AI systems are proliferating in everyday life, and it’s imperative to understand those systems from a human perspective. We design and investigate new forms of human-AI interactions and experiences that enhance and extend human capabilities for the good of our products, clients, and society at large.
Despite increasing levels of automation enabled by AI, the common thread to all of these systems is the human element: people are critical in the design, operation, and use of AI systems. We have a responsibility to ensure those systems operate transparently, act equitably, respect our privacy, and effectively serve people's needs.
How can we ensure that AI systems are designed responsibly and produce effective outcomes? We address this question by pursuing research projects across human-AI collaboration, responsible and human-compatible AI, as well as natural language and visual interaction systems.
Carl K. Chang, Paolo Ceravolo, et al.2021ICWS 2021
Rahul Nair, Massimiliano Mattetti, et al.2021IJCAI 2021
April Wang, Dakuo Wang, et al.2021IJCAI 2021
Eduard Dragut, Lucian Popa, et al.2021KDD 2021
Shubhi Asthana, Pawan Chowdhary, et al.2021KDD 2021
Dinesh Raghu, Atishya Jain, et al.2021ACL-IJCNLP 2021
Tools + code
AI FactSheets 360
Toolkit to create factsheets outlining the details about how an AI service operates, how it was trained and tested, its performance metrics, fairness and robustness checks, intended uses, maintenance, and other critical details.View project ↗
A chemically agnostic attention-guided reaction mapper.View project ↗
Learn + Play
A collection of browser-based games that explore key concepts behind IBM's AI research.View project ↗
AI Explainability 360
This open source toolkit contains eight algorithms that help you comprehend how machine-learning models predict labels throughout the AI application lifecycle. It’s designed to translate algorithmic research into the real-world use cases in a range of files, such as finance, human capital management, healthcare, and education.View project ↗
AI Model Explorer and Editor
An interactive tool for exploring and editing machine learning models. It uses sets of generated rules in order to create a model surrogate, which can then be edited and compared.View project ↗
An experiment of visual poetry generated by Artificial Intelligence.View project ↗
An experimental platform for to explore Automated AI. Includes the ability to automatically generate optimized machine learning pipelines for time series datasets.View project ↗