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Achieving a Data-dr...
Achieving a Data-driven Risk Assessment Methodology for Ethical AI
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- Felländer, Anna (author)
- AI Sustainability Center
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- Rebane, Jonathan (author)
- AI Sustainability Center
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- Larsson, Stefan (author)
- Lund University,Lunds universitet,Fastighetsvetenskap,Institutionen för teknik och samhälle,Institutioner vid LTH,Lunds Tekniska Högskola,Real Estate Science,Department of Technology and Society,Departments at LTH,Faculty of Engineering, LTH,AI Sustainability Center
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- Wiggberg, Mattias (author)
- KTH Royal Institute of Technology
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- Heintz, Fredrik (author)
- Linköping University
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(creator_code:org_t)
- 2021
- English 29 s.
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Series: arXiv.org, 2331-8422
- Related links:
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https://arxiv.org/ab... (free)
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Abstract
Subject headings
Close
- The AI landscape demands a broad set of legal, ethical, and societal considerations to be accounted for in order to develop ethical AI (eAI) solutions which sustain human values and rights. Currently, a variety of guidelines and a handful of niche tools exist to account for and tackle individual challenges. However, it is also well established that many organizations face practical challenges in navigating these considerations from a risk management perspective. Therefore, new methodologies are needed to provide a well-vetted and real-world applicable structure and path through the checks and balances needed for ethically assessing and guiding the development of AI. In this paper we show that a multidisciplinary research approach, spanning cross-sectional viewpoints, is the foundation of a pragmatic definition of ethical and societal risks faced by organizations using AI. Equally important is the findings of cross-structural governance for implementing eAI successfully. Based on evidence acquired from our multidisciplinary research investigation, we propose a novel data-driven risk assessment methodology, entitled DRESS-eAI. In addition, through the evaluation of our methodological implementation, we demonstrate its state-of-the-art relevance as a tool for sustaining human values in the data-driven AI era.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Annan teknik -- Övrig annan teknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Other Engineering and Technologies -- Other Engineering and Technologies not elsewhere specified (hsv//eng)
- SAMHÄLLSVETENSKAP -- Juridik -- Juridik och samhälle (hsv//swe)
- SOCIAL SCIENCES -- Law -- Law and Society (hsv//eng)
- SAMHÄLLSVETENSKAP -- Ekonomi och näringsliv -- Företagsekonomi (hsv//swe)
- SOCIAL SCIENCES -- Economics and Business -- Business Administration (hsv//eng)
- HUMANIORA -- Filosofi, etik och religion -- Etik (hsv//swe)
- HUMANITIES -- Philosophy, Ethics and Religion -- Ethics (hsv//eng)
Keyword
- data-driven
- ethical AI
- sustainable AI
- AI-assessment
- risk assessment
- accountability
- transparency
- multidisciplinary
- AI and society
- AI and organisations
Publication and Content Type
- ovr (subject category)
- vet (subject category)
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