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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 ...
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 ...
Lastly, the transparency principle states that a system's transparency is only necessary when there is a high risk of violating fundamental rights. As easily observed, the Brazilian Legal Framework for Artificial Intelligence lacks binding and obligatory clauses and is rather filled with relaxed guidelines.
The European Centre for Algorithmic Transparency was created to aid the enforcement of this. [12] A December 2020 Time article said that while many of its provisions only apply to platforms which have more than 45 million users in the European Union, the Act could have repercussions beyond Europe.
Published by Harvard University Press in 2016, The Black Box Society has six chapters, totalling 319 pages. In his review of the book for Business Ethics Quarterly, law professor Alan Rubel identifies Pasquale’s central thesis: the algorithms which control and monitor individual reputation, information seeking, and data retrieval in the search, reputation, and finance sectors embody "some ...
The standard provides guidelines for articulating transparency to authorities or end users and mitigating algorithmic biases. [ 170 ] [ 171 ] [ 173 ] Transparency and monitoring
Development of practical methodologies towards fair, transparent and accountable algorithmic approaches, with a focus on recommender systems and information retrieval. 3. Networking and community building. Sharing of knowledge and facilitation of discussions on algorithmic transparency with international stakeholders.