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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 ...
The phrases "algorithmic transparency" and "algorithmic accountability" [2] are sometimes used interchangeably – especially since they were coined by the same people – but they have subtly different meanings. Specifically, "algorithmic transparency" states that the inputs to the algorithm and the algorithm's use itself must be known, but ...
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 ...
Bias, transparency, and ethics concerns have emerged with respect to the use of algorithms in diverse domains ranging from criminal justice [10] to healthcare [11] —many fear that artificial intelligence could replicate existing social inequalities along race, class, gender, and sexuality lines.
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 ...
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.
Problems in understanding, researching, and discovering algorithmic bias persist due to the proprietary nature of algorithms, which are typically treated as trade secrets. Even when full transparency is provided, the complexity of certain algorithms poses a barrier to understanding their functioning.
The ethics of artificial intelligence covers a broad range of topics within the field that are considered to have particular ethical stakes. [1] This includes algorithmic biases, fairness, [2] automated decision-making, accountability, privacy, and regulation.