Martin Hirzel- Fairness for Scikit-Learn Pipelines with Lale | PyData NYC 2022

PyData NYC 2022 is set to feature a talk by Martin Hirzel on Fairness for Scikit-Learn Pipelines with Lale. This talk is expected to delve into the important topic of fairness in machine learning and how it can be incorporated into the popular Scikit-Learn library using Lale.

Lale is a new open-source library that aims to make it easier to design and improve machine learning pipelines. It provides a way to define, optimize, and automate pipelines that include all aspects of machine learning, from data preprocessing to model selection and orchestration.

During the talk, Martin Hirzel, a senior researcher at IBM Research, is likely to discuss the challenges and complexities of ensuring fairness in machine learning models. As machine learning continues to be applied in various domains, including finance, healthcare, and criminal justice, the need for fair and unbiased models becomes increasingly crucial.

Martin Hirzel is expected to showcase how Lale can be used to incorporate fairness considerations into Scikit-Learn pipelines, allowing data scientists and machine learning engineers to develop models that take into account fairness constraints and societal impacts.

The talk is likely to be of interest to anyone working in the field of machine learning, as well as those who are concerned about the ethical implications of AI and data science. By attending the session, participants can gain valuable insights into the practical application of fairness principles in machine learning pipelines.

PyData NYC 2022 is a premier conference for data science and machine learning practitioners, and Martin Hirzel’s talk on fairness for Scikit-Learn pipelines with Lale promises to be a highlight of the event. With the growing importance of fairness and ethics in machine learning, this talk is not to be missed.

For more information about the speaker lineup and schedule for PyData NYC 2022, visit the official conference website.