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| 2. |
- Larsson, Stefan, et al.
(author)
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AI-teknologin måste gå att lita på
- 2019
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In: Entreprenör.
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Journal article (pop. science, debate, etc.)abstract
- DEBATT Den snabba utvecklingen inom artificiell intelligens ger stora utmaningar, vilket kan skada tilliten till AI, skriver fyra forskare. I en nyligen publicerad rapport har de presenterat såväl problemområden som förslag till lösningar.
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| 3. |
- Larsson, Stefan, et al.
(author)
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HÅLLBAR AI : Inventering av kunskapsläget för etiska, sociala och rättsliga utmaningar med artificiell intelligens
- 2019
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Reports (other academic/artistic)abstract
- Detta är en inventering av kunskapsläget för etiska, sociala, och rättsliga utmaningar med artificiell intelligens, utfört i ett Vinnovafinansierat projekt lett av Anna Felländer. Baserat på en kartläggning av rapporter och studier, en kvantitativ och bibliometrisk analys, och områdesfördjupningar inom vård och hälsa, telekom, och digitala plattformar ges tre rekommendationer: Hållbar AI kräver att vi 1. fokuserar regleringsfrågor i vid mening, 2. stimulerar mångvetenskap och samverkan, samt att 3. tillitsbyggande i användningen av samhällsapplicerad artificiell intelligens och maskininlärning är centralt och kräver mer kunskap i relationen mellan transparens och ansvar.
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| 4. |
- Larsson, Stefan, et al.
(author)
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Sustainable AI : An inventory of the state of knowledge of ethical, social, and legal challenges related to artificial intelligence
- 2019
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Reports (other academic/artistic)abstract
- This report is an inventory of the state of knowledge of ethical, social, and legal challenges related to artificial intelligence conducted within the Swedish Vinnova-funded project “Hållbar AI – AI Ethics and Sustainability”, led by Anna Felländer. Based on a review and mapping of reports and studies, a quantitative and bibliometric analysis, and in-depth analyses of the healt- care sector, the telecom sector, and digital platforms, the report proposes three recommendations. Sustainable AI requires: 1. a broad focus on AI governance and regulation issues, 2. promoting multi-disciplinary collaboration, and 3. building trust in AI applications and applied machine-learning, which is a matter of key importance and requires further study of the relationship between transparency and accountability.
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| 7. |
- Felländer, Anna, et al.
(author)
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Achieving a Data-driven Risk Assessment Methodology for Ethical AI
- 2021
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Other publication (other academic/artistic)abstract
- 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.
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| 8. |
- Felländer, Anna, et al.
(author)
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Achieving a Data‐Driven Risk Assessment Methodology for Ethical AI
- 2022
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In: Digital Society. - : Springer Science and Business Media LLC. - 2731-4669 .- 2731-4650. ; 1:2, s. 1-27
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Journal article (peer-reviewed)abstract
- 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 within AI governance. 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 are 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.
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| 9. |
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| 10. |
- Felländer, Anna, et al.
(author)
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The three phases of FinTech
- 2018
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In: The rise and development of FinTech : accounts of disruption from Sweden and beyond. - 9780815378501 ; , s. 154-167
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Book chapter (other academic/artistic)
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