The Value of Gen-AI Conversations: A bottom-up Framework for AI Value Alignment
- URL: http://arxiv.org/abs/2507.21091v1
- Date: Thu, 26 Jun 2025 12:57:07 GMT
- Title: The Value of Gen-AI Conversations: A bottom-up Framework for AI Value Alignment
- Abstract summary: We propose a novel, bottom-up approach to value alignment, utilizing the value of the ISO Value-Based Engineering standard for ethical IT design.<n>We analyse 593 ethically sensitive system outputs identified from 16,908 conversational logs of a major European employment service CA.<n>Results revealed nine core values and 32 different value misalignments that negatively impacted users.
- Score: 42.37986459997699
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Conversational agents (CAs) based on generative artificial intelligence frequently face challenges ensuring ethical interactions that align with human values. Current value alignment efforts largely rely on top-down approaches, such as technical guidelines or legal value principles. However, these methods tend to be disconnected from the specific contexts in which CAs operate, potentially leading to misalignment with users interests. To address this challenge, we propose a novel, bottom-up approach to value alignment, utilizing the value ontology of the ISO Value-Based Engineering standard for ethical IT design. We analyse 593 ethically sensitive system outputs identified from 16,908 conversational logs of a major European employment service CA to identify core values and instances of value misalignment within real-world interactions. The results revealed nine core values and 32 different value misalignments that negatively impacted users. Our findings provide actionable insights for CA providers seeking to address ethical challenges and achieve more context-sensitive value alignment.
Related papers
- Do LLMs Have Values? A Quantitative Analysis and Alignment Framework for Values in Large Language Models [49.45276299939287]
Large Language Models (LLMs) increasingly handle complex subjective tasks.<n>LLMs fluctuate unpredictably under minor wording changes.<n> resolving this duality is critical for reliable AI alignment.
arXiv Detail & Related papers (2026-09-15T03:35:04Z) - Rethinking How AI Embeds and Adapts to Human Values: Challenges and Opportunities [0.6113558800822273]
We argue that AI systems should implement long-term reasoning and remain adaptable to evolving values.<n>Value alignment requires more theories to address the full spectrum of human values.<n>We identify the challenges associated with value alignment and indicate directions for advancing value alignment research.
arXiv Detail & Related papers (2025-08-23T18:19:05Z) - LLM Ethics Benchmark: A Three-Dimensional Assessment System for Evaluating Moral Reasoning in Large Language Models [8.018569128518187]
This study establishes a novel framework for systematically evaluating the moral reasoning capabilities of large language models (LLMs)<n>Our framework addresses this challenge by quantifying alignment with human ethical standards through three dimensions.<n>This approach enables precise identification of ethical strengths and weaknesses in LLMs, facilitating targeted improvements and stronger alignment with societal values.
arXiv Detail & Related papers (2025-05-01T20:36:19Z) - ValueCompass: A Framework for Measuring Contextual Value Alignment Between Human and LLMs [14.621675648356236]
We introduce Value, a framework of fundamental values, grounded in psychological theory and a systematic review.<n>We apply Value to measure the value alignment of humans and large language models (LLMs) across four real-world scenarios.
arXiv Detail & Related papers (2024-09-15T02:13:03Z) - Unpacking the Ethical Value Alignment in Big Models [46.560886177083084]
This paper provides an overview of the risks and challenges associated with big models, surveys existing AI ethics guidelines, and examines the ethical implications arising from the limitations of these models.
We introduce a novel conceptual paradigm for aligning the ethical values of big models and discuss promising research directions for alignment criteria, evaluation, and method.
arXiv Detail & Related papers (2023-10-26T16:45:40Z) - Heterogeneous Value Alignment Evaluation for Large Language Models [91.96728871418]
Large Language Models (LLMs) have made it crucial to align their values with those of humans.
We propose a Heterogeneous Value Alignment Evaluation (HVAE) system to assess the success of aligning LLMs with heterogeneous values.
arXiv Detail & Related papers (2023-05-26T02:34:20Z) - How Different Groups Prioritize Ethical Values for Responsible AI [75.40051547428592]
Private companies, public sector organizations, and academic groups have outlined ethical values they consider important for responsible AI technologies.
While their recommendations converge on a set of central values, little is known about the values a more representative public would find important for the AI technologies they interact with and might be affected by.
We conducted a survey examining how individuals perceive and prioritize responsible AI values across three groups.
arXiv Detail & Related papers (2022-05-16T14:39:37Z) - Towards a multi-stakeholder value-based assessment framework for
algorithmic systems [76.79703106646967]
We develop a value-based assessment framework that visualizes closeness and tensions between values.
We give guidelines on how to operationalize them, while opening up the evaluation and deliberation process to a wide range of stakeholders.
arXiv Detail & Related papers (2022-05-09T19:28:32Z) - Metaethical Perspectives on 'Benchmarking' AI Ethics [81.65697003067841]
Benchmarks are seen as the cornerstone for measuring technical progress in Artificial Intelligence (AI) research.
An increasingly prominent research area in AI is ethics, which currently has no set of benchmarks nor commonly accepted way for measuring the 'ethicality' of an AI system.
We argue that it makes more sense to talk about 'values' rather than 'ethics' when considering the possible actions of present and future AI systems.
arXiv Detail & Related papers (2022-04-11T14:36:39Z) - The Challenge of Value Alignment: from Fairer Algorithms to AI Safety [2.28438857884398]
This paper addresses the question of how to align AI systems with human values.
It situates it within a wider body of thought regarding technology and value.
arXiv Detail & Related papers (2021-01-15T11:03:15Z) - Steps Towards Value-Aligned Systems [0.0]
Algorithmic (including AI/ML) decision-making artifacts are an established and growing part of our decision-making ecosystem.
Current literature is full of examples of how individual artifacts violate societal norms and expectations.
This discussion argues for a more structured systems-level approach for assessing value-alignment in sociotechnical systems.
arXiv Detail & Related papers (2020-02-10T22:47:30Z)
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.