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.
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
Mission-Based Prioritization: A New Method for Prioritizing Agile Backlogs
My Story in Computing with Carol Smith
Digital Engineering and DevSecOps
A 10-Step Framework for Managing Risk
7 Steps to Engineer Security into Ongoing and Future Container Adoption Efforts
Ransomware: Evolution, Rise, and Response
VINCE: A Software Vulnerability Coordination Platform
Work From Home: Threats, Vulnerabilities, and Strategies for Protecting Your Network
An Introduction to CMMC Assessment Guides
The CMMC Level 3 Assessment Guide: A Closer Look
The CMMC Level 1 Assessment Guide: A Closer Look
Achieving Continuous Authority to Operate (ATO)
Challenging the Myth of the 10x Programmer
A Stakeholder-Specific Approach to Vulnerability Management
Optimizing Process Maturity in CMMC Level 5
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