Abstract: What if the code you’ve written excluded 25% of your customers? Or what if it caused a group of people real-life harm? If you're a responsible engineer, these should be amongst of your biggest fears.
Artificial Intelligence and Machine Learning have introduced a new paradigm whereby code can do actual harm. While some of effects may be comically embarrassing - like a smart speaker not accounting for a regional accent; others can be deeply unfair to large groups of people.
Join Nivia (a life-long learner and Sr Engineering Manager at Spotify) and Andre (a polymath and Sr engineer at Venmo), on a journey to explore the world of machine learning and algorithmic bias. We'll explore how algorithms are created, trained and implemented; and more importantly, discuss practical steps to detect, mitigate and eliminate potential biases.
Learning Outcomes: - At the end of this session, the participant will be able to:
- 1. Understand the difference between Artificial Intelligence and Machine Learning (ML)
- 2. Understand the types of ML algorithms and their values
- 3. Understand the ways in which bias is introduced in the creation process
- 4. Have a sense of the consequences of these biases
- 5. Have practical means of preventing, mitigating and eliminating such biases
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