A Beautiful Mind: Principles and Strategies for AI-Augmented Human Reasoning

• URL: http://arxiv.org/abs/2503.15530v2
• Date: Fri, 11 Apr 2025 03:41:00 GMT
• Title: A Beautiful Mind: Principles and Strategies for AI-Augmented Human Reasoning
• Authors: Sean Koon,
• Abstract summary: This paper outlines a human-centered augmented reasoning paradigm.<n>It offers examples of interaction modes that can serve as bridges between human reasoning and AI algorithms.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Amidst the race to create more intelligent machines there is a risk that we will rely on AI in ways that reduce our own agency as humans. To reduce this risk, we could aim to create tools that prioritize and enhance the human role in human-AI interactions. This paper outlines a human-centered augmented reasoning paradigm by 1. Articulating fundamental principles for augmented reasoning tools, emphasizing their ergonomic, pre-conclusive, directable, exploratory, enhancing, and integrated nature; 2. Proposing a 'many tasks, many tools' approach to ensuring human influence and control, and 3. Offering examples of interaction modes that can serve as bridges between human reasoning and AI algorithms.

Related papers

• Creating 'Full-Stack' Hybrid Reasoning Systems that Prioritize and Enhance Human Intelligence [0.0]
  The paper proposes the development of generative AI-based tools that enhance the human ability to reflect upon a problem. A high-level model is also described for integrating AI and human capabilities in a way that centralizes human participation and control.
  arXiv  Detail & Related papers  (2025-04-18T05:38:21Z)
• Aligning Generalisation Between Humans and Machines [74.120848518198]
  Recent advances in AI have resulted in technology that can support humans in scientific discovery and decision support but may also disrupt democracies and target individuals. The responsible use of AI increasingly shows the need for human-AI teaming. A crucial yet often overlooked aspect of these interactions is the different ways in which humans and machines generalise.
  arXiv  Detail & Related papers  (2024-11-23T18:36:07Z)
• How Performance Pressure Influences AI-Assisted Decision Making [57.53469908423318]
  We show how pressure and explainable AI (XAI) techniques interact with AI advice-taking behavior.<n>Our results show complex interaction effects, with different combinations of pressure and XAI techniques either improving or worsening AI advice taking behavior.
  arXiv  Detail & Related papers  (2024-10-21T22:39:52Z)
• Combining Theory of Mind and Kindness for Self-Supervised Human-AI Alignment [0.0]
  Current AI models prioritize task optimization over safety, leading to risks of unintended harm. We propose a novel human-inspired approach which aims to address these various concerns and help align competing objectives.
  arXiv  Detail & Related papers  (2024-10-21T22:04:44Z)
• Towards Bidirectional Human-AI Alignment: A Systematic Review for Clarifications, Framework, and Future Directions [101.67121669727354]
  Recent advancements in AI have highlighted the importance of guiding AI systems towards the intended goals, ethical principles, and values of individuals and groups, a concept broadly recognized as alignment. The lack of clarified definitions and scopes of human-AI alignment poses a significant obstacle, hampering collaborative efforts across research domains to achieve this alignment. We introduce a systematic review of over 400 papers published between 2019 and January 2024, spanning multiple domains such as Human-Computer Interaction (HCI), Natural Language Processing (NLP), Machine Learning (ML)
  arXiv  Detail & Related papers  (2024-06-13T16:03:25Z)
• Human-AI Safety: A Descendant of Generative AI and Control Systems Safety [6.100304850888953]
  We argue that meaningful safety assurances for advanced AI technologies require reasoning about how the feedback loop formed by AI outputs and human behavior may drive the interaction towards different outcomes. We propose a concrete technical roadmap towards next-generation human-centered AI safety.
  arXiv  Detail & Related papers  (2024-05-16T03:52:00Z)
• Beyond Prompts: Learning from Human Communication for Enhanced AI Intent Alignment [30.93897332124916]
  We study human strategies for intent specification in human-human communication. This study aims to advance toward a human-centered AI system by bringing together human communication strategies for the design of AI systems.
  arXiv  Detail & Related papers  (2024-05-09T11:10:29Z)
• 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)
• On some Foundational Aspects of Human-Centered Artificial Intelligence [52.03866242565846]
  There is no clear definition of what is meant by Human Centered Artificial Intelligence. This paper introduces the term HCAI agent to refer to any physical or software computational agent equipped with AI components. We see the notion of HCAI agent, together with its components and functions, as a way to bridge the technical and non-technical discussions on human-centered AI.
  arXiv  Detail & Related papers  (2021-12-29T09:58:59Z)
• Trustworthy AI: A Computational Perspective [54.80482955088197]
  We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
  arXiv  Detail & Related papers  (2021-07-12T14:21:46Z)
• Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
  In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
  arXiv  Detail & Related papers  (2021-06-25T22:31:55Z)

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.