Algorithmic Bias and the Apple Card: Examining Gender Discrimination in Credit Scoring

The video discusses a controversy involving the Apple Card, where gender bias in credit limits was highlighted by a viral Twitter thread. The issue is linked to algorithmic bias, as historical data used in machine learning can perpetuate discrimination. The Equal Credit Opportunity Act of 1974 made gender-based credit discrimination illegal, but current algorithms may still be biased. Solutions include reintroducing gender variables into algorithms, but this poses legal challenges. The video suggests contacting policymakers to address these issues.

What was the main issue highlighted in David Heinemeier Hansson's Twitter thread regarding the Apple Card?

How did Apple respond to the concerns raised about the credit limits assigned to couples applying for the Apple Card?

What was the outcome of the investigation by the New York Department of Financial Services regarding Goldman Sachs?

What does the term 'algorithmic bias' refer to in the context of credit card applications?

What historical legislation was mentioned that aimed to prevent discrimination in credit lending?

In what ways can the introduction of explicit gender and racial variables into algorithms help combat bias?

What are some potential consequences of reintroducing gender into the credit card algorithm decision-making process?