Our Responsible AI Symposium brought together over 300 attendees from across the university and industry partners for two days of deep discussions on the future of ethical AI development.
Day 1: Setting the Stage
The symposium kicked off with a powerful keynote on "AI Governance in the Age of Large Language Models" that highlighted the growing need for frameworks that ensure AI systems are fair, transparent, and accountable.
The panel discussion on bias in machine learning featured perspectives from researchers, industry practitioners, and policy advocates. Key themes included the importance of diverse training data, the role of human oversight in AI decision-making, and the need for standardized auditing procedures.
Day 2: Hands-On Learning
The second day focused on practical workshops where participants got hands-on experience with fairness-aware ML tools. Teams worked on real datasets to identify and mitigate bias, learning techniques like adversarial debiasing, calibrated equalized odds, and fairness constraints in model training.
Student Hackathon Results
The 6-hour mini-hackathon challenged teams to build responsible AI prototypes. The winning team created an automated bias detection tool for hiring algorithms that evaluates candidate scoring models across multiple demographic dimensions.
Looking Ahead
The success of this symposium has reinforced our commitment to responsible computing education. We're already planning follow-up workshops and a semester-long reading group on AI ethics.