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

🔗 CONNECT WITH DataTalksClub

Join DataTalks.Club:⁠⁠https://datatalks.club/slack.html⁠⁠

Our events:⁠⁠https://datatalks.club/events.html⁠⁠

Datalike Substack -⁠⁠https://datalike.substack.com/⁠⁠

LinkedIn:⁠⁠  / datatalks-club