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The Pan-Canadian Artificial Intelligence Strategy (2017) is supported by federal funding of Can $125 million with the objectives of increasing the number of outstanding AI researchers and skilled graduates in Canada, establishing nodes of scientific excellence at the three major AI centres, developing 'global thought leadership' on the economic ...
Unless the weights and model architecture are released under free and open source licence, in which case only a training data summary and a copyright compliance policy are required, they are subject to transparency requirements. High-impact general-purpose AI systems including free and open source ones which could pose systemic risks (notably ...
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 ...
“These new requirements will be supported by greater transparency,” Young said, highlighting the agency reporting requirements. “AI presents not only risks, but also tremendous opportunity ...
Stanford University researchers issued a report on Wednesday measuring the transparency of artificial intelligence foundation models from companies like OpenAI and Google, and the authors urged ...
Marvin Minsky et al. raised the issue that AI can function as a form of surveillance, with the biases inherent in surveillance, suggesting HI (Humanistic Intelligence) as a way to create a more fair and balanced "human-in-the-loop" AI. [61] Explainable AI has been recently a new topic researched amongst the context of modern deep learning.
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 ...
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