Towards Human-AI Mutual Learning: A New Research Paradigm
- URL: http://arxiv.org/abs/2405.04687v1
- Date: Tue, 7 May 2024 21:59:57 GMT
- Title: Towards Human-AI Mutual Learning: A New Research Paradigm
- Authors: Xiaomei Wang, Xiaoyu Chen,
- Abstract summary: This paper describes a new research paradigm for studying human-AI collaboration, named "human-AI mutual learning"
We describe relevant methodologies, motivations, domain examples, benefits, challenges, and future research agenda under this paradigm.
- Score: 9.182022050832108
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This paper describes a new research paradigm for studying human-AI collaboration, named "human-AI mutual learning", defined as the process where humans and AI agents preserve, exchange, and improve knowledge during human-AI collaboration. We describe relevant methodologies, motivations, domain examples, benefits, challenges, and future research agenda under this paradigm.
Related papers
- 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) - 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) - Multi-Agent, Human-Agent and Beyond: A Survey on Cooperation in Social Dilemmas [15.785674974107204]
The study of cooperation within social dilemmas has long been a fundamental topic across various disciplines.
Recent advancements in Artificial Intelligence have significantly reshaped this field.
This survey examines three key areas at the intersection of AI and cooperation in social dilemmas.
arXiv Detail & Related papers (2024-02-27T07:31:30Z) - Human-AI Coevolution [48.74579595505374]
Coevolution AI is a process in which humans and AI algorithms continuously influence each other.
This paper introduces Coevolution AI as the cornerstone for a new field of study at the intersection between AI and complexity science.
arXiv Detail & Related papers (2023-06-23T18:10:54Z) - Mutual Theory of Mind for Human-AI Communication [5.969858080492586]
New developments are enabling AI systems to perceive, recognize, and respond with social cues based on humans' explicit or implicit behavioral and verbal cues.
These AI systems are currently serving as matchmakers on dating platforms, assisting student learning as teaching assistants, and enhancing productivity as work partners.
We propose the Mutual Theory of Mind (MToM) framework, inspired by our capability of ToM in human-human communications, to guide this new generation of HAI research.
arXiv Detail & Related papers (2022-10-07T22:46:04Z) - A Mental-Model Centric Landscape of Human-AI Symbiosis [31.14516396625931]
We introduce a significantly general version of human-aware AI interaction scheme, called generalized human-aware interaction (GHAI)
We will see how this new framework allows us to capture the various works done in the space of human-AI interaction and identify the fundamental behavioral patterns supported by these works.
arXiv Detail & Related papers (2022-02-18T22:08:08Z) - Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and
Stir" [76.44130385507894]
This paper aims to ground what we dub a 'participatory turn' in AI design by synthesizing existing literature on participation and through empirical analysis of its current practices.
Based on our literature synthesis and empirical research, this paper presents a conceptual framework for analyzing participatory approaches to AI design.
arXiv Detail & Related papers (2021-11-01T17:57:04Z) - Human-Robot Collaboration and Machine Learning: A Systematic Review of
Recent Research [69.48907856390834]
Human-robot collaboration (HRC) is the approach that explores the interaction between a human and a robot.
This paper proposes a thorough literature review of the use of machine learning techniques in the context of HRC.
arXiv Detail & Related papers (2021-10-14T15:14:33Z) - Human-Centered AI for Data Science: A Systematic Approach [48.71756559152512]
Human-Centered AI (HCAI) refers to the research effort that aims to design and implement AI techniques to support various human tasks.
We illustrate how we approach HCAI using a series of research projects around Data Science (DS) works as a case study.
arXiv Detail & Related papers (2021-10-03T21:47:13Z) - Human-AI Symbiosis: A Survey of Current Approaches [18.252264744963394]
We highlight various aspects of works on the human-AI team such as the flow of complementing, task horizon, model representation, knowledge level, and teaming goal.
We hope that the survey will provide a more clear connection between the works in the human-AI team and guidance to new researchers in this area.
arXiv Detail & Related papers (2021-03-18T02:39:28Z)
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.