Achieving a Data-driven Risk Assessment Methodology for Ethical AI
(2021) In arXiv.org- 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... (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. 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/b0bbd0a6-a9f9-4054-9bf6-8ae4002c04f1
- author
- Felländer, Anna ; Rebane, Jonathan ; Larsson, Stefan LU ; Wiggberg, Mattias and Heintz, Fredrik
- organization
- publishing date
- 2021-12-06
- type
- Working paper/Preprint
- publication status
- published
- subject
- keywords
- data-driven, ethical AI, sustainable AI, AI-assessment, risk assessment, accountability, transparency, multidisciplinary, AI and society, AI and organisations
- in
- arXiv.org
- pages
- 29 pages
- publisher
- arXiv.org
- ISSN
- 2331-8422
- project
- AI Transparency and Consumer Trust
- language
- English
- LU publication?
- yes
- id
- b0bbd0a6-a9f9-4054-9bf6-8ae4002c04f1
- alternative location
- https://arxiv.org/abs/2112.01282
- date added to LUP
- 2021-12-06 11:26:37
- date last changed
- 2022-03-11 10:20:44
@misc{b0bbd0a6-a9f9-4054-9bf6-8ae4002c04f1, 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.}}, author = {{Felländer, Anna and Rebane, Jonathan and Larsson, Stefan and Wiggberg, Mattias and Heintz, Fredrik}}, issn = {{2331-8422}}, keywords = {{data-driven; ethical AI; sustainable AI; AI-assessment; risk assessment; accountability; transparency; multidisciplinary; AI and society; AI and organisations}}, language = {{eng}}, month = {{12}}, note = {{Preprint}}, publisher = {{arXiv.org}}, series = {{arXiv.org}}, title = {{Achieving a Data-driven Risk Assessment Methodology for Ethical AI}}, url = {{https://arxiv.org/abs/2112.01282}}, year = {{2021}}, }