I'm a computer scientist / bioinformatician who has turned to be a core developer of
fairlearn, and work as a Machine Learning Engineer at Hugging Face. I'm also an organizer of PyData Berlin.
These days I mostly focus on aspects of machine learning and tools which help with creating more ethical and fair decision making systems. This trend has influenced me to work on
fairlearn, and to work on aspects of
scikit-learn which would help tools such as
fairlearn to work more fluently with the package; and at Hugging Face, my focus is to enable the community of these libraries to be able to share their models more easily and be more open about their work.
@adrinjalaliInstitute / Company –
Fairness, accountability, and transparency in machine learning have become a major part of the ML discourse. Since these issues have attracted attention from the public, and certain legislation are being put in place regulating the usage of machine learning in certain domains, the industry has been catching up with the topic and a few groups have been developing toolboxes to allow practitioners incorporate fairness constraints into their pipelines and make their models more transparent and accountable. Some examples are fairlearn, AIF360, LiFT, fairness-indicators (TF), ...
This talk explores some of the tools existing in this domain and discusses work being done in scikit-learn to make it easier for practitioners to adopt these tools.