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  • 1.
    Brännström, Mattias
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Jiang, Lili
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Aler Tubella, Andrea
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Dignum, Virginia
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Impact based fairness framework for socio-technical decision making2023In: Proceedings of the 1st workshop on fairness and bias in AIco-located with 26th european conference on artificial intelligence (ECAI 2023) / [ed] Roberta Calegari; Andrea Aler Tubella; Gabriel González Castañe; Virginia Dignum; Michela Milano, CEUR-WS , 2023Conference paper (Refereed)
    Abstract [en]

    Avoiding bias and understanding the consequences of artificial intelligence used in decision making is of high importance to avoid mistreatment and unintended harm. This paper aims to present an impact focused approach to model the information flow of a socio-technical decision system for analysis of bias and fairness. The framework roots otherwise abstract technical accuracy and bias measures in stakeholder effects and forms a scaffold around which further analysis of the socio-technical system and its components can be coordinated. Two example use-cases are presented and analysed.

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  • 2.
    Brännström, Mattias
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Theodorou, Andreas
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Dignum, Virginia
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Let it RAIN for social good2022In: Proceedings of the Workshop on Artificial Intelligence Safety 2022 (AISafety 2022) / [ed] Gabriel Pedroza; Xin Cynthia Chen; José Hernández-Orallo; Xiaowei Huang; Huáscar Espinoza; Richard Mallah; John McDermid; Mauricio Castillo-Effen, CEUR-WS , 2022, Vol. 3215, article id 6Conference paper (Refereed)
    Abstract [en]

    Artificial Intelligence (AI) as a highly transformative technology take on a special role as both an enabler and a threat to UN Sustainable Development Goals (SDGs). AI Ethics and emerging high-level policy efforts stand at the pivot point between these outcomes but is barred from effect due the abstraction gap between high-level values and responsible action. In this paper the Responsible Norms (RAIN) framework is presented, bridging this gap thereby enabling effective high-level control of AI impact. With effective and operationalized AI Ethics, AI technologies can be directed towards global sustainable development.

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    fulltext
  • 3.
    Methnani, Leila
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Brännström, Mattias
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Theodorou, Andreas
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Operationalising AI ethics: conducting socio-technical assessment2023In: Human-Centered Artificial Intelligence: Advanced Lectures / [ed] Mohamed Chetouani; Virginia Dignum; Paul Lukowicz; Carles Sierra, Springer, 2023, p. 304-321Conference paper (Refereed)
    Abstract [en]

    Several high profile incidents that involve Artificial Intelligence (AI) have captured public attention and increased demand for regulation. Low public trust and attitudes towards AI reinforce the need for concrete policy around its development and use. However, current guidelines and standards rolled out by institutions globally are considered by many as high-level and open to interpretation, making them difficult to put into practice. This paper presents ongoing research in the field of Responsible AI and explores numerous methods of operationalising AI ethics. If AI is to be effectively regulated, it must not be considered as a technology alone—AI is embedded in the fabric of our societies and should thus be treated as a socio-technical system, requiring multi-stakeholder involvement and employment of continuous value-based methods of assessment. When putting guidelines and standards into practice, context is of critical importance. The methods and frameworks presented in this paper emphasise this need and pave the way towards operational AI ethics.

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