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Achieving a Data‐Driven Risk Assessment Methodology for Ethical AI

Felländer, Anna ; Rebane, Jonathan ; Larsson, Stefan LU ; Wiggberg, Mattias and Heintz, Fredrik (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)
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author
; ; ; and
organization
publishing date
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}},
}