Diving Deep Into Fair Synthetic Data Generation (Fairness Series Part 5)

In the previous part of this series, we have discussed two risks entailed in the rise of digitalization and artificial intelligence: the violation of the privacy and fairness of individuals. We have also outlined our approach to mitigate privacy and fairness risks with bias-corrected synthetic data: this allows for privacy-preserving data sharing and also aids […]

Tackling AI Bias At Its Source – With Fair Synthetic Data (Fairness Series Part 4)

In the age of digitalization and the rise of artificial intelligence, more and more tasks in public and private organizations are managed or supported by computers and machine-learning algorithms. These include tasks such as data analysis, automated decision making, customer interaction services such as automated emails or chatbots, and recommendation systems. In general, we believe […]

We Want Fair AI Algorithms – But How To Define Fairness? (Fairness Series Part 3)

“One of the major challenges in making algorithms fair lies in deciding what fairness actually means,” said Dr. Chris Russell, who is leading the safe and ethical AI group at the Alan Turing Institute, in an interview with Wired. “Trying to understand what fairness means, and when a particular approach is the right one to […]

Why Bias in AI is a Problem & Why Business Leaders Should Care (Fairness Series Part 1)

In November 2019 a Danish Apple Card user uncovered that Apple’s AI algorithm granted him 20 times the credit limit that his wife received. This disparity came as a major surprise as the couple shared assets and she actually had a higher credit score than he did. Apple and Goldman Sachs, who partnered on this […]