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.
Incorporating Supply-Chain Risk and DevSecOps into a Cybersecurity Strategy
Software and Systems Collaboration in the Era of Smart Systems
Securing the Supply Chain for the Defense Industrial Base
Building on Ghidra: Tools for Automating Reverse Engineering and Malware Analysis
Envisioning the Future of Software Engineering
Implementing the DoD's Ethical AI Principles
Walking Fast Into the Future: Evolvable Technical Reference Frameworks for Mixed-Criticality Systems
Software Engineering for Machine Learning: Characterizing and Understanding Mismatch in ML Systems
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
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