Netflix's documentary Coded Bias argues that machine-learning systems quietly absorb the sexism and racism of the society that builds them — and that facial recognition in the hands of governments and big tech raises serious privacy stakes. In this episode, drawn from Luiz Felipe Mendes' 2021 essay (updated for the GPT era), we walk through what the film gets right, where it oversimplifies, and why "the technology isn't evil — how we deploy it is" is the throughline. We also connect the film's warnings to today's large language models.

In this episode:

• Why AI isn't inherently evil, and how the same models that encode bias can be used to detect and reduce it
• The film's strengths: human stories, concrete real-world examples, and that it actually proposes solutions (regulation, not just alarm)
• Where it falls short: treating algorithms as sinister "entities," and the overstated "black box" framing
• Transparency vs. global interpretability vs. local interpretation — and the tools that make models explainable
• Regulation in practice: Brazil's LGPD and Europe's GDPR
• A 2023 update: how GPT-4, Bard, and other LLMs inherit the very biases the documentary warned about

Resources mentioned:

• Documentary: Coded Bias (2021)
• Bolukbasi et al., "Man Is to Computer Programmer as Woman Is to Homemaker?" — arxiv.org/abs/1607.06520
• "A Survey on Bias and Fairness in Machine Learning" — arxiv.org/abs/1908.09635
• Explainability tools: LIME (github.com/marcotcr/lime), SHAP (github.com/slundberg/shap), Shapash (maif.github.io/shapash)
• Christoph Molnar, Interpretable Machine Learning — christophm.github.io/interpretable-ml-book

Read the original post on Medium: medium.com/@lfomendes

GuaxiCast turns Luiz Felipe Mendes' essays on AI, data science, and building in public into short, honest conversations. Built with curiosity, shipped with ☕.