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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 (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:
author
; ; ; and
organization
publishing date
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}},
}