Accountability Framework for Healthcare AI Systems: Towards Joint Accountability in Decision Making

• URL: http://arxiv.org/abs/2509.03286v1
• Date: Wed, 03 Sep 2025 13:05:29 GMT
• Title: Accountability Framework for Healthcare AI Systems: Towards Joint Accountability in Decision Making
• Authors: Prachi Bagave, Marcus Westberg, Marijn Janssen, Aaron Yi Ding,
• Abstract summary: This paper bridges the gap between the ''what'' and ''how'' of AI accountability, specifically for AI systems in healthcare.<n>We do this by analysing the concept of accountability, formulating an accountability framework, and providing a three-tier structure for handling various accountability mechanisms.
• Score: 1.9774267722954466
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI is transforming the healthcare domain and is increasingly helping practitioners to make health-related decisions. Therefore, accountability becomes a crucial concern for critical AI-driven decisions. Although regulatory bodies, such as the EU commission, provide guidelines, they are highlevel and focus on the ''what'' that should be done and less on the ''how'', creating a knowledge gap for actors. Through an extensive analysis, we found that the term accountability is perceived and dealt with in many different ways, depending on the actor's expertise and domain of work. With increasing concerns about AI accountability issues and the ambiguity around this term, this paper bridges the gap between the ''what'' and ''how'' of AI accountability, specifically for AI systems in healthcare. We do this by analysing the concept of accountability, formulating an accountability framework, and providing a three-tier structure for handling various accountability mechanisms. Our accountability framework positions the regulations of healthcare AI systems and the mechanisms adopted by the actors under a consistent accountability regime. Moreover, the three-tier structure guides the actors of the healthcare AI system to categorise the mechanisms based on their conduct. Through our framework, we advocate that decision-making in healthcare AI holds shared dependencies, where accountability should be dealt with jointly and should foster collaborations. We highlight the role of explainability in instigating communication and information sharing between the actors to further facilitate the collaborative process.

Related papers

• Media and responsible AI governance: a game-theoretic and LLM analysis [61.132523071109354]
  This paper investigates the interplay between AI developers, regulators, users, and the media in fostering trustworthy AI systems.<n>Using evolutionary game theory and large language models (LLMs), we model the strategic interactions among these actors under different regulatory regimes.
  arXiv  Detail & Related papers  (2025-03-12T21:39:38Z)
• AI and the Transformation of Accountability and Discretion in Urban Governance [1.9152655229960793]
  The study synthesizes insights to propose guiding principles for responsible AI integration in decision-making processes.<n>The analysis argues that AI does not simply restrict or enhance discretion but redistributes it across institutional levels.<n>It may simultaneously strengthen managerial oversight, enhance decision-making consistency, and improve operational efficiency.
  arXiv  Detail & Related papers  (2025-02-18T18:11:39Z)
• Causal Responsibility Attribution for Human-AI Collaboration [62.474732677086855]
  This paper presents a causal framework using Structural Causal Models (SCMs) to systematically attribute responsibility in human-AI systems. Two case studies illustrate the framework's adaptability in diverse human-AI collaboration scenarios.
  arXiv  Detail & Related papers  (2024-11-05T17:17:45Z)
• Combining AI Control Systems and Human Decision Support via Robustness and Criticality [53.10194953873209]
  We extend a methodology for adversarial explanations (AE) to state-of-the-art reinforcement learning frameworks. We show that the learned AI control system demonstrates robustness against adversarial tampering. In a training / learning framework, this technology can improve both the AI's decisions and explanations through human interaction.
  arXiv  Detail & Related papers  (2024-07-03T15:38:57Z)
• Towards Responsible AI in Banking: Addressing Bias for Fair Decision-Making [69.44075077934914]
  "Responsible AI" emphasizes the critical nature of addressing biases within the development of a corporate culture. This thesis is structured around three fundamental pillars: understanding bias, mitigating bias, and accounting for bias. In line with open-source principles, we have released Bias On Demand and FairView as accessible Python packages.
  arXiv  Detail & Related papers  (2024-01-13T14:07:09Z)
• Unravelling Responsibility for AI [0.8472029664133528]
  It is widely acknowledged that we need to establish where responsibility lies for the outputs and impacts of AI-enabled systems.<n>This paper presents a conceptual framework of responsibility, accompanied with a graphical notation and general methodology.<n>It unravels the concept of responsibility to clarify that there are different possibilities of who is responsible for AI.
  arXiv  Detail & Related papers  (2023-08-04T13:12:17Z)
• Holding AI to Account: Challenges for the Delivery of Trustworthy AI in Healthcare [8.351355707564153]
  We examine the problem of trustworthy AI and explore what delivering this means in practice. We argue here that this overlooks the important part played by organisational accountability in how people reason about and trust AI in socio-technical settings.
  arXiv  Detail & Related papers  (2022-11-29T18:22:23Z)
• The Conflict Between Explainable and Accountable Decision-Making Algorithms [10.64167691614925]
  Decision-making algorithms are being used in important decisions, such as who should be enrolled in health care programs and be hired. XAI initiative aims to make algorithms explainable to comply with legal requirements, promote trust, and maintain accountability. This paper questions whether and to what extent explainability can help solve the responsibility issues posed by autonomous AI systems.
  arXiv  Detail & Related papers  (2022-05-11T07:19:28Z)
• Transparency, Compliance, And Contestability When Code Is(n't) Law [91.85674537754346]
  Both technical security mechanisms and legal processes serve as mechanisms to deal with misbehaviour according to a set of norms. While they share general similarities, there are also clear differences in how they are defined, act, and the effect they have on subjects. This paper considers the similarities and differences between both types of mechanisms as ways of dealing with misbehaviour.
  arXiv  Detail & Related papers  (2022-05-08T18:03:07Z)
• Accountability in AI: From Principles to Industry-specific Accreditation [4.033641609534416]
  Recent AI-related scandals have shed a spotlight on accountability in AI. This paper draws on literature from public policy and governance to make two contributions.
  arXiv  Detail & Related papers  (2021-10-08T16:37:11Z)
• Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims [59.64274607533249]
  AI developers need to make verifiable claims to which they can be held accountable. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.
  arXiv  Detail & Related papers  (2020-04-15T17:15:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.