Reflective Hybrid Intelligence for Meaningful Human Control in Decision-Support Systems

• URL: http://arxiv.org/abs/2307.06159v1
• Date: Wed, 12 Jul 2023 13:32:24 GMT
• Title: Reflective Hybrid Intelligence for Meaningful Human Control in Decision-Support Systems
• Authors: Catholijn M. Jonker, Luciano Cavalcante Siebert and Pradeep K. Murukannaiah
• Abstract summary: We introduce the notion of self-reflective AI systems for meaningful human control over AI systems. We propose a framework that integrates knowledge from psychology and philosophy with formal reasoning methods and machine learning approaches. We argue that self-reflective AI systems can lead to self-reflective hybrid systems (human + AI)
• Score: 4.1454448964078585
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the growing capabilities and pervasiveness of AI systems, societies must collectively choose between reduced human autonomy, endangered democracies and limited human rights, and AI that is aligned to human and social values, nurturing collaboration, resilience, knowledge and ethical behaviour. In this chapter, we introduce the notion of self-reflective AI systems for meaningful human control over AI systems. Focusing on decision support systems, we propose a framework that integrates knowledge from psychology and philosophy with formal reasoning methods and machine learning approaches to create AI systems responsive to human values and social norms. We also propose a possible research approach to design and develop self-reflective capability in AI systems. Finally, we argue that self-reflective AI systems can lead to self-reflective hybrid systems (human + AI), thus increasing meaningful human control and empowering human moral reasoning by providing comprehensible information and insights on possible human moral blind spots.

Related papers

• Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
  We argue that shortcomings stem from one overarching failure: AI systems lack wisdom. While AI research has focused on task-level strategies, metacognition is underdeveloped in AI systems. We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety.
  arXiv  Detail & Related papers  (2024-11-04T18:10:10Z)
• 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)
• Advancing Explainable AI Toward Human-Like Intelligence: Forging the Path to Artificial Brain [0.7770029179741429]
  The intersection of Artificial Intelligence (AI) and neuroscience in Explainable AI (XAI) is pivotal for enhancing transparency and interpretability in complex decision-making processes. This paper explores the evolution of XAI methodologies, ranging from feature-based to human-centric approaches. The challenges in achieving explainability in generative models, ensuring responsible AI practices, and addressing ethical implications are discussed.
  arXiv  Detail & Related papers  (2024-02-07T14:09:11Z)
• Managing extreme AI risks amid rapid progress [171.05448842016125]
  We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems. There is a lack of consensus about how exactly such risks arise, and how to manage them. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
  arXiv  Detail & Related papers  (2023-10-26T17:59:06Z)
• Intent-aligned AI systems deplete human agency: the need for agency foundations research in AI safety [2.3572498744567127]
  We argue that alignment to human intent is insufficient for safe AI systems. We argue that preservation of long-term agency of humans may be a more robust standard.
  arXiv  Detail & Related papers  (2023-05-30T17:14:01Z)
• World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges [51.92834011423463]
  We focus on the two concepts of world models and predictive coding. In neuroscience, predictive coding proposes that the brain continuously predicts its inputs and adapts to model its own dynamics and control behavior in its environment.
  arXiv  Detail & Related papers  (2023-01-14T06:38:14Z)
• BIASeD: Bringing Irrationality into Automated System Design [12.754146668390828]
  We claim that the future of human-machine collaboration will entail the development of AI systems that model, understand and possibly replicate human cognitive biases. We categorize existing cognitive biases from the perspective of AI systems, identify three broad areas of interest and outline research directions for the design of AI systems that have a better understanding of our own biases.
  arXiv  Detail & Related papers  (2022-10-01T02:52:38Z)
• Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
  Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
  arXiv  Detail & Related papers  (2022-01-26T18:53:09Z)
• Meaningful human control over AI systems: beyond talking the talk [8.351027101823705]
  We identify four properties which AI-based systems must have to be under meaningful human control. First, a system in which humans and AI algorithms interact should have an explicitly defined domain of morally loaded situations. Second, humans and AI agents within the system should have appropriate and mutually compatible representations. Third, responsibility attributed to a human should be commensurate with that human's ability and authority to control the system.
  arXiv  Detail & Related papers  (2021-11-25T11:05:37Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)
• Perspectives and Ethics of the Autonomous Artificial Thinking Systems [0.0]
  Our model uses four hierarchies: the hierarchy of information systems, the cognitive hierarchy, the linguistic hierarchy and the digital informative hierarchy. The question of the capability of autonomous system to provide a form of artificial thought arises with the ethical consequences on the social life and the perspective of transhumanism.
  arXiv  Detail & Related papers  (2020-01-13T14:23:21Z)

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.