Equidad2

Equity in Artificial Intelligence

Introduction to Equity in AI

• The video introduces the topic of equity within the context of artificial intelligence, focusing on how algorithmic biases can lead to discrimination.

• It aims to highlight the importance of applying equity as a principle in the design and evaluation of AI systems.

Understanding Equity vs. Equality

• Equity in AI seeks fair automated decisions for all individuals, acknowledging social, cultural, and gender differences.

• Unlike equality, which treats everyone the same, equity considers context and aims to eliminate biases that perpetuate discrimination.

Case Study: Gender Bias in Facial Recognition

• In 2018, researchers Joy Buolamwini and Timnit Gebru published a significant study titled "Gender Shades," analyzing facial recognition systems from Microsoft and IBM.

• They found high accuracy rates (99%) for white males but only 65% for dark-skinned females due to inadequate representation in training datasets.

Implications of Algorithmic Bias

• The study underscored the urgency of addressing algorithmic discrimination by applying equitable principles during training phases.

• It calls for a critical examination of perceived neutrality in algorithms since technology reflects human biases.

Designing Equitable AI Systems

• Implementing equity requires reviewing datasets, training processes, and evaluation criteria to ensure algorithmic justice.

• An equitable system acknowledges diversity and promotes active inclusion rather than uniformity; it serves as a tool for substantive equality by correcting historical inequalities.