AI Alignment through Reinforcement Learning from Human Feedback? Contradictions and Limitations
- URL: http://arxiv.org/abs/2406.18346v1
- Date: Wed, 26 Jun 2024 13:42:13 GMT
- Title: AI Alignment through Reinforcement Learning from Human Feedback? Contradictions and Limitations
- Authors: Adam Dahlgren Lindström, Leila Methnani, Lea Krause, Petter Ericson, Íñigo Martínez de Rituerto de Troya, Dimitri Coelho Mollo, Roel Dobbe,
- Abstract summary: We show the shortcomings of the broadly pursued alignment goals of honesty, harmlessness, and helpfulness.
We highlight tensions and contradictions inherent in the goals of RLxF.
We conclude by urging researchers and practitioners alike to critically assess the sociotechnical ramifications of RLxF.
- Score: 0.2106667480549292
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: This paper critically evaluates the attempts to align Artificial Intelligence (AI) systems, especially Large Language Models (LLMs), with human values and intentions through Reinforcement Learning from Feedback (RLxF) methods, involving either human feedback (RLHF) or AI feedback (RLAIF). Specifically, we show the shortcomings of the broadly pursued alignment goals of honesty, harmlessness, and helpfulness. Through a multidisciplinary sociotechnical critique, we examine both the theoretical underpinnings and practical implementations of RLxF techniques, revealing significant limitations in their approach to capturing the complexities of human ethics and contributing to AI safety. We highlight tensions and contradictions inherent in the goals of RLxF. In addition, we discuss ethically-relevant issues that tend to be neglected in discussions about alignment and RLxF, among which the trade-offs between user-friendliness and deception, flexibility and interpretability, and system safety. We conclude by urging researchers and practitioners alike to critically assess the sociotechnical ramifications of RLxF, advocating for a more nuanced and reflective approach to its application in AI development.
Related papers
- 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 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) - RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs [49.386699863989335]
Training large language models (LLMs) to serve as effective assistants for humans requires careful consideration.
A promising approach is reinforcement learning from human feedback (RLHF), which leverages human feedback to update the model in accordance with human preferences.
In this paper, we analyze RLHF through the lens of reinforcement learning principles to develop an understanding of its fundamentals.
arXiv Detail & Related papers (2024-04-12T15:54:15Z) - Quantifying AI Vulnerabilities: A Synthesis of Complexity, Dynamical Systems, and Game Theory [0.0]
We propose a novel approach that introduces three metrics: System Complexity Index (SCI), Lyapunov Exponent for AI Stability (LEAIS), and Nash Equilibrium Robustness (NER)
SCI quantifies the inherent complexity of an AI system, LEAIS captures its stability and sensitivity to perturbations, and NER evaluates its strategic robustness against adversarial manipulation.
arXiv Detail & Related papers (2024-04-07T07:05:59Z) - Towards Human-AI Deliberation: Design and Evaluation of LLM-Empowered Deliberative AI for AI-Assisted Decision-Making [47.33241893184721]
In AI-assisted decision-making, humans often passively review AI's suggestion and decide whether to accept or reject it as a whole.
We propose Human-AI Deliberation, a novel framework to promote human reflection and discussion on conflicting human-AI opinions in decision-making.
Based on theories in human deliberation, this framework engages humans and AI in dimension-level opinion elicitation, deliberative discussion, and decision updates.
arXiv Detail & Related papers (2024-03-25T14:34:06Z) - Methodological reflections for AI alignment research using human
feedback [0.0]
AI alignment aims to investigate whether AI technologies align with human interests and values and function in a safe and ethical manner.
LLMs have the potential to exhibit unintended behavior due to their ability to learn and adapt in ways that are difficult to predict.
arXiv Detail & Related papers (2022-12-22T14:27:33Z) - Achieving a Data-driven Risk Assessment Methodology for Ethical AI [3.523208537466128]
We show that a multidisciplinary research approach is the foundation of a pragmatic definition of ethical and societal risks faced by organizations using AI.
We propose a novel data-driven risk assessment methodology, entitled DRESS-eAI.
arXiv Detail & Related papers (2021-11-29T12:55:33Z) - Counterfactual Explanations as Interventions in Latent Space [62.997667081978825]
Counterfactual explanations aim to provide to end users a set of features that need to be changed in order to achieve a desired outcome.
Current approaches rarely take into account the feasibility of actions needed to achieve the proposed explanations.
We present Counterfactual Explanations as Interventions in Latent Space (CEILS), a methodology to generate counterfactual explanations.
arXiv Detail & Related papers (2021-06-14T20:48:48Z) - 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) - Transdisciplinary AI Observatory -- Retrospective Analyses and
Future-Oriented Contradistinctions [22.968817032490996]
This paper motivates the need for an inherently transdisciplinary AI observatory approach.
Building on these AI observatory tools, we present near-term transdisciplinary guidelines for AI safety.
arXiv Detail & Related papers (2020-11-26T16:01:49Z)
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.