · unit #8, pp. 195

Artificial Intelligence Index Report 2026 · Stanford Institute for Human-Centered AI (AI Inde… · 2026

“[2026 Index headlines: SWE-bench Verified from 60% to near 100% in a year; 88% organizational adoption; Foundation Model Transparency Index average dropped to 40 from 58; 'a widening gap between what AI can do and how prepared we are to manage it'.]”
why coded: Transparency DECLINING as capability rises - the epistemic basis for responsibility is shrinking, measured · unit #1, pp. 1