Summary of Mathematical Algorithm Design For Deep Learning Under Societal and Judicial Constraints: the Algorithmic Transparency Requirement, by Holger Boche et al.
Mathematical Algorithm Design for Deep Learning under Societal and Judicial Constraints: The Algorithmic Transparency Requirement
by Holger Boche, Adalbert Fono, Gitta Kutyniok
First submitted to arxiv on: 18 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Complexity (cs.CC)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper investigates the feasibility of trustworthy deep learning by developing a mathematical framework that enables transparent implementation in computing models. The authors aim to mitigate the risks associated with AI by ensuring clear obligations and regulatory guidelines are met. They apply their framework to analyze deep learning approaches for inverse problems in digital and analog computing models, concluding that Blum-Shub-Smale Machines have potential for trustworthy solvers under general conditions. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary AI can be trusted if algorithms are transparent and computations retraced. The European AI Act proposes regulatory guidelines. Researchers develop a mathematical framework for trustworthiness in deep learning. They apply it to inverse problems on digital (Turing) and analog (Blum-Shub-Smale) machines, finding Blum-Shub-Smale Machines can be trustworthy under general conditions. |
Keywords
* Artificial intelligence * Deep learning