Learn how you can contribute to Fairlearn and how to contribute.

In this podcast episode, we talked with Tamara Atanasoska about ​building fair AI systems.

About the Speaker:​Tamara works on ML explainability, interpretability and fairness as Open Source Software Engineer at probable. She is a maintainer of fairlearn, contributor to scikit-learn and skops. Tamara has both computer science/ software engineering and a computational linguistics(NLP) background.During the event, the guest discussed their career journey from software engineering to open-source contributions, focusing on explainability in AI through Scikit-learn and Fairlearn. They explored fairness in AI, including challenges in credit loans, hiring, and decision-making, and emphasized the importance of tools, human judgment, and collaboration. The guest also shared their involvement with PyLadies and encouraged contributions to Fairlearn. 00:00 Introduction to the event and the community 01:51 Topic introduction: Linguistic fairness and socio-technical perspectives in AI 02:37 Guest introduction: Tamara’s background and career 03:18 Tamara’s career journey: Software engineering, music tech, and computational linguistics 09:53 Tamara’s background in language and computer science 14:52 Exploring fairness in AI and its impact on society 21:20 Fairness in AI models26:21 Automating fairness analysis in models 32:32 Balancing technical and domain expertise in decision-making 37:13 The role of humans in the loop for fairness 40:02 Joining Probable and working on open-source projects 46:20 Scopes library and its integration with Hugging Face 50:48 PyLadies and community involvement 55:41 The ethos of Scikit-learn and Fairlearn

🔗 CONNECT WITH TAMARA ATANASOSKA Linkedin - https://www.linkedin.com/in/tamaraatanasoska GitHub- https://github.com/TamaraAtanasoska

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Scikit-learn is a popular machine learning library. It currently has over 200 estimators ready to use for a vast array of use cases. What if you are working on something special that still hasn't found its way into the library? Scikit-learn offers a way to write new compatible estimators, which can be seamlessly integrated with the rest of the library. We will look into what an estimator is, what API that scikit-learn estimators have, reasons why you would like to implement your own and an example of how to. We will end with real-world examples of how other OSS projects use this for their needs.

How would you model the mental hops that lead from one word to the next? And how about when instead of a word, the starting point are concepts grounded explicitly or implicitly in an image? These questions, and more, were the topic of my latest research project. Working to automatically generate image-term pairs for an image-grounded, collaborative Wordle game, I looked for combinations that spark the desired type of dialogue - illuminating the participants' decision-making. The project fits the broader efforts toward natural language explainability that Prof. Schlangen’s research group at the University of Potsdam is undertaking. We will look at the method I developed from an engineering perspective, going over all the NLP concepts composing it, and touch upon a bit of linguistics theory too. Level: Beginner to the domain (already familiar with Python)