XAIR: A Framework of Explainable AI in Augmented Reality
We propose XAIR, a design framework that addresses when, what, and how to provide explanations of AI output in AR. The framework was based on a multi-disciplinary literature...
We propose XAIR, a design framework that addresses when, what, and how to provide explanations of AI output in AR. The framework was based on a multi-disciplinary literature...
From time to time, Meta invites academics to propose research in specific areas that align with our mission of building community and bringing the world closer together.
This work designs a method to estimate the item-level effects from the causal perspective. We resort to causal graphs to characterize the average treatment effect of...
The cumulative approach is currently unconventional, yet offers many favorable statistical properties, guaranteed via mathematical theory backed by rigorous proofs and...
We describe descriptive statistics related to how long engineers are able to focus. We find that at Meta, Engineers have a total of 14.25 hours of personal-focus time per week.
In this paper, we present our work on a code quality prediction framework, we call Automated Incremental Effort Investments (AIEI), to fasten the process of going from data...
In this paper, we present a real-world case study of an architectural refactoring project within an industrial setting. The system in scope is our codenamed ItemIndexer delivery...
What it’s like to be a parent on the Core Data Science team at Meta