AI-HRI Brings New Dimensions to Human-Aware Design for Human-Aware AI
- URL: http://arxiv.org/abs/2210.11832v1
- Date: Fri, 21 Oct 2022 09:25:06 GMT
- Title: AI-HRI Brings New Dimensions to Human-Aware Design for Human-Aware AI
- Authors: Richard G. Freedman
- Abstract summary: We will explore how AI-HRI can change the way researchers think about human-aware AI.
There is no greater opportunity for sharing perspectives at the moment than human-aware AI.
- Score: 2.512827436728378
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Since the first AI-HRI held at the 2014 AAAI Fall Symposium Series, a lot of
the presented research and discussions have emphasized how artificial
intelligence (AI) developments can benefit human-robot interaction (HRI). This
portrays HRI as an application, a source of domain-specific problems to solve,
to the AI community. Likewise, this portrays AI as a tool, a source of
solutions available for relevant problems, to the HRI community. However,
members of the AI-HRI research community will point out that the relationship
has a deeper synergy than matchmaking problems and solutions -- there are
insights from each field that impact how the other one thinks about the world
and performs scientific research. There is no greater opportunity for sharing
perspectives at the moment than human-aware AI, which studies how to account
for the fact that people are more than a source of data or part of an
algorithm. We will explore how AI-HRI can change the way researchers think
about human-aware AI, from observation through validation, to make even the
algorithmic design process human-aware.
Related papers
- Explainable Human-AI Interaction: A Planning Perspective [32.477369282996385]
AI systems need to be explainable to the humans in the loop.
We will discuss how the AI agent can use mental models to either conform to human expectations, or change those expectations through explanatory communication.
While the main focus of the book is on cooperative scenarios, we will point out how the same mental models can be used for obfuscation and deception.
arXiv Detail & Related papers (2024-05-19T22:22:21Z) - Advancing Explainable AI Toward Human-Like Intelligence: Forging the
Path to Artificial Brain [0.7770029179741429]
The intersection of Artificial Intelligence (AI) and neuroscience in Explainable AI (XAI) is pivotal for enhancing transparency and interpretability in complex decision-making processes.
This paper explores the evolution of XAI methodologies, ranging from feature-based to human-centric approaches.
The challenges in achieving explainability in generative models, ensuring responsible AI practices, and addressing ethical implications are discussed.
arXiv Detail & Related papers (2024-02-07T14:09:11Z) - 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) - The Role of AI in Drug Discovery: Challenges, Opportunities, and
Strategies [97.5153823429076]
The benefits, challenges and drawbacks of AI in this field are reviewed.
The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
arXiv Detail & Related papers (2022-12-08T23:23:39Z) - Proceedings of the AI-HRI Symposium at AAAI-FSS 2022 [10.710184843122311]
The Artificial Intelligence for Human-Robot Interaction (HRI) Symposium has been a successful venue of discussion and collaboration since 2014.
This year, after a review of the achievements of the AI-HRI community over the last decade in 2021, we are focusing on a visionary theme: exploring the future of AI-HRI.
With the success of past symposiums, AI-HRI impacts a variety of communities and problems, and has pioneered the discussions in recent trends and interests.
arXiv Detail & Related papers (2022-09-28T17:55:46Z) - On the Effect of Information Asymmetry in Human-AI Teams [0.0]
We focus on the existence of complementarity potential between humans and AI.
Specifically, we identify information asymmetry as an essential source of complementarity potential.
By conducting an online experiment, we demonstrate that humans can use such contextual information to adjust the AI's decision.
arXiv Detail & Related papers (2022-05-03T13:02:50Z) - On some Foundational Aspects of Human-Centered Artificial Intelligence [52.03866242565846]
There is no clear definition of what is meant by Human Centered Artificial Intelligence.
This paper introduces the term HCAI agent to refer to any physical or software computational agent equipped with AI components.
We see the notion of HCAI agent, together with its components and functions, as a way to bridge the technical and non-technical discussions on human-centered AI.
arXiv Detail & Related papers (2021-12-29T09:58:59Z) - A User-Centred Framework for Explainable Artificial Intelligence in
Human-Robot Interaction [70.11080854486953]
We propose a user-centred framework for XAI that focuses on its social-interactive aspect.
The framework aims to provide a structure for interactive XAI solutions thought for non-expert users.
arXiv Detail & Related papers (2021-09-27T09:56:23Z) - Trustworthy AI: A Computational Perspective [54.80482955088197]
We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being.
For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
arXiv Detail & Related papers (2021-07-12T14:21:46Z) - eXtended Artificial Intelligence: New Prospects of Human-AI Interaction
Research [8.315174426992087]
The article provides a theoretical treatment and model of human-AI interaction based on an XR-AI continuum.
It shows why the combination of XR and AI fruitfully contributes to a valid and systematic investigation of human-AI interactions and interfaces.
The first experiment reveals an interesting gender effect in human-robot interaction, while the second experiment reveals an Eliza effect of a recommender system.
arXiv Detail & Related papers (2021-03-27T22:12:06Z) - The Road to a Successful HRI: AI, Trust and ethicS-TRAITS [65.60507052509406]
The aim of this workshop is to give researchers from academia and industry the possibility to discuss the inter-and multi-disciplinary nature of the relationships between people and robots.
arXiv Detail & Related papers (2021-03-23T16:52:12Z)
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.