Fairness in AI

Main challenges and how to tackle them

In collaboration with Data Science for Social Good Portugal, we are excited to announce a groundbreaking series of webinars focusing on AI topics that intersect with the greater social good. Our recent webinar, the second installment in this series, took place on the 29th of July and featured an insightful presentation by Francisca Morgado. She delved into a crucial topic in the AI landscape – “Fairness in AI.”

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If you could not secure a spot or missed the live session, fret not! You can access the entire webinar in the video below. Additionally, if you missed our previous talk on Geospatial Machine Learning, it’s not too late to catch up.

Why fairness in AI?

Throughout history, the notion of fairness has evolved, adapting to the changing dynamics of societies and cultures. We’ve witnessed numerous instances of unfairness that have left an indelible mark on human behavior. The question arises: Have these biases and inequities been inadvertently encoded into the AI technology we rely on today?

In our recent webinar, we embarked on a journey to dissect the concept of fairness within AI systems. We delved into thought-provoking questions such as “What exactly is fairness, and how can it be quantified?” We also explored the intricate ways algorithms can introduce biases and scrutinized the potential sources of bias lurking within datasets. Most importantly, we offered practical insights on how to steer clear of making unfair decisions within AI frameworks.

Course, Templates

Data Ignite

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This session provided attendees with a comprehensive understanding of fairness in AI and tangible examples to reinforce the key concepts discussed. As AI continues to shape our world, addressing fairness becomes paramount in ensuring that technology serves the greater good.

Slides are available here.


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