Loading Now

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)

     Abstract of paper      PDF of paper


GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

Summary difficulty Written by Summary
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