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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 ...
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 ...
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.
The regulation of AI in China is mainly governed by the State Council of the People's Republic of China's July 8, 2017 "A Next Generation Artificial Intelligence Development Plan" (State Council Document No. 35), in which the Central Committee of the Chinese Communist Party and the State Council of the PRC urged the governing bodies of China 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 ...
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.
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.
These ideas led to the ACTUS proposal for a data standard alongside an algorithmic standard. Together, these can describe most financial instruments through 31 contract types or modular templates. The ACTUS Financial Research Foundation and the ACTUS Users Association develop the structure to implement the ideas.
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