An HCAI Methodological Framework (HCAI-MF): Putting It Into Action to Enable Human-Centered AI

• URL: http://arxiv.org/abs/2311.16027v4
• Date: Sat, 21 Dec 2024 04:36:02 GMT
• Title: An HCAI Methodological Framework (HCAI-MF): Putting It Into Action to Enable Human-Centered AI
• Authors: Wei Xu, Zaifeng Gao, Marvin Dainoff,
• Abstract summary: Human-centered artificial intelligence (HCAI) is a design philosophy that prioritizes humans in the design, development, deployment, and use of AI systems.<n>Despite its growing prominence in literature, the lack of methodological guidance for its implementation poses challenges to HCAI practice.<n>This paper proposes a comprehensive HCAI methodological framework (HCAI-MF) comprising five key components.
• Score: 8.094008212925598
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human-centered artificial intelligence (HCAI) is a design philosophy that prioritizes humans in the design, development, deployment, and use of AI systems, aiming to maximize AI's benefits while mitigating its negative impacts. Despite its growing prominence in literature, the lack of methodological guidance for its implementation poses challenges to HCAI practice. To address this gap, this paper proposes a comprehensive HCAI methodological framework (HCAI-MF) comprising five key components: HCAI requirement hierarchy, approach and method taxonomy, process, interdisciplinary collaboration approach, and multi-level design paradigms. A case study demonstrates HCAI-MF's practical implications, while the paper also analyzes implementation challenges. Actionable recommendations and a "three-layer" HCAI implementation strategy are provided to address these challenges and guide future evolution of HCAI-MF. HCAI-MF is presented as a systematic and executable methodology capable of overcoming current gaps, enabling effective design, development, deployment, and use of AI systems, and advancing HCAI practice.

Related papers

• Human-AI Interaction Design Standards [7.48545280997737]
  Human-AI interaction (HAII) standards are critical to supporting the industrialization of AI technology by following an HCAI approach. Despite their importance, the creation and adoption of HCAI-based interaction design standards face challenges. This chapter provides a comparative analysis of HAII versus traditional human-computer interaction (HCI) and outlines guiding principles for HCAI-based design.
  arXiv  Detail & Related papers  (2025-03-02T18:50:54Z)
• MQG4AI Towards Responsible High-risk AI - Illustrated for Transparency Focusing on Explainability Techniques [1.1105279729898387]
  We propose an approach for AI lifecycle planning that bridges the gap between generic guidelines and use case-specific requirements. Our work aims to contribute to the development of practical tools for implementing Responsible AI (RAI)
  arXiv  Detail & Related papers  (2025-02-17T15:14:52Z)
• AI-Aided Kalman Filters [65.35350122917914]
  The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing. Recent developments illustrate the possibility of fusing deep neural networks (DNNs) with classic Kalman-type filtering. This article provides a tutorial-style overview of design approaches for incorporating AI in aiding KF-type algorithms.
  arXiv  Detail & Related papers  (2024-10-16T06:47:53Z)
• 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)
• How Human-Centered Explainable AI Interface Are Designed and Evaluated: A Systematic Survey [48.97104365617498]
  The emerging area of em Explainable Interfaces (EIs) focuses on the user interface and user experience design aspects of XAI. This paper presents a systematic survey of 53 publications to identify current trends in human-XAI interaction and promising directions for EI design and development.
  arXiv  Detail & Related papers  (2024-03-21T15:44:56Z)
• 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)
• Human-Centered AI Product Prototyping with No-Code AutoML: Conceptual Framework, Potentials and Limitations [0.0]
  This paper focuses on the challenges posed by the probabilistic nature of AI behavior and the limited accessibility of prototyping tools to non-experts. A Design Science Research (DSR) approach is presented which culminates in a conceptual framework aimed at improving the AI prototyping process. The framework describes the seamless incorporation of non-expert input and evaluation during prototyping, leveraging the potential of no-code AutoML to enhance accessibility and interpretability.
  arXiv  Detail & Related papers  (2024-02-06T16:00:32Z)
• Enabling Human-Centered AI: A Methodological Perspective [10.746728034149989]
  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.
  arXiv  Detail & Related papers  (2023-11-12T01:31:34Z)
• Levels of AGI for Operationalizing Progress on the Path to AGI [64.59151650272477]
  We propose a framework for classifying the capabilities and behavior of Artificial General Intelligence (AGI) models and their precursors. This framework introduces levels of AGI performance, generality, and autonomy, providing a common language to compare models, assess risks, and measure progress along the path to AGI.
  arXiv  Detail & Related papers  (2023-11-04T17:44:58Z)
• Conceptual Framework for Autonomous Cognitive Entities [0.9285295512807729]
  This paper introduces the Autonomous Cognitive Entity model, a novel framework for a cognitive architecture. The model is designed to harness the capabilities of the latest generative AI technologies, including large language models (LLMs) and multimodal generative models (MMMs) The ACE framework also incorporates mechanisms for handling failures and adapting actions, thereby enhancing the robustness and flexibility of autonomous agents.
  arXiv  Detail & Related papers  (2023-10-03T15:53:55Z)
• 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)
• Responsible AI Implementation: A Human-centered Framework for Accelerating the Innovation Process [0.8481798330936974]
  This paper proposes a theoretical framework for responsible artificial intelligence (AI) implementation. The proposed framework emphasizes a synergistic business technology approach for the agile co-creation process. The framework emphasizes establishing and maintaining trust throughout the human-centered design and agile development of AI.
  arXiv  Detail & Related papers  (2022-09-15T06:24:01Z)
• 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)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)
• 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.