Latest posts
- Bill of materials for responsible AI: collaborative labeling
- In our previous posts, we discussed how explainable AI is crucial to ensure the transparency and auditability of your AI deployments and how trustworthy AI adoption and its successful integration into our country’s critical infrastructure and systems are paramount. In this post, we dive into making trustworthy and responsible AI possible with Snorkel Flow,… ... - ICLR 2022 recap from Snorkel AI
- We are honored to be part of the International Conference on Learning Representations (ICLR) 2022, where Snorkel AI founders and researchers will be presenting five papers on data-centric AI topics The field of artificial intelligence moves fast! Hardly a month goes by without exciting new state-of-the-art techniques, results, datasets, and… ... - Explainability through provenance and lineage - In our previous post, we discussed how trustworthy AI adoption and its successful integration into our country’s critical infrastructure and systems are paramount. In this post, we discuss how explainability in AI is crucial to ensure the transparency and auditability of your AI deployments. Outputs from trustworthy AI applications must be explainable… ...
- Spring 2022 Snorkel Flow release roundup
- Latest features and platform improvements for Snorkel Flow 2022 is off to a strong start as we continue to make the benefits of data-centric AI more accessible to the enterprise. With this release, we’re further empowering AI/ML teams to drive rapid, analysis-driven training data iteration and development. Improvements include streamlined data… ... - Introduction to trustworthy AI - The adoption of trustworthy AI and its successful integration into our country’s most critical systems is paramount to achieving the goal of employing AI applications to accelerate economic prosperity and national security. However, traditional approaches to developing AI applications suffer from a critical flaw that leads to significant ethics and… ...
- How to better govern ML models? Hint: auditable training data
- ML models will always have some level of bias. Rather than relying on black-box algorithms, how can we make the entire AI development workflow more auditable? How do we build applications where bias can be easily detected and quickly managed? Today, most organizations focus their model governance efforts on investigating… ... - Algorithms that leverage data from other tasks with Chelsea Finn
- The Future of Data-Centric AI Talk Series Background Chelsea Finn is an assistant professor of computer science and electrical engineering at Stanford University, whose research has been widely recognized, including in the New York Times and MIT Technology Review. In this talk, Chelsea talks about algorithms that use data from… ... - Snorkel AI welcomes industry leaders to the team
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