VernacuLab

About Services Capabilities Recently

The most pressing questions about AI are human. 
Our services focus on these factors, including:
Consulting
Research
Testing and Evaluation
AI Governance
Trustworthy AI
Risk Management
Operating and Monitoring AI in Deployment
Analysis of Online Narratives  
Markup of Human Dialogues with AI Chatbot
Feedback Loops in Technology Use
Securing Proprietary Content
AI Risk Management 
Proving AI Uses Cases
Experience and Expertise 
Resources
Advisory
Guidance
Recent Presentations
Video Presentations
American Bar Association President’s Task Force on Artificial Intelligence and the Law
National Academy of Sciences
Program Lead: NIST Assessing Risks and Impacts of AI (ARIA)
ARIA Program Webpage
The Assessing Risks and Impacts of AI (ARIA) Program Evaluation Design Document (2024)
The NIST Assessing Risks and Impacts of AI (ARIA) Pilot Evaluation Plan (2024)
(ARIA) Program Video
Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile, NIST Trustworthy and Responsible AI, National Institute of Standards and Technology, (2024)
Towards a Standard for Identifying and Managing Bias in Artificial Intelligence, Special Publication (NIST SP), National Institute of Standards and Technology, (2024)
Evaluation Programs
Documents
Podcast Appearances
Keynote Speaker, IEEE ProComm June 2024

Featured Speaker, Carnegie Mellon Convening on Operationalizing the NIST Risk Management Framework (July 2023)

Invited Workshop Presenter: Sociotechnical Approaches to Measurement and Validation for Safety in AI (July 2023)

Fireside Chat: Using the AI RMF, 2023 Insurance Public Policy Summit (May 2023)

Fireside Chat: Using AI RMF to manage the risks of Generative AI, Harvard Berkman Klein Center (May 2023)
Schwartz, R. (2024) Informing an Artificial Intelligence risk aware culture with the NIST AI Risk Management Framework. Chapter. Artificial Intelligence: Legal Issues, Policy, and Practical Strategies Edited by Cynthia H Cwik, Christopher A Suarez, and Lucy L Thomson. American Bar Association.

Slaughter, I., Greenberg, C., Schwartz, R., & Caliskan, A. (2023). Pre-trained Speech Processing Models Contain Human-Like Biases that Propagate to Speech Emotion Recognition. Conference on Empirical Methods in Natural Language Processing.
Recent Publications
Qin, H., Kong, J., Ding, W., Ahluwalia, R., El Morr, C., Engin, Z., Effoduh, J.O., Hwa, R., Guo, S.J., Seyyed-Kalantari, L., Muyingo, S.K., Moore, C.M., Parikh, R., Schwartz, R., Zhu, D., Wang, X., & Zhang, Y. (2023). Towards Trustworthy Artificial Intelligence for Equitable Global Health. ArXiv, abs/2309.05088.

Daniel Atherton, Reva Schwartz, Peter C. Fontana, Patrick Hall (2023) The Language of Trustworthy AI: An In-Depth Glossary of Terms. (National Institute of Standards and Technology, Gaithersburg, MD), NIST Artificial Intelligence AI 100-3

Gleaves, L.P., Schwartz, R., and Broniatowski, D.A. (2020) The Role of Individual User Differences in Interpretable and Explainable Machine Learning Systems, arXiv:2009.06675
Contact
Interested in working together? Fill out some info and we will be in touch shortly. We can’t wait to hear from you!