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| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | Creation of a Platform to Compute the Trustworthiness Level of Unsupervised and Supervised ML/DL Models |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2023 |
| Abstract Text | AI has the potential to revolutionize industries and improve daily life through the development of advanced machine learning (ML) and deep learning (DL) models. These models, such as chatbots and language models, use algorithms or artificial neural networks to recognize patterns and make decisions. ML involves training algorithms on large datasets to identify patterns and make decisions, while DL uses artificial neural networks composed of interconnected nodes called artificial neurons to process and transmit information. Neural networks can learn and make decisions by adjusting the connections between neurons based on input data. There are two types of ML and DL: unsupervised and supervised. Unsupervised learning involves using algorithms or neural networks to learn from data without labeled outcomes, while supervised learning involves training algorithms or neural networks on labeled data to make predictions or decisions. As AI becomes more advanced and widespread, it is important to have confidence in the decisions and actions of these systems. Trusted AI refers to the reliability and ethical behavior of AI systems. It is crucial to have a framework for evaluating the trustworthiness of different AI models to ensure their safe and responsible deployment. A taxonomy of pillars and metrics can be used to quantify the trustworthiness of AI models, allowing for a structured and comprehensive evaluation of their strengths and limitations. The following bachelor thesis aims to survey existing platforms, define requirements and develop a web app that allows the computation of the trustscore, pillarscores, metricscores of supervised and unsupervised and DL platform is extended to allow for user management, and the return of the trustworthiness levels via API endpoints. |
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