Paul Tiwald has been part of the MOSTLY AI team since the beginning. He is the mastermind behind the team's research into fairness and the idea of fair synthetic data.
In this episode, you will hear about:
- what it's like to work in the field of artificial intelligence (spoiler: it's really fun!)
- how the idea of fair synthetic data came up
- how to create machine learning models that are private and fair by design
- why is it so challenging to remove bias from an algorithm
- what are proxy variables, and why are they dangerous
- what is the definition of fairness, and why do we need one in the first place
- how should companies start implementing fairness and ethical approaches into their AI development
- why it's impossible to fix bias without fair synthetic data and algorithmic fairness
Data Democratization Podcast: Stories about AI, Data, and Privacy