Algorithms at Scale: Raising Awareness on Latent Inequities in Our Data
summary
Claims that >90% of businesses have AI embedded and that AI drives revenue, cost and profitability are supported with a concrete pricing/IoT/edge use case and a cautionary patient-care example showing how historical data reproduces discrimination. Recommends specific mitigations: synthetic data to fill gaps, continuous enterprise-wide governance, independent validation, and cites DARPA’s effort toward explainable AI "third wave" models rather than vague promises about fairness.
AI-written summary · about
chapters (7)
- 0:00AI bias in critical decisions
- 3:57How biases enter AI algorithms
- 6:45Why AI bias is challenging
- 9:10Black box problem in AI explainability
- 12:40Best practices to reduce AI bias
- 17:36Global trustworthy AI efforts
- 20:46Q&A
related talks
11mAddressing Privacy, Bias, and Fairness in AI Systems
37mBuilding a case for Fair and bias-free AI
40mEthical AI: Addressing Bias & Algorithmic Fairness
14mData-Driven Transformation: Scaling Businesses with AI, Ethics, and Strategic Vision
47mEthics & AI: Identifying & preventing bias in predictive models
26m(Un)ethical Artificial Intelligence: How to Keep the AI Fair for Everyone
59mIn search of inclusive AI
23mDeveloping an intersectional framework to analyze biases in artificial intelligence and deep neural