Achieving a Data‐Driven Risk Assessment Methodology for Ethical AI
(2022) In Digital Society 1(2). p.1-27- 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... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/cf36e6b3-a769-4912-9a5e-8b1657e31561
- author
- Felländer, Anna ; Rebane, Jonathan ; Larsson, Stefan LU ; Wiggberg, Mattias and Heintz, Fredrik
- organization
- publishing date
- 2022-08-18
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- AI ethics, AI governance, ethical assessment, multidisciplinary research, sustainable AI
- in
- Digital Society
- volume
- 1
- issue
- 2
- article number
- 13
- pages
- 27 pages
- publisher
- Springer Nature
- ISSN
- 2731-4669
- DOI
- 10.1007/s44206-022-00016-0
- project
- AI Transparency and Consumer Trust
- Ramverk för Hållbar AI
- language
- English
- LU publication?
- yes
- id
- cf36e6b3-a769-4912-9a5e-8b1657e31561
- date added to LUP
- 2022-08-18 13:54:16
- date last changed
- 2023-02-28 10:40:38
@article{cf36e6b3-a769-4912-9a5e-8b1657e31561, 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.}}, author = {{Felländer, Anna and Rebane, Jonathan and Larsson, Stefan and Wiggberg, Mattias and Heintz, Fredrik}}, issn = {{2731-4669}}, keywords = {{AI ethics; AI governance; ethical assessment; multidisciplinary research; sustainable AI}}, language = {{eng}}, month = {{08}}, number = {{2}}, pages = {{1--27}}, publisher = {{Springer Nature}}, series = {{Digital Society}}, title = {{Achieving a Data‐Driven Risk Assessment Methodology for Ethical AI}}, url = {{https://lup.lub.lu.se/search/files/122805415/Fell_nder_Rebane_et_al_2022_Achieving_a_Data_Driven_Risk_Assessment_Methodology_for_Ethical_AI.pdf}}, doi = {{10.1007/s44206-022-00016-0}}, volume = {{1}}, year = {{2022}}, }