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Trust me, I'm a Data Scientist

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Trust me, I'm a Data Scientist
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Ethics for builders of data-based applications
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132
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CC Attribution - NonCommercial - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
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Abstract
Data Science is gonna save the world, right? Or is it? Machine Learning epic fails are being largely commented. It's easy to convince ourselves that they are due to the inconsiderate misuse of Data Science. But is it really so? Is it possible that innocuous choices lead an honnest team to a disaster? During the course of this talk, we will build together an (imaginary) application: a disruptive AI-based smart virtual assistant, pledging to help high-schoolers with their university choice. We will see how unintended biaises may creep in at every step, even with the best of intentions. We will explore different topics, such as algorithmic fairness, model interpretability and the handling of minority classes. Through this practical example, this talk will present a review of major ethical pitfalls identified in the Machine Learning community along with suggestions on how to avoid them. This talk is intended for beginner to intermediate Data Scientists, and people working with Data Scientists, even without specific technical knowledge.
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Transcript: English(auto-generated)