Modeling Human Beliefs about AI Behavior for Scalable Oversight

• URL: http://arxiv.org/abs/2502.21262v1
• Date: Fri, 28 Feb 2025 17:39:55 GMT
• Title: Modeling Human Beliefs about AI Behavior for Scalable Oversight
• Authors: Leon Lang, Patrick Forré,
• Abstract summary: As AI systems grow more capable, human feedback becomes increasingly unreliable.<n>This raises the problem of scalable oversight: How can we supervise AI systems that exceed human capabilities?<n>We propose to model the human evaluator's beliefs about the AI system's behavior to better interpret the human's feedback.
• Score: 15.535954576226207
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Contemporary work in AI alignment often relies on human feedback to teach AI systems human preferences and values. Yet as AI systems grow more capable, human feedback becomes increasingly unreliable. This raises the problem of scalable oversight: How can we supervise AI systems that exceed human capabilities? In this work, we propose to model the human evaluator's beliefs about the AI system's behavior to better interpret the human's feedback. We formalize human belief models and theoretically analyze their role in inferring human values. We then characterize the remaining ambiguity in this inference and conditions for which the ambiguity disappears. To mitigate reliance on exact belief models, we then introduce the relaxation of human belief model covering. Finally, we propose using foundation models to construct covering belief models, providing a new potential approach to scalable oversight.

Related papers

• Measurement of LLM's Philosophies of Human Nature [113.47929131143766]
  We design the standardized psychological scale specifically targeting large language models (LLM) We show that current LLMs exhibit a systemic lack of trust in humans. We propose a mental loop learning framework, which enables LLM to continuously optimize its value system.
  arXiv  Detail & Related papers  (2025-04-03T06:22:19Z)
• Modelling Human Values for AI Reasoning [2.320648715016106]
  We detail a formal model of human values for their explicit computational representation. We show how this model can provide the foundational apparatus for AI-based reasoning over values. We propose a roadmap for future integrated, and interdisciplinary, research into human values in AI.
  arXiv  Detail & Related papers  (2024-02-09T12:08:49Z)
• Exploration with Principles for Diverse AI Supervision [88.61687950039662]
  Training large transformers using next-token prediction has given rise to groundbreaking advancements in AI. While this generative AI approach has produced impressive results, it heavily leans on human supervision. This strong reliance on human oversight poses a significant hurdle to the advancement of AI innovation. We propose a novel paradigm termed Exploratory AI (EAI) aimed at autonomously generating high-quality training data.
  arXiv  Detail & Related papers  (2023-10-13T07:03:39Z)
• 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)
• Fairness in AI and Its Long-Term Implications on Society [68.8204255655161]
  We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time. We discuss how biased models can lead to more negative real-world outcomes for certain groups. If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest.
  arXiv  Detail & Related papers  (2023-04-16T11:22:59Z)
• When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment [96.77970239683475]
  AI systems need to be able to understand, interpret and predict human moral judgments and decisions. A central challenge for AI safety is capturing the flexibility of the human moral mind. We present a novel challenge set consisting of rule-breaking question answering.
  arXiv  Detail & Related papers  (2022-10-04T09:04:27Z)
• Best-Response Bayesian Reinforcement Learning with Bayes-adaptive POMDPs for Centaurs [22.52332536886295]
  We present a novel formulation of the interaction between the human and the AI as a sequential game. We show that in this case the AI's problem of helping bounded-rational humans make better decisions reduces to a Bayes-adaptive POMDP. We discuss ways in which the machine can learn to improve upon its own limitations as well with the help of the human.
  arXiv  Detail & Related papers  (2022-04-03T21:00:51Z)
• The Response Shift Paradigm to Quantify Human Trust in AI Recommendations [6.652641137999891]
  Explainability, interpretability and how much they affect human trust in AI systems are ultimately problems of human cognition as much as machine learning. We developed and validated a general purpose Human-AI interaction paradigm which quantifies the impact of AI recommendations on human decisions. Our proof-of-principle paradigm allows one to quantitatively compare the rapidly growing set of XAI/IAI approaches in terms of their effect on the end-user.
  arXiv  Detail & Related papers  (2022-02-16T22:02:09Z)
• Uncalibrated Models Can Improve Human-AI Collaboration [10.106324182884068]
  We show that presenting AI models as more confident than they actually are can improve human-AI performance. We first learn a model for how humans incorporate AI advice using data from thousands of human interactions.
  arXiv  Detail & Related papers  (2022-02-12T04:51:00Z)
• 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)
• 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)

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.