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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 ...
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.
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 ...
Health Level Seven, abbreviated to HL7, is a range of global standards for the transfer of clinical and administrative health data between applications with the aim to improve patient outcomes and health system performance. The HL7 standards focus on the application layer, which is "layer 7" in the Open Systems Interconnection model.
The price transparency rule, which took effect January 2021, requires "a comprehensive machine-readable file with all standard charges established by the hospital for all the items and services it ...
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.
Weyl explains the goal is to create transparency about what social structures people are participating in, and about how “the algorithm is pushing them in a direction, so they have agency to ...
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 ...