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.
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- Prerequisites: master degree
- Price: free
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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?
This lecture ‘Machine-learning: Bias In, Bias Out’, is the first of
our monthly lecture series called ‘Sense & Sensibility of AI’,
developed by the Flemish AI Academy, with the support of its large
network, in 2021.
Teacher / speaker
Toon Calders
Toon Calders is professor at the computer science department of the University of Antwerp in Belgium. He is an active researcher in the area of data mining and machine learning.
He is editor of the data mining journal, and has been program chair of a number of data mining and machine learning conferences, including ECML/PKDD 2014 and Discovery Science 2016.
Toon Calders was one of the first researchers to study how to measure and avoid algorithmic bias in machine learning and is one of the editors of the book “Discrimination and Privacy in the Information Society – Data Mining and Profiling in Large Databases”, published by Springer in 2013.
He is currently leading a group of 6 researchers studying theoretical aspects of fairness in machine learning, as well as looking into practical use cases in collaboration with Flemish tax authorities, public welfare organizations, and an insurance company.
Sense & Sensibility of AI
AI has an increasing influence on our daily lives, examples include automated decision-making for high-stake decisions such as mortgages and loans, automated risk assessments for bail or recommenders on the internet. These AI systems carry the risk of creating filter bubbles and polarization. While AI is being rolled out into society, the discussion on how AI-based systems may align with and even affect our values, is pushed to the forefront. We gave the computer senses, but how can we give it sensibility? It requires a multi-disciplinary view, where both technical and non-technical perspectives have a prominent place.
In our lecture series ‘Sense & Sensibility of AI,’ we aim for Ph.D. Students to learn about the different aspects of Ethics in AI, not only to become aware of them but also to learn about the impact of AI on society and about methodologies to identify, assess, and possibly address ethical issues. The monthly seminars tackle subjects such as bias and fairness, privacy, trustworthiness, balancing technical, social, and regulatory perspectives.
The series is targeted towards doctoral students working in the broad field of AI and data science. To understand the lectures in full, it may be required to have a background in the technical aspects of AI/machine learning.
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Sense & Sensibility of AI is a seminar series developed by Flemish AI Academy in collaboration and with the support of all our partners, all universities in Flanders, and Knowledge Center Data & Society.
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