Results for 'Jovana Obradović'

4 found
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  1. The algorithm audit: Scoring the algorithms that score us.Jovana Davidovic, Shea Brown & Ali Hasan - 2021 - Big Data and Society 8 (1).
    In recent years, the ethical impact of AI has been increasingly scrutinized, with public scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This has led to a growing mistrust of AI and increased calls for mandated ethical audits of algorithms. Current proposals for ethical assessment of algorithms are either too high level to be put into practice without further guidance, or they focus on very specific and technical notions of fairness or transparency that do not (...)
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  2. The International Rule of Law and Killing in War.Jovana Davidovic - 2012 - Social Theory and Practice 38 (3):531-553.
    In this paper, I suggest that for some proposed solutions to global justice problems, incompatibility with the necessary features of international law is a reason to reject them. I illustrate this by discussing the problem raised by the case of unjust combatants, that is, combatants lacking a just cause for war. I argue that the principle of inequality of combatants, which suggests that we ought to prohibit those without a just cause for war from fighting, is not only a bad (...)
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  3. A Framework for Assurance Audits of Algorithmic Systems.Benjamin Lange, Khoa Lam, Borhane Hamelin, Davidovic Jovana, Shea Brown & Ali Hasan - forthcoming - Proceedings of the 2024 Acm Conference on Fairness, Accountability, and Transparency.
    An increasing number of regulations propose the notion of ‘AI audits’ as an enforcement mechanism for achieving transparency and accountability for artificial intelligence (AI) systems. Despite some converging norms around various forms of AI auditing, auditing for the purpose of compliance and assurance currently have little to no agreed upon practices, procedures, taxonomies, and standards. We propose the ‘criterion audit’ as an operationalizable compliance and assurance external audit framework. We model elements of this approach after financial auditing practices, and argue (...)
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  4. Algorithmic Bias and Risk Assessments: Lessons from Practice.Ali Hasan, Shea Brown, Jovana Davidovic, Benjamin Lange & Mitt Regan - 2022 - Digital Society 1 (1):1-15.
    In this paper, we distinguish between different sorts of assessments of algorithmic systems, describe our process of assessing such systems for ethical risk, and share some key challenges and lessons for future algorithm assessments and audits. Given the distinctive nature and function of a third-party audit, and the uncertain and shifting regulatory landscape, we suggest that second-party assessments are currently the primary mechanisms for analyzing the social impacts of systems that incorporate artificial intelligence. We then discuss two kinds of as-sessments: (...)
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