Ángel Alexander Cabrera

schoolCarnegie Mellon Universitybar_chart#2508 Global Rank

Total Papers

HCI Rank

#2508

Research Areas

Years Active

Recent Publications

Latest papers from Ángel Alexander Cabrera

CSCW '231st AuthorHuman AI Collaboration I

Improving Human-AI Collaboration With Descriptions of AI Behavior

People work with AI systems to improve their decision making, but often under- or over-rely on AI predictions and perform worse than they would have unassisted. To help people appropriately rely on AI aids, we propose showing them behavior descriptions, details of how AI systems perform on subgroups of instances. We t…

CHI '231st AuthorExplainable AI (XAI) +1

Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning

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 detect and mitigate such failures, practitioners run behavioral evaluation of their models, checking model outputs for specific types of inputs. B…

CHI '222nd AuthorExplainable AI (XAI) +3

Symphony: Composing Interactive Interfaces for Machine Learning

Interfaces for machine learning (ML), information and visualizations about models or data, can help practitioners build robust and responsible ML systems. Despite their benefits, recent studies of ML teams and our interviews with practitioners (n=9) showed that ML interfaces have limited adoption in practice. While ex…

CSCW '211st AuthorCrowds and Collaboration

Discovering and Validating AI Errors With Crowdsourced Failure Reports

AI systems can fail to learn important behaviors, leading to real-world issues like safety concerns and biases. Discovering these systematic failures often requires significant developer attention, from hypothesizing potential edge cases to collecting evidence and validating patterns. To scale and streamline this proc…

CSCW '203rd AuthorInterpreting and Explaining AI

Designing Alternative Representations of Confusion Matrices to Support Non-Expert Public Understanding of Algorithm Performance

Ensuring effective public understanding of algorithmic decisions that are powered by machine learning techniques has become an urgent task with the increasing deployment of AI systems into our society. In this work, we present a concrete step toward this goal by redesigning confusion matrices for binary classification…

Quick Actions

Share this page

ios_share

X in mail

https://hci.top/en/authors/7822?source=all