Rsqrd is a community of AI builders [engineers, scientists, product managers, etc] who are committed to making AI technology robust & responsible. We regularly organize community events to share knowledge and collaborate on best practices for enterprise-scale AI development. Rsqrd brings together AI builders across AI-focused enterprises:

AI-focused enterprises


Discover what happened at past events:





AI and ML have at least one thing in common with traditional software systems: they all fail. AI failures might consist of discriminatory behavior, of privacy violations, or even security breaches that can lead to lawsuits, regulatory fines and more. What can organizations do to avoid these pitfalls? In this talk Patrick Hall will outline a new approach to “incident response” specifically tailored to AI and it will present a free and open sample AI incident response plan. Participants will leave understanding when and why AI creates liability for the organizations that employ it, and how organizations should react when their AI causes major problems.

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Can you do UX Research on machine learning systems? Can you get feedback from real users before the AI has been built? Can you even test an ML system before you have a production-ready model? Yes, yes, and yes! In this talk, Michelle Carney will share about how she combines her background in machine learning with her expertise in UX - including the MLUX meetup she organizes, her favorite resource the People + AI Research Guidebook, and a case study on her process of doing UXR for ML.

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It's extremely difficult to anticipate all the ways a user might want to interact with an open-ended conversational interface. The good news is that you don't have to. Conversational driven development is a framework that can be used to both improve the developer experience and create more inclusive, robust conversational interfaces. Join us with Rachael Tatman, Senior Developer Advocate @ Rasa, where we'll walk through the conversational driven development process and its benefits and drawbacks!

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Curating a dataset for ML applications involves decisions that are prone to subjectivity, which poses both ethical and technical issues. After creating datasets and running them through a model, there aren't many best practices on error analysis to better understand systematic behavior in NLP. Join us for a conversation on strategies for being a critical consumer and producer of datasets and operationalizing linguistically informed error analysis in various NLP applications!

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