Summary of Developing Trustworthy Ai Applications with Foundation Models, by Michael Mock (1) et al.
Developing trustworthy AI applications with foundation models
by Michael Mock, Sebastian Schmidt, Felix Müller, Rebekka Görge, Anna Schmitz, Elena Haedecke, Angelika Voss, Dirk Hecker, Maximillian Poretschkin
First submitted to arxiv on: 8 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| 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 whitepaper proposes an application-specific, risk-based approach to evaluate and ensure the trustworthiness of artificial intelligence (AI) applications developed with foundation models. Foundation models in text, speech, and image processing offer new possibilities for developing AI applications, but their trustworthiness must be ensured. The approach is based on the ‘AI Assessment Catalog – Guideline for Trustworthy Artificial Intelligence’ by Fraunhofer IAIS, which takes into account specific risks of foundation models that can impact the AI application. The paper explains the relationship between foundation models and AI applications in terms of trustworthiness, introduces the technical construction of foundation models, shows how AI applications are developed based on them, and highlights the resulting risks regarding trustworthiness. Finally, it provides an overview of the expected requirements for AI applications and foundation models according to the draft of the European Union’s AI Regulation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This whitepaper helps ensure that AI applications using foundation models are trustworthy. Foundation models can process text, speech, or images, making them useful for developing AI applications. To make sure these applications are reliable, we need a way to test and evaluate their trustworthiness. This paper shows how to do just that by applying an approach developed for testing AI applications in general to the special case of foundation models. It explains what foundation models are, how they work, and how AI applications can be built on top of them. The paper also highlights some risks to consider when evaluating the trustworthiness of these applications. |
