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This lesson equips participants with trustworthy AI/ML practices, emphasizing fairness, explainability, reproducibility, accountability, and safety across three general data/model modalities: structured data (tabular), natural language processing (NLP), and computer vision. Participants will learn to evaluate and enhance the trustworthiness and reliability of models in each modality. Additionally, they will explore how to integrate these principles into future models, bridging ethical practices with practical applications in their research.
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- Participants should have experience using Python.
- Participants should have a basic understanding of machine learning (e.g., familiar with the concepts like train/test split and cross-validation) and should have trained at least one model in the past.
- Participants should have some preliminary experience (or at least exposure) to neural networks.
- Participants should care about the interpretability, reproducibility, and/or fairness of the models they build.
- Participants should have domain knowledge of the field they work in and want to build models for. ::::::::::::::::