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Specifically, "algorithmic transparency" states that the inputs to the algorithm and the algorithm's use itself must be known, but they need not be fair. " Algorithmic accountability " implies that the organizations that use algorithms must be accountable for the decisions made by those algorithms, even though the decisions are being made by a ...
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT, formerly known as ACM FAT*) is a peer-reviewed academic conference series about ethics and computing systems. [1] Sponsored by the Association for Computing Machinery , this conference focuses on issues such as algorithmic transparency , fairness in machine learning , bias ...
New York University’s Information Law Institute hosted a conference on algorithmic accountability, noting: “Scholars, stakeholders, and policymakers question the adequacy of existing mechanisms governing algorithmic decision-making and grapple with new challenges presented by the rise of algorithmic power in terms of transparency, fairness ...
Algorithm certification involves auditing whether the algorithm used during the life cycle 1) conforms to the protocoled requirements (e.g., for correctness, completeness, consistency, and accuracy); 2) satisfies the standards, practices, and conventions; and 3) solves the right problem (e.g., correctly model physical laws), and satisfies the ...
Currently, a new IEEE standard is being drafted that aims to specify methodologies which help creators of algorithms eliminate issues of bias and articulate transparency (i.e. to authorities or end users) about the function and possible effects of their algorithms.
Algorithmic system inspections to support enforcement of the DSA. Technical tests on algorithmic systems to enhance the understanding of their functioning. Advice on procedures to secure data access to regulators and researchers. 2. Scientific research and foresight. Study of the short, mid and long-term societal impact of algorithmic systems.
In academic discourse, the usage of the term “black box” dates back to at least 1963 with Mario Bunge's work on a black box theory in mathematics. [18]The term “black box,” as used throughout The Black Box Society by author and law professor, Frank Pasquale, is a dual metaphor for a recording device such as a data-monitoring system and for a system whose inner workings are secret or ...
However, making code open source does not make it comprehensible, which by many definitions means that the AI code is not transparent. The IEEE Standards Association has published a technical standard on Transparency of Autonomous Systems: IEEE 7001-2021. [64] The IEEE effort identifies multiple scales of transparency for different stakeholders.
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