The Algorithmic Transparency Requirement

Add to your list(s) Download to your calendar using vCal

• Adalbert Fono (LMU Munich)
• Thursday 11 April 2024, 14:00-15:00
• Centre for Mathematical Sciences, MR14.

If you have a question about this talk, please contact Matthew Colbrook.

Deep learning still has drawbacks in terms of trustworthiness, which describes a comprehensible, fair, safe, and reliable method. To mitigate the potential risk of AI, clear obligations associated to trustworthiness have been proposed via regulatory guidelines, e.g., in the European AI Act. Therefore, a central question is to what extent trustworthy deep learning can be realized. Establishing the described properties constituting trustworthiness requires that the factors influencing an algorithmic computation can be retraced, i.e., the algorithmic implementation is transparent. We derive a mathematical framework which enables us to analyze whether a transparent implementation in a computing model is feasible. Finally, we exemplarily apply our trustworthiness framework to analyze deep learning approaches for inverse problems in digital computing models represented by Turing machines.

This talk is part of the Applied and Computational Analysis series.

This talk is included in these lists:

• All CMS events
• All Talks (aka the CURE list)
• Applied and Computational Analysis
• CMS Events
• Centre for Mathematical Sciences, MR14
• DAMTP info aggregator
• Featured lists
• Interested Talks
• My seminars
• Type the title of a new list here
• bld31

Note that ex-directory lists are not shown.