How I'm fighting bias in algorithms | Joy Buolamwini

Summary

In this compelling talk, Joy Buolamwini, a poet of code, highlights the dangers of algorithmic bias, demonstrating how it can lead to exclusion and discrimination. Through personal anecdotes and examples, she shows how biased facial recognition software and machine learning systems disproportionately affect people of color. Buolamwini calls for more inclusive coding practices, emphasizing the importance of diverse teams, fair coding, and socially conscious technology. She introduces the 'incoding' movement, urging others to join her in creating a world where technology serves everyone equally, challenging the 'coded gaze' and striving for algorithmic justice.

Takeaways

• 😀 Algorithmic bias, like human bias, leads to unfairness and can spread rapidly through algorithms, impacting large groups.
• 😀 Algorithms can cause exclusionary experiences and discriminatory practices, especially when not trained inclusively.
• 😀 Facial recognition technology can struggle to detect faces that deviate from a narrow 'norm,' highlighting the issue of algorithmic bias.
• 😀 Inconsistent facial recognition can lead to real-world consequences, such as misidentifying criminal suspects or breaching civil liberties.
• 😀 Algorithmic bias can travel globally as quickly as downloading files, affecting systems and individuals in various locations.
• 😀 Data sets used to train algorithms must be diverse to avoid exclusion, and creating more inclusive training sets can mitigate bias.
• 😀 Facial recognition software is increasingly being used by law enforcement, raising concerns about unregulated use and fairness.
• 😀 Algorithms are increasingly used to make important decisions about hiring, loans, insurance, college admissions, and more.
• 😀 Machine learning can impact justice systems, with some judges using algorithmic risk scores to determine prison sentences.
• 😀 The 'incoding' movement focuses on creating inclusive code through diverse teams, fair coding practices, and prioritizing social change in technology development.
• 😀 We can fight algorithmic bias by building platforms to identify bias, creating inclusive training sets, and auditing existing software for fairness.

Q & A

What is 'the coded gaze' as mentioned in the transcript?

'The coded gaze' is the term used by Joy Buolamwini to describe algorithmic bias, which refers to the unfair and discriminatory practices caused by algorithms that fail to account for diversity in their data or training sets.

How does algorithmic bias impact the recognition of faces in facial recognition technology?

Algorithmic bias affects facial recognition technology because many training sets used to teach computers to recognize faces are not diverse enough. As a result, faces that deviate from the majority in terms of race, gender, or other characteristics are more difficult for these systems to detect, leading to misidentification and exclusion.

What specific problem did Joy Buolamwini experience when testing facial recognition software?

Joy Buolamwini experienced difficulty with facial recognition software failing to detect her face when she was not wearing a white mask. This highlighted the software's bias towards lighter skin tones and the lack of diversity in its training data.

What example from Joy's undergraduate experience illustrated the issue of algorithmic bias?

When Joy was working with a social robot that played peek-a-boo, the robot could not recognize her face, as it was trained on a limited set of faces. She had to borrow her roommate's face to complete the task, showing how algorithmic bias affected the functionality of technology.

What global realization did Joy have about algorithmic bias during her time in Hong Kong?

While in Hong Kong for an entrepreneurship competition, Joy realized that algorithmic bias could spread globally, as she encountered the same facial recognition software, which failed to detect her face even though it worked for others.

What are some potential negative consequences of algorithmic bias, as discussed in the script?

Algorithmic bias can lead to exclusionary experiences, misidentifications in law enforcement, and discriminatory practices. For example, misidentifying suspects, affecting civil liberties, and leading to unfair decision-making in areas like hiring, loans, insurance, and criminal sentencing.

How is facial recognition being used in law enforcement, and what concerns arise from it?

Facial recognition is increasingly being used by police departments to identify suspects. However, concerns arise because the algorithms are not always accurate, and they have not been thoroughly audited. The widespread use of this technology raises issues of privacy and potential civil rights violations.

What is a 'Weapons of Math Destruction' (WMD), and how does it relate to algorithmic bias?

'Weapons of Math Destruction' (WMDs) is a term used by data scientist Cathy O'Neil to describe harmful algorithms that are widespread, mysterious, and destructive. These algorithms, such as those used in predictive policing and sentencing, can perpetuate bias and unfair outcomes, affecting individuals' lives in significant ways.

What solutions does Joy Buolamwini propose to address algorithmic bias?

Joy suggests creating more inclusive coding practices, diverse teams, and better training sets. She also advocates for platforms that can identify bias, auditing existing software, and initiatives like the 'Selfies for Inclusion' campaign to help developers create better training sets.

What is the 'incoding' movement, and what are its core principles?

The 'incoding' movement promotes creating inclusive code by focusing on three principles: who codes, how we code, and why we code. It encourages the involvement of diverse individuals in coding, emphasizes fairness in development, and advocates for social change as a central goal of technology development.