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.

In this SEI Podcast in the “My Story in Computing” series, Rachel Dzombak discusses her journey integrating biomedical, mechanical, and civil engineering to her current leadership role at the SEI as digital transformation lead in artificial-intelligence (AI) engineering.

The rapid pace of change in software development, in business, and in the world has many organizations struggling to execute daily operations, wrangle big projects, and feel confident that there is a long-term strategy at play. Incorporating agile principles into strategic planning and execution is a highly effective way to drive strategy development, strategy execution, data-driven decision making, and results. In this SEI Podcast, Linda Parker Gates, initiative lead, Software Acquisition Pathways, and Suzanne Miller, principal researcher in the SEI's Software Solutions Division, discuss the principles of Agile Strategic Planning and methods for success.

In this SEI Podcast, Dr. Leigh Metcalf and Dr. Jonathan Spring, both researchers with the Carnegie Mellon University Software Engineering Institute's CERT Division, discuss the application of scientific methods to cybersecurity. As described in their recently published book, Using Science in Cybersecurity, Metcalf and Spring describe a common-sense approach and practical tools for applying scientific rigor to the field of cybersecurity.

Zero trust adoption is a security initiative that an enterprise must understand, interpret, and implement. Enterprise security initiatives are never simple, and their goal to improve cybersecurity posture requires the alignment of multiple stakeholders, systems, acquisitions, and exponentially changing technology. This alignment is always a complex undertaking and requires cybersecurity strategy and engineering to succeed. In this SEI Podcast, Geoff Sanders, a senior network defense analyst in the CERT Division at Carnegie Mellon University's Software Engineering Institute, discusses zero trust adoption and its benefits, applications, and available resources.

In this SEI Podcast, Dr. Eric Heim, a senior machine learning research scientist at Carnegie Mellon University's Software Engineering Institute (SEI), discusses the quantification of uncertainty in machine-learning (ML) systems. ML systems can make wrong predictions and give inaccurate estimates for the uncertainty of their predictions. It can be difficult to predict when their predictions will be wrong. Heim also discusses new techniques to quantify uncertainty, identify causes of uncertainty, and efficiently update ML models to reduce uncertainty in their predictions. The work of Heim and colleagues at the SEI Emerging Technology Center closes the gap between the scientific and mathematical advances from the ML research community and the practitioners who use the systems in real-life contexts, such as software engineers, software developers, data scientists, and system developers.

In this SEI Podcast, Aaron Greenhouse, a senior architecture researcher with Carnegie Mellon University's Software Engineering Institute, talks with principal researcher Suzanne Miller about use of the Bell–LaPadula mathematical security model in concert with the Architecture Analysis and Design Language (AADL) to model and validate confidentiality. Greenhouse and Miller also discuss 11 analysis rules that must be enforced over an AADL instance to ensure the consistency of a security model. Mapping Bell–LaPadula to AADL allows the expression of key concepts within the AADL model so that they can be analyzed automatically.

Nataliya (Natasha) Shevchenko and Mary Popeck, both senior researchers in the CERT Division at Carnegie Mellon University's Software Engineering Institute, discuss the use of model-based systems engineering (MBSE), which, in contrast to document-centric engineering, puts models at the center of system design. MBSE is used to support the requirements, design, analysis, verification, and validation associated with the development of complex systems.