Incorporating machine learning (ML) components into software products raises new
software-engineering challenges and exacerbates existing ones. Many researchers have …

Despite the growing consensus that stakeholders affected by AI systems should participate
in their design, enormous variation and implicit disagreements exist among current …

Machine learning models with high accuracy on test data can still produce systematic
failures, such as harmful biases and safety issues, when deployed in the real world. To …

An emerging body of research indicates that ineffective cross-functional collaboration–the
interdisciplinary work done by industry practitioners across roles–represents a major barrier …

Data scientists require rich mental models of how AI systems behave to effectively train,
debug, and work with them. Despite the prevalence of AI analysis tools, there is no general …

To design with AI models, user experience (UX) designers must assess the fit between the
model and user needs. Based on user research, they need to contextualize the model's …

Causal inference is a statistical paradigm for quantifying causal effects using observational
data. It is a complex process, requiring multiple steps, iterations, and collaborations with …

Computational notebooks, such as Jupyter Notebook, have become data scientists' de facto
programming environments. Many visualization researchers and practitioners have …

On-device machine learning (ML) promises to improve the privacy, responsiveness, and
proliferation of new, intelligent user experiences by moving ML computation onto everyday …

Data analysis is challenging as it requires synthesizing domain knowledge, statistical
expertise, and programming skills. Assistants powered by large language models (LLMs) …