Building Ethical AI - Transparency, Bias Mitigation, and Trust

Learn to build trustworthy, human-oriented AI systems through this 26-minute conference talk that addresses the critical challenges of transparency, bias mitigation, and ethical development in artificial intelligence. Explore the fundamental importance of interpretable AI systems as their prevalence grows in everyday applications, understanding how lack of transparency leaves users without insight into decision-making processes. Discover practical strategies for identifying and mitigating bias, racism, and discrimination in AI models while implementing a human-centered approach that prioritizes ethical considerations throughout development. Examine real-world cases of AI bias and discrimination, gaining insights into how various tech professionals including software engineers, data engineers, and data scientists can collaborate to create fair and accountable systems. Master the principles of building user trust through transparent AI architectures and understand your responsibility as a technology professional to develop systems that positively impact society while preventing harmful outcomes.