Enabling Human-Centered AI: A Methodological Perspective
- URL: http://arxiv.org/abs/2311.06703v2
- Date: Tue, 14 Nov 2023 17:32:07 GMT
- Title: Enabling Human-Centered AI: A Methodological Perspective
- Authors: Wei Xu, Zaifeng Gao
- Abstract summary: Human-centered AI (HCAI) is a design philosophy that advocates prioritizing humans in designing, developing, and deploying intelligent systems.
This paper proposes a comprehensive HCAI framework based on our previous work with integrated components, including design goals, design principles, implementation approaches, interdisciplinary teams, HCAI methods, and HCAI processes.
- Score: 10.746728034149989
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Human-centered AI (HCAI) is a design philosophy that advocates prioritizing
humans in designing, developing, and deploying intelligent systems, aiming to
maximize the benefits of AI to humans and avoid potential adverse impacts.
While HCAI continues to influence, the lack of guidance on methodology in
practice makes its adoption challenging. This paper proposes a comprehensive
HCAI framework based on our previous work with integrated components, including
design goals, design principles, implementation approaches, interdisciplinary
teams, HCAI methods, and HCAI processes. This paper also presents a
"three-layer" approach to facilitate the implementation of the framework. We
believe this systematic and executable framework can overcome the weaknesses in
current HCAI frameworks and the challenges currently faced in practice, putting
it into action to enable HCAI further.
Related papers
- AI-Driven Human-Autonomy Teaming in Tactical Operations: Proposed Framework, Challenges, and Future Directions [10.16399860867284]
Artificial Intelligence (AI) techniques are transforming tactical operations by augmenting human decision-making capabilities.
This paper explores AI-driven Human-Autonomy Teaming (HAT) as a transformative approach.
We propose a comprehensive framework that addresses the key components of AI-driven HAT.
arXiv Detail & Related papers (2024-10-28T15:05:16Z) - Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI [52.138044013005]
generative AI, particularly large language models (LLMs), become increasingly integrated into production applications.
New attack surfaces and vulnerabilities emerge and put a focus on adversarial threats in natural language and multi-modal systems.
Red-teaming has gained importance in proactively identifying weaknesses in these systems, while blue-teaming works to protect against such adversarial attacks.
This work aims to bridge the gap between academic insights and practical security measures for the protection of generative AI systems.
arXiv Detail & Related papers (2024-09-23T10:18:10Z) - Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems [88.80306881112313]
We will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI.
The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees.
We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them.
arXiv Detail & Related papers (2024-05-10T17:38:32Z) - Incentive Compatibility for AI Alignment in Sociotechnical Systems:
Positions and Prospects [11.086872298007835]
Existing methodologies primarily focus on technical facets, often neglecting the intricate sociotechnical nature of AI systems.
We posit a new problem worth exploring: Incentive Compatibility Sociotechnical Alignment Problem (ICSAP)
We discuss three classical game problems for achieving IC: mechanism design, contract theory, and Bayesian persuasion, in addressing the perspectives, potentials, and challenges of solving ICSAP.
arXiv Detail & Related papers (2024-02-20T10:52:57Z) - An HCAI Methodological Framework (HCAI-MF): Putting It Into Action to Enable Human-Centered AI [8.094008212925598]
Human-centered artificial intelligence (HCAI) is a design philosophy that prioritizes humans in the design, development, deployment, and use of AI systems.
Despite its growing prominence in literature, the lack of methodological guidance for its implementation poses challenges to HCAI practice.
This paper proposes a comprehensive HCAI methodological framework (HCAI-MF) comprising five key components.
arXiv Detail & Related papers (2023-11-27T17:40:49Z) - The Participatory Turn in AI Design: Theoretical Foundations and the
Current State of Practice [64.29355073494125]
This article aims to ground what we dub the "participatory turn" in AI design by synthesizing existing theoretical literature on participation.
We articulate empirical findings concerning the current state of participatory practice in AI design based on an analysis of recently published research and semi-structured interviews with 12 AI researchers and practitioners.
arXiv Detail & Related papers (2023-10-02T05:30:42Z) - 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) - 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) - From Human-Computer Interaction to Human-AI Interaction: New Challenges
and Opportunities for Enabling Human-Centered AI [7.3800748017024755]
We focus on the unique characteristics of AI technology and the differences between non-AI computing systems and AI systems.
We promote the research and application of human-AI interaction (HAII) as an interdisciplinary collaboration.
arXiv Detail & Related papers (2021-05-12T04:30:45Z) - Distributed and Democratized Learning: Philosophy and Research
Challenges [80.39805582015133]
We propose a novel design philosophy called democratized learning (Dem-AI)
Inspired by the societal groups of humans, the specialized groups of learning agents in the proposed Dem-AI system are self-organized in a hierarchical structure to collectively perform learning tasks more efficiently.
We present a reference design as a guideline to realize future Dem-AI systems, inspired by various interdisciplinary fields.
arXiv Detail & Related papers (2020-03-18T08:45:10Z)
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.