In this podcast from the Carnegie Mellon University Software Engineering Institute, Carol Smith, a senior research scientist in human-machine interaction, and Jonathan Spring, a senior vulnerability researcher, discuss the hidden sources of bias in artificial intelligence (AI) systems and how systems developers can raise their awareness of bias, mitigate consequences, and reduce risks.
A Discussion on Automation with Watts Humphrey Award Winner Rajendra Prasad
Enabling Transition From Sustainment to Engineering Within the DoD
The Silver Thread of Cyber in the Global Supply Chain
Measuring DevSecOps: The Way Forward
My Story in Computing with Rachel Dzombak
Agile Strategic Planning: Concepts and Methods for Success
Applying Scientific Methods in Cybersecurity
Zero Trust Adoption: Benefits, Applications, and Resources
Uncertainty Quantification in Machine Learning: Measuring Confidence in Predictions
11 Rules for Ensuring a Security Model with AADL and Bell–LaPadula
Benefits and Challenges of Model-Based Systems Engineering
Fostering Diversity in Software Engineering
Can DevSecOps Make Developers Happier?
Is Your Organization Ready for AI?
My Story in Computing with Marisa Midler
Managing Vulnerabilities in Machine Learning and Artificial Intelligence Systems
AI Workforce Development
Moving from DevOps to DevSecOps
My Story in Computing with David Zubrow
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