Machine Learning: Bias In, Bias Out


Dr. Toon Calders (University of Antwerp) presents Machine Learning: Bias In, Bias Out. Artificial intelligence is more and more responsible for decisions that have a huge impact on our lives. But predictions made using data mining and algorithms can affect population subgroups differently. Academic researchers and journalists have shown that decisions taken by predictive algorithms sometimes lead to biased outcomes, reproducing inequalities already present in society. Is it possible to make a fairness-aware data mining process? Are algorithms biased because people are too? Or is it how machine learning works at the most fundamental level?

 
DateWednesday, July 8, 2020 - 11:00am to 12:00pm

Cost

Free

Website

https://wecount.inclusivedesign.ca/initiatives/

Location

ZOOM Webinar

This is a free Webinar brought to you as part of the We Count Digging DEEPer series.  Join Dr. Toon Calders (University of Antwerp) as he shares his work on bias in machine learning. Artificial intelligence is more and more responsible for decisions that have a huge impact on our lives. But predictions made using data mining and algorithms can affect population subgroups differently. Academic researchers and journalists have shown that decisions taken by predictive algorithms sometimes lead to biased outcomes, reproducing inequalities already present in society. Is it possible to make a fairness-aware data mining process? Are algorithms biased because people are too? Or is it how machine learning works at the most fundamental level?



Digging DEEPer Webinar. Machine Learning Discrimination: Bias in, bias out. Special guest speaker; Dr. Toon Calders
DateWednesday, July 8, 2020 - 11:00am to 12:00pm

Cost

Free

Website

https://wecount.inclusivedesign.ca/initiatives/

Location

ZOOM Webinar

We Count Logo
Wednesday, July 8, 2020 - 11:00am to 12:00pm

This is a free Webinar brought to you as part of the We Count Digging DEEPer series.  Join Dr. Toon Calders (University of Antwerp) as he shares his work on bias in machine learning. Artificial intelligence is more and more responsible for decisions that have a huge impact on our lives. But predictions made using data mining and algorithms can affect population subgroups differently. Academic researchers and journalists have shown that decisions taken by predictive algorithms sometimes lead to biased outcomes, reproducing inequalities already present in society. Is it possible to make a fairness-aware data mining process? Are algorithms biased because people are too? Or is it how machine learning works at the most fundamental level?

Venue & Address: 
ZOOM Webinar
Website: 
https://wecount.inclusivedesign.ca/initiatives/
Cost: 
Free
Digging DEEPer Webinar. Machine Learning Discrimination: Bias in, bias out. Special guest speaker; Dr. Toon Calders
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