Human-Centered AI
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.
Overview
Artificial intelligence is having a profound impact on all aspects of our lives. AI systems are being created that can drive our vehicles, design our drugs, determine what information we see, and even decide how our money is invested. Some of these systems operate entirely autonomously, while others only make recommendations or suggestions.
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.
Our work
- ResearchKim Martineau
Goal-oriented flow assist: supporting low code data flow automation with natural language
Technical noteKartik Talamadupula and Michelle BrachmanWhat is human-centered AI?
ExplainerWerner Geyer, Justin Weisz, Claudio Santos Pinhanez, and Elizabeth Daly6 minute readIBM’s Uncertainty Quantification 360 toolkit boosts trust in AI
ReleasePrasanna Sattigeri and Vera Liao7 minute readNew smartphone app to navigate blind people to stand in lines with distances
ResearchHironobu Takagi, Chieko Asakawa, Masaki Kuribayashi, and Seita Kayukawa3 minute readPushing the boundaries of human-AI interaction at IUI 2021
News9 minute read- See more of our work on Human-Centered AI
Projects
We're developing technological solutions to assist subject matter experts with their scientific workflows by enabling the Human-AI co-creation process.
Publications
Enhancing Study-Level Inference from Clinical Trial Papers via Reinforcement Learning-Based Numeric Reasoning
- Massimiliano Pronesti
- Michela Lorandi
- et al.
- 2025
- EMNLP 2025
Declarative Techniques for NL Queries over Heterogeneous Data
- Elham Khabiri
- Jeff Kephart
- et al.
- 2025
- EMNLP 2025
Classifier-Augmented Generation for Structured Workflow Prediction
- Thomas Gschwind
- Shramona Chakraborty
- et al.
- 2025
- EMNLP 2025
Highlight All the Phrases: Enhancing LLM Transparency through Visual Factuality Indicators
- Hyo Jin Do
- Rachel Ostrand
- et al.
- 2025
- AIES 2025
Localizing Persona Representations in LLMs
- 2025
- AIES 2025
Exposing AI Bias by Crowdsourcing: Democratizing Critique of Large Language Models
- Hangzhi Guo
- Pranav Venkit
- et al.
- 2025
- AIES 2025