Developing an Intersectional Framework to Analyze Biases in Artificial Intelligence and Deep Neural

Explore an intersectional framework for analyzing biases in artificial intelligence and deep neural networks in this 23-minute conference talk. Delve into the complexities of AI and deep learning, examining how historical data can perpetuate discrimination and false correlations. Investigate the rise of deepfakes and their marginalizing effects, particularly on women. Learn about the importance of ethical tech and rigorous checks in AI development. Discover sources of bias, facial recognition challenges, data sourcing issues, and methods for retracing bias. Gain insights into a conceptual framework using synthetic data and critical theories, and understand the significance of contextual labelling. Examine labelling examples and discuss the ongoing challenges in creating unbiased AI systems.

Intro
Sources of Bias
Bias and Facial
Sourcing Data
Retracing Bias
Conceptual Framework using Synthetic Data and Critical Theories
Contextual Labelling
Labelling Examples
Challenges