PyData Zurich: ML Fairness and Tensor Type Annotations

## Details

After a long time of hibernation, the PyData Zurich meetup is finally back! We would like to rekindle the community and provide regular meetups with talks revolving around all things data in the Python ecosystem. This will be our second edition.

Schedule:

6:00 Entry

6:15 Start

6:20 Talk #1

6:50 Q&A

7:00 Talk #2

7:30 Q&A

7:40 Apero

Talk #1: How (Not) to Use Fairness Metrics in ML - Michèle Wieland
Machine learning is being used in critical decision-making processes, including lending, hiring and litigation. What makes machine learning so powerful is its ability to learn decision rules directly from training data and to apply those rules to make predictions. Therefore, if discriminatory biases are hidden in the training data, algorithms are likely to produce biased predictions. One approach to addressing the problem of bias and discrimination in ma chine learning involves using fairness metrics. This presentation provides an overview of how to apply fairness metrics in practice and highlights potential pitfalls to avoid when working with them.

Talk #2: Can We Squeeze() More Out of Python’s Type System? The Challenge of Tensor Shape Annotations - Eric Wolf
Ever confused NHWC with NCHW tensor shapes? Misaligned axes can turn debugging into a nightmare. In this talk, we will push Python's type system to its limits. We'll explore the new variadic generics, delve into tensor shape arithmetic, and see where Python falls short compared to other languages and libraries.