This invited paper reviews a framework to assist in mitigating societal risks that software can pose. This is to promote effective human oversight, which is a central requirement enforced by the European Union’s upcoming AI Act [29]. The paper advertises fragments of an upcoming journal publication [12], and as such is itself low in genuine originality. Yet it offers a specific perspective on that original work. Extrapolating earlier work on software doping, we report on the combination of established techniques for runtime monitoring and for probabilistic falsification to arrive at a black-box analysis technique for identifying undesired effects of software. We describe its application to high-risk systems that evaluate humans in a possibly unfair or discriminating way. The approach can assist humans-in-the-loop to make better informed and more responsible decisions. Our technical contribution is complemented by juridically, philosophically, and psychologically informed perspectives on the potential problems caused by AI systems.


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    Titel :

    Taming the AI Monster: Monitoring of Individual Fairness for Effective Human Oversight


    Weitere Titelangaben:

    Lect.Notes Computer


    Beteiligte:
    Neele, Thomas (Herausgeber:in) / Wijs, Anton (Herausgeber:in) / Baum, Kevin (Autor:in) / Biewer, Sebastian (Autor:in) / Hermanns, Holger (Autor:in) / Hetmank, Sven (Autor:in) / Langer, Markus (Autor:in) / Lauber-Rönsberg, Anne (Autor:in) / Sterz, Sarah (Autor:in)

    Kongress:

    International Symposium on Model Checking Software ; 2024 ; Luxembourg City, Luxembourg April 10, 2024 - April 11, 2024


    Erschienen in:

    Model Checking Software ; Kapitel : 1 ; 3-25


    Erscheinungsdatum :

    13.10.2024


    Format / Umfang :

    23 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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