TED: How I'm fighting bias in algorithms | Joy Buolamwini

Joy introduces the concept of algorithmic bias, termed the 'coded gaze', and shares personal experiences with facial recognition software that failed to detect her face due to lack of diversity in training sets. She highlights the widespread impact of algorithmic bias, including its use in law enforcement and decision-making processes. Joy proposes solutions through the incoding movement, emphasizing the importance of diverse teams and inclusive coding practices. She invites viewers to join the Algorithmic Justice League to combat bias and promote fairness in technology.

What term does the speaker use to describe algorithmic bias?

• Digital Vision

• Coded Gaze

• Algorithmic View

• Machine Sight

What issue did the speaker face with facial recognition software?

• It required special hardware

• It was too expensive

• It was too slow

• It couldn't detect her face

Why is diversity important in training sets for facial recognition?

• To ensure all faces are recognized

• To improve speed

• To reduce costs

• To make the software user-friendly

What is a potential consequence of algorithmic bias in law enforcement?

• Higher policing costs

• Misidentification of suspects

• Faster response times

• Increased crime rates

What does the speaker suggest to combat algorithmic bias?

• Creating more inclusive training sets

• Limiting the use of technology

• Using more advanced hardware

• Developing faster algorithms

What is the purpose of the Algorithmic Justice League?

• To promote faster algorithms

• To fight against algorithmic bias

• To develop new software

• To train more coders

What are the three tenets of the incoding movement?

• Innovation, creativity, technology

• Speed, efficiency, cost

• Who codes, how we code, why we code

• Security, privacy, accessibility