Learning Norms via Natural Language Teachings

• URL: http://arxiv.org/abs/2201.10556v1
• Date: Thu, 20 Jan 2022 22:09:42 GMT
• Title: Learning Norms via Natural Language Teachings
• Authors: Taylor Olson and Ken Forbus
• Abstract summary: This paper introduces and demonstrates a computational approach to learning norms from natural language text. It provides a foundation for everyday people to train AI systems about social norms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To interact with humans, artificial intelligence (AI) systems must understand our social world. Within this world norms play an important role in motivating and guiding agents. However, very few computational theories for learning social norms have been proposed. There also exists a long history of debate on the distinction between what is normal (is) and what is normative (ought). Many have argued that being capable of learning both concepts and recognizing the difference is necessary for all social agents. This paper introduces and demonstrates a computational approach to learning norms from natural language text that accounts for both what is normal and what is normative. It provides a foundation for everyday people to train AI systems about social norms.

Related papers

• The Goofus & Gallant Story Corpus for Practical Value Alignment [2.0938191327156037]
  Values or principles are key elements of human society that influence people to behave and function according to an accepted standard set of social rules. As AI systems are becoming ubiquitous in human society, it is a major concern that they could violate these norms or values and potentially cause harm. This work presents a multi-modal dataset illustrating normative and non-normative behavior in real-life situations.
  arXiv  Detail & Related papers  (2025-01-16T17:58:58Z)
• Are language models rational? The case of coherence norms and belief revision [63.78798769882708]
  We consider logical coherence norms as well as coherence norms tied to the strength of belief in language models. We argue that rational norms tied to coherence do apply to some language models, but not to others.
  arXiv  Detail & Related papers  (2024-06-05T16:36:21Z)
• Reading Books is Great, But Not if You Are Driving! Visually Grounded Reasoning about Defeasible Commonsense Norms [65.17491295329991]
  We construct a new benchmark for studying visual-grounded commonsense norms: NORMLENS. We find that state-of-the-art model judgments and explanations are not well-aligned with human annotation. We present a new approach to better align models with humans by distilling social commonsense knowledge from large language models.
  arXiv  Detail & Related papers  (2023-10-16T14:00:07Z)
• Shaping New Norms for AI [0.0]
  Article aims to offer readers interpretive tools to understand society's response to the growing pervasiveness of AI. An outlook on how AI could influence the formation of future social norms emphasises the importance for open societies to anchor their formal deliberation process in an open, inclusive, and transparent public discourse.
  arXiv  Detail & Related papers  (2023-07-17T15:31:58Z)
• NormSAGE: Multi-Lingual Multi-Cultural Norm Discovery from Conversations On-the-Fly [61.77957329364812]
  We introduce a framework for addressing the novel task of conversation-grounded multi-lingual, multi-cultural norm discovery. NormSAGE elicits knowledge about norms through directed questions representing the norm discovery task and conversation context. It further addresses the risk of language model hallucination with a self-verification mechanism ensuring that the norms discovered are correct.
  arXiv  Detail & Related papers  (2022-10-16T18:30:05Z)
• 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)
• SocialAI: Benchmarking Socio-Cognitive Abilities in Deep Reinforcement Learning Agents [23.719833581321033]
  Building embodied autonomous agents capable of participating in social interactions with humans is one of the main challenges in AI. We argue that aiming towards human-level AI requires a broader set of key social skills. We present SocialAI, a benchmark to assess the acquisition of social skills of DRL agents.
  arXiv  Detail & Related papers  (2021-07-02T10:39:18Z)
• SocialAI 0.1: Towards a Benchmark to Stimulate Research on Socio-Cognitive Abilities in Deep Reinforcement Learning Agents [23.719833581321033]
  Building embodied autonomous agents capable of participating in social interactions with humans is one of the main challenges in AI. Current approaches focus on language as a communication tool in very simplified and non diverse social situations. We argue that aiming towards human-level AI requires a broader set of key social skills.
  arXiv  Detail & Related papers  (2021-04-27T14:16:29Z)
• Social Chemistry 101: Learning to Reason about Social and Moral Norms [73.23298385380636]
  We present Social Chemistry, a new conceptual formalism to study people's everyday social norms and moral judgments. Social-Chem-101 is a large-scale corpus that catalogs 292k rules-of-thumb. Our model framework, Neural Norm Transformer, learns and generalizes Social-Chem-101 to successfully reason about previously unseen situations.
  arXiv  Detail & Related papers  (2020-11-01T20:16:45Z)
• What am I allowed to do here?: Online Learning of Context-Specific Norms by Pepper [22.387008072671005]
  The paper utilizes a recent state-of-the-art approach for incremental learning and adapts it for online learning of scenes (contexts) After learning the scenes (contexts), we use active learning to learn related norms. Our results show that Pepper can learn different scenes and related norms simply by communicating with a human partner in an online manner.
  arXiv  Detail & Related papers  (2020-08-22T07:27:02Z)
• Machine Common Sense [77.34726150561087]
  Machine common sense remains a broad, potentially unbounded problem in artificial intelligence (AI) This article deals with the aspects of modeling commonsense reasoning focusing on such domain as interpersonal interactions.
  arXiv  Detail & Related papers  (2020-06-15T13:59:47Z)

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.