Learning Human-like Representations to Enable Learning Human Values

• URL: http://arxiv.org/abs/2312.14106v3
• Date: Fri, 08 Nov 2024 17:33:39 GMT
• Title: Learning Human-like Representations to Enable Learning Human Values
• Authors: Andrea Wynn, Ilia Sucholutsky, Thomas L. Griffiths,
• Abstract summary: We explore the effects of representational alignment between humans and AI agents on learning human values. We show that this kind of representational alignment can support safely learning and exploring human values in the context of personalization.
• Score: 11.236150405125754
• License:
• Abstract: How can we build AI systems that can learn any set of individual human values both quickly and safely, avoiding causing harm or violating societal standards for acceptable behavior during the learning process? We explore the effects of representational alignment between humans and AI agents on learning human values. Making AI systems learn human-like representations of the world has many known benefits, including improving generalization, robustness to domain shifts, and few-shot learning performance. We demonstrate that this kind of representational alignment can also support safely learning and exploring human values in the context of personalization. We begin with a theoretical prediction, show that it applies to learning human morality judgments, then show that our results generalize to ten different aspects of human values -- including ethics, honesty, and fairness -- training AI agents on each set of values in a multi-armed bandit setting, where rewards reflect human value judgments over the chosen action. Using a set of textual action descriptions, we collect value judgments from humans, as well as similarity judgments from both humans and multiple language models, and demonstrate that representational alignment enables both safe exploration and improved generalization when learning human values.

Related papers

• ValueCompass: A Framework of Fundamental Values for Human-AI Alignment [15.35489011078817]
  We introduce Value, a framework of fundamental values, grounded in psychological theory and a systematic review. We apply Value to measure the value alignment of humans and language models (LMs) across four real-world vignettes. Our findings uncover risky misalignment between humans and LMs, such as LMs agreeing with values like "Choose Own Goals", which are largely disagreed by humans.
  arXiv  Detail & Related papers  (2024-09-15T02:13:03Z)
• 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)
• 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)
• Culturally-Attuned Moral Machines: Implicit Learning of Human Value Systems by AI through Inverse Reinforcement Learning [11.948092546676687]
  We argue that the value system of an AI should be culturally attuned. How AI systems might acquire such codes from human observation and interaction has remained an open question. We show that an AI agent learning from the average behavior of a particular cultural group can acquire altruistic characteristics reflective of that group's behavior.
  arXiv  Detail & Related papers  (2023-12-29T05:39:10Z)
• Human-oriented Representation Learning for Robotic Manipulation [64.59499047836637]
  Humans inherently possess generalizable visual representations that empower them to efficiently explore and interact with the environments in manipulation tasks. We formalize this idea through the lens of human-oriented multi-task fine-tuning on top of pre-trained visual encoders. Our Task Fusion Decoder consistently improves the representation of three state-of-the-art visual encoders for downstream manipulation policy-learning.
  arXiv  Detail & Related papers  (2023-10-04T17:59:38Z)
• Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Duties [68.66719970507273]
  Value pluralism is the view that multiple correct values may be held in tension with one another. As statistical learners, AI systems fit to averages by default, washing out potentially irreducible value conflicts. We introduce ValuePrism, a large-scale dataset of 218k values, rights, and duties connected to 31k human-written situations.
  arXiv  Detail & Related papers  (2023-09-02T01:24:59Z)
• Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning [78.13740873213223]
  Bongard problems (BPs) were introduced as an inspirational challenge for visual cognition in intelligent systems. We propose a new benchmark Bongard-LOGO for human-level concept learning and reasoning.
  arXiv  Detail & Related papers  (2020-10-02T03:19:46Z)
• Aligning AI With Shared Human Values [85.2824609130584]
  We introduce the ETHICS dataset, a new benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality. We find that current language models have a promising but incomplete ability to predict basic human ethical judgements. Our work shows that progress can be made on machine ethics today, and it provides a steppingstone toward AI that is aligned with human values.
  arXiv  Detail & Related papers  (2020-08-05T17:59:16Z)
• Joint Inference of States, Robot Knowledge, and Human (False-)Beliefs [90.20235972293801]
  Aiming to understand how human (false-temporal)-belief-a core socio-cognitive ability unify-would affect human interactions with robots, this paper proposes to adopt a graphical model to the representation of object states, robot knowledge, and human (false-)beliefs. An inference algorithm is derived to fuse individual pg from all robots across multi-views into a joint pg, which affords more effective reasoning inference capability to overcome the errors originated from a single view.
  arXiv  Detail & Related papers  (2020-04-25T23:02:04Z)

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.