Empowering AI Innovation in Dallas: Responsible AI Development & Trustworthy AI Systems
Join ACM Dallas for a special session under the theme “Empowering AI Innovation in Dallas,” featuring two expert talks on responsible AI development and trustworthy AI systems in production.
This event brings together practical perspectives on how engineers, researchers, and technology professionals can use AI responsibly while maintaining strong engineering discipline, reliability, security, and human judgment.
Speaker 1: Ravi Kiran Pagidi Senior Data Engineer and AI/Data Systems Researcher
Topic: AI-Assisted Development Without Losing Engineering Discipline
Ravi’s talk will focus on how AI coding tools can help engineers move faster without replacing professional judgment. The session will cover responsible prompting, reviewing AI-generated code, avoiding subtle bugs, protecting sensitive data, and creating team norms around AI usage.
Speaker 2: Krishna Tirupati
Software Engineering Lead at Microsoft and Independent Researcher
Topic: From Overconfident LLMs to Trustworthy AI: Building Uncertainty-Aware Decision Systems in Production
This talk explores how to design trustworthy AI systems that understand uncertainty, reduce overconfidence, and support safer production decisions. Topics include hallucinations in LLMs, probabilistic deep learning, Monte Carlo Dropout, selective prediction, confidence-aware inference, human-in-the-loop workflows, MLOps monitoring, AI observability, and Responsible AI governance.
Key Takeaways:
Learn how to use AI coding assistants responsibly
Understand risks such as hallucinated APIs, hidden bugs, insecure patterns, and overconfidence
Explore uncertainty-aware AI architectures for production systems
Learn how human-in-the-loop design improves reliability and auditability
Understand how Responsible AI, MLOps, and observability support trustworthy AI systems
Gain practical ideas for applying AI safely in engineering and enterprise environments
Who Should Attend: Software engineers, data engineers, AI/ML professionals, cloud engineers, researchers, students, technical leaders, and anyone interested in practical and responsible AI adoption.