Mechanisms and processes for operationalizing responsible ML
Making Data Science Work: Session 6
AI | We need to trust it now more than ever. But can we? by Yiannis Kanellopoulos, Code4Thought The Context # Human rights defenders across the world are fighting facial recognition surveillance more
As Machine Learning (ML) is being embedded into more services, the need to address broader business and social concerns about its use is growing. It is not enough to have an accurate model or at least having accuracy as its sole quality criterion. It is necessary for models to implement multi-dimensional quality criteria and be defensible with all stakeholders and build trust. ML models have to be both economically viable and FAccT (Fair, Accountable, Transparent). The terminology is new but not the need to defend models or to attest they can be trusted. Such requirements were present from the 70s for credit scoring models. What has changed is the scale and scope. In this session, we will explore:
Dr. Joshua Rubin, Data Scientist, Fiddler Labs, San Francisco
Dr. Yiannis Kanellopoulos, Founder, Code4Thought, Athens
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Registered participants can also leave comments and questions for the hosts and speakers, which will be taken up during the session.
Previous session: The previous session was held on 15 July. Summary of the session is available on https://hasgeek.com/fifthelephant/making-data-science-work-5/
and Indrayudh Ghoshal of Scribble Data will host the meetup. Scribble Data is a Bangalore/Toronto startup, active in the data community. Scribble is a MLOps product company whose main offering is ML Feature Store that increases trust and efficiency of production models.
About the curators: Suchana Seth, Venkata Pingali and Indrayudh Ghoshal of Scribble Data have curated this session. Suchana Seth is Fellow at Berkman Klein Center, Harvard University. Scribble Data is a Bangalore/Toronto startup, active in the data community.
About the series producer: The Fifth Elephant is platform for practitioners working with data (engineering, to application of data science for different use cases) to showcase their work and to collaborate.
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