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.
Four Types of Shift Left Testing
Capturing the Expertise of Cybersecurity Incident Handlers
Toward Speed and Simplicity: Creating a Software Library for Graph Analytics
Improving Quality Using Architecture Fault Analysis with Confidence Arguments
A Taxonomy of Testing Types
Reducing Complexity in Software & Systems
Designing Security Into Software-Reliant Systems
Agile Methods in Air Force Sustainment
Defect Prioritization With the Risk Priority Number
SEI-HCII Collaboration Explores Context-Aware Computing for Soldiers
An Introduction to Context-Aware Computing
Data Driven Software Assurance
Applying Agile in the DoD: Twelfth Principle
Supply Chain Risk Management: Managing Third Party and External Dependency Risk
Introduction to the Mission Thread Workshop
Applying Agile in the DoD: Eleventh Principle
A Workshop on Measuring What Matters
Applying Agile in the DoD: Tenth Principle
Predicting Software Assurance Using Quality and Reliability Measures
Applying Agile in the DoD: Ninth Principle
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