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.
SEI Fellows Series: Peter Feiler
NTP Best Practices
Establishing Trust in Disconnected Environments
Distributed Artificial Intelligence in Space
Verifying Distributed Adaptive Real-Time Systems
10 At-Risk Emerging Technologies
Technical Debt as a Core Software Engineering Practice
DNS Best Practices
Three Roles and Three Failure Patterns of Software Architects
Security Modeling Tools
Best Practices for Preventing and Responding to Distributed Denial of Service (DDoS) Attacks
Cyber Security Engineering for Software and Systems Assurance
Moving Target Defense
Improving Cybersecurity Through Cyber Intelligence
A Requirement Specification Language for AADL
Becoming a CISO: Formal and Informal Requirements
Predicting Quality Assurance with Software Metrics and Security Methods
Network Flow and Beyond
A Community College Curriculum for Secure Software Development
Security and the Internet of Things
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