Achieving a Data‐Driven Risk Assessment Methodology for Ethical AI

Anna Felländer, Jonathan Rebane, Stefan Larsson, Mattias Wiggberg, Fredrik Heintz

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskriftPeer review

32 Nedladdningar (Pure)


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.
Sidor (från-till)1-27
Antal sidor27
TidskriftDigital Society
StatusPublished - 2022 aug. 18

Ämnesklassifikation (UKÄ)

  • Annan teknik
  • Juridik och samhälle
  • Tvärvetenskapliga studier


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