Responsible Artificial Intelligence: A Structured Literature Review

• URL: http://arxiv.org/abs/2403.06910v1
• Date: Mon, 11 Mar 2024 17:01:13 GMT
• Title: Responsible Artificial Intelligence: A Structured Literature Review
• Authors: Sabrina Goellner, Marina Tropmann-Frick, Bostjan Brumen
• Abstract summary: The EU has recently issued several publications emphasizing the necessity of trust in AI. This highlights the urgent need for international regulation. This paper introduces a comprehensive and, to our knowledge, the first unified definition of responsible AI.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Our research endeavors to advance the concept of responsible artificial intelligence (AI), a topic of increasing importance within EU policy discussions. The EU has recently issued several publications emphasizing the necessity of trust in AI, underscoring the dual nature of AI as both a beneficial tool and a potential weapon. This dichotomy highlights the urgent need for international regulation. Concurrently, there is a need for frameworks that guide companies in AI development, ensuring compliance with such regulations. Our research aims to assist lawmakers and machine learning practitioners in navigating the evolving landscape of AI regulation, identifying focal areas for future attention. This paper introduces a comprehensive and, to our knowledge, the first unified definition of responsible AI. Through a structured literature review, we elucidate the current understanding of responsible AI. Drawing from this analysis, we propose an approach for developing a future framework centered around this concept. Our findings advocate for a human-centric approach to Responsible AI. This approach encompasses the implementation of AI methods with a strong emphasis on ethics, model explainability, and the pillars of privacy, security, and trust.

Related papers

• Using AI Alignment Theory to understand the potential pitfalls of regulatory frameworks [55.2480439325792]
  This paper critically examines the European Union's Artificial Intelligence Act (EU AI Act) Uses insights from Alignment Theory (AT) research, which focuses on the potential pitfalls of technical alignment in Artificial Intelligence. As we apply these concepts to the EU AI Act, we uncover potential vulnerabilities and areas for improvement in the regulation.
  arXiv  Detail & Related papers  (2024-10-10T17:38:38Z)
• Responsible AI: Portraits with Intelligent Bibliometrics [30.51687434548628]
  This study defined responsible AI and identified its core principles. Empirically, this study investigated 17,799 research articles contributed by the AI community since 2015. An analysis of a core cohort comprising 380 articles from multiple disciplines captures the most recent advancements in responsible AI.
  arXiv  Detail & Related papers  (2024-05-05T08:40:22Z)
• Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits [54.648819983899614]
  General purpose AI seems to have lowered the barriers for the public to use AI and harness its power. We introduce PARTICIP-AI, a framework for laypeople to speculate and assess AI use cases and their impacts.
  arXiv  Detail & Related papers  (2024-03-21T19:12:37Z)
• Towards a Privacy and Security-Aware Framework for Ethical AI: Guiding the Development and Assessment of AI Systems [0.0]
  This study conducts a systematic literature review spanning the years 2020 to 2023. Through the synthesis of knowledge extracted from the SLR, this study presents a conceptual framework tailored for privacy- and security-aware AI systems.
  arXiv  Detail & Related papers  (2024-03-13T15:39:57Z)
• Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
  We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions. In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
  arXiv  Detail & Related papers  (2024-02-28T16:09:56Z)
• POLARIS: A framework to guide the development of Trustworthy AI systems [3.02243271391691]
  There is a significant gap between high-level AI ethics principles and low-level concrete practices for AI professionals. We develop a novel holistic framework for Trustworthy AI - designed to bridge the gap between theory and practice. Our goal is to empower AI professionals to confidently navigate the ethical dimensions of Trustworthy AI.
  arXiv  Detail & Related papers  (2024-02-08T01:05:16Z)
• Artificial intelligence in government: Concepts, standards, and a unified framework [0.0]
  Recent advances in artificial intelligence (AI) hold the promise of transforming government. It is critical that new AI systems behave in alignment with the normative expectations of society.
  arXiv  Detail & Related papers  (2022-10-31T10:57:20Z)
• Fairness in Agreement With European Values: An Interdisciplinary Perspective on AI Regulation [61.77881142275982]
  This interdisciplinary position paper considers various concerns surrounding fairness and discrimination in AI, and discusses how AI regulations address them. We first look at AI and fairness through the lenses of law, (AI) industry, sociotechnology, and (moral) philosophy, and present various perspectives. We identify and propose the roles AI Regulation should take to make the endeavor of the AI Act a success in terms of AI fairness concerns.
  arXiv  Detail & Related papers  (2022-06-08T12:32:08Z)
• Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
  In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
  arXiv  Detail & Related papers  (2021-06-25T22:31:55Z)
• 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)
• The Sanction of Authority: Promoting Public Trust in AI [4.729969944853141]
  We argue that public distrust of AI originates from the under-development of a regulatory ecosystem that would guarantee the trustworthiness of the AIs that pervade society. We elaborate the pivotal role of externally auditable AI documentation within this model and the work to be done to ensure it is effective.
  arXiv  Detail & Related papers  (2021-01-22T22:01:30Z)

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.