Human-driven AI research that grows people and organizations, not replaces them.
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The Center for Human-Driven AI Research and Methods
The Center for Human-Driven AI Research and Methods (CHARM) is a multi-faculty center at the Harvard John A. Paulson School of Engineering and Applied Sciences. We focus on human–AI interaction design and computational methods that leaves people stronger, not just faster in the moment.
Why CHARM now?
AI is often framed as a race for speed: replace human labor with computation, produce more, finish faster. Yet this approach of optimizing for the immediate term risks losing out in the longer run.
At CHARM, we recognize that a single task is never the whole story. A great summary for a negotiator is just one step in their negotiation journey. A great data analysis for a patient is just one step in their health journey. A great writing support tool for a student is just one step of their learning journey. Each task is a moment in a longer human arc—learning, judgment, health, negotiation, creativity.
In addition to asking “Did the tool complete this step?” we also ask “Did the person leave stronger after using it?” or “Is the person better able to fulfill their needs and values?”
Our Mission & Strategy
Mission: To create AI tools that put people first: AI that expands human capability, leaving people better prepared for the next challenge.
Strategy: We make scientific contributions in human-computer interaction, visual computing, and machine learning to design tools that augment human capabilities and unlock what people and organizations can achieve. We move beyond proofs-of-concept to effective products by collaborating with institutions from around the world.
Who are we?
Faculty leading CHARM
CHARM brings together faculty from across Harvard SEAS, united by a focus on human-driven AI methods and interaction design.
Finale Doshi-Velez
Herchel Smith Professor of Computer Science
Reinforcement learning, AI in medicine, decision-making
Krzysztof Gajos
Gordon McKay professor of Computer Science
Human-computer interaction, accessible computing, intelligent interactive systems
Elena Glassman
Assistant Professor of Computer Science
AI-resilient interfaces, AI safety, human-computer interaction
Hanspeter Pfister
An Wang Professor of Computer Science
Visualization, computer graphics, computer vision
Announcements
Latest from CHARM
Barbara Grosz visits CHARM
We had the honor of hosting Barbara Grosz, Higgins Research Professor of Natural Sciences Emerita at CHARM on April 23rd, where CHARM members had the chance to discuss AI and its impact on society, plus how we can move forward using AI to aid us, while we as humans are still the decision-makers.
CHARM Welcomes three new members!
CHARM is excited to welcome Ruishi Zou, Chau Vu, and Jonas Raedler into our community as incoming PhD students. We can’t wait to share their work with you all!
CHARM members at CHI ’26
CHARM had multiple members papers accepted this year at CHI! One of our students, Hongjin Lin’s paper, “Funding AI for Good: A Call for Meaningful Engagement” can be read here, and all of our accepted papers can be seen on our research page. Congratulations to our accepted members!
Student Spotlight
Student Spotlight
Ziwei Gu on AI-Resiliency · May, 2026
As generative AI produces information—code, essays, analysis, videos—at an unprecedented scale and speed, we face a critical bottleneck: the human capacity to verify alignment and make sense of it all. To address this, Ziwei is moving the paradigm from “AI that generates” toward “AI that co-creates.” A key theme of his research is AI-resiliency, the design of interfaces that ensure potential AI errors or contextually inappropriate decisions are easily noticed, judged, and recovered from.
This vision has taken shape in his recent publications. In his CHI 2024 work, Ziwei proposes GP-TSM, a novel LLM-powered text rendering technique that improves reading efficiency while minimizing the risks of AI-generated summaries. He further expanded this to large-scale document analysis with AbstractExplorer (UIST 2025), which leverages LLMs and human cognitive theories to help users navigate patterns across massive text corpora without losing the context of individual documents.
Currently, Ziwei is rethinking the fundamental interfaces of human-AI interaction. He is developing generative controls to allow users to intuitively audit and refine AI outputs. He is also extending the AI-resiliency framework to autonomous agents. By designing mechanisms to help users anticipate the consequences of an agent’s actions before they are finalized, he aims to empower users to remain in control in increasingly complex, agent-driven workflows.
Recent Publications
BRIDGE: Borderless Reconfiguration for Inclusive and Diverse Gameplay Experience via Embodiment Transformation
Hayato Saiki, Chunggi Lee, Hikari Takahashi, Tica Lin, Hidetada Kishi, Kaori Tachibana, Yasuhiro Suzuki, Hanspeter Pfister, Kenji Suzuki
CHI ’26 Best Paper Award, April 13-17, 2026, Barcelona, Spain
Training resources for parasports are limited, reducing opportunities for athletes and coaches to engage with sport-specific movements and tactical coordination. To address this gap, we developed BRIDGE, a system that integrates a reconstruction pipeline, which detects and tracks players from broadcast video to generate 3D play sequences, with an embodiment-aware visualization framework that decomposes head, trunk, and wheelchair base orientations to represent attention, intent, and mobility. We evaluated BRIDGE in two controlled studies with 20 participants (10 national wheelchair basketball team players and 10 amateur players).The results showed that BRIDGE significantly enhanced the perceived naturalness of player postures and made tactical intentions easier to understand. In addition, it supported functional classification by realistically conveying players’ capabilities, which in turn improved participants’ sense of self-efficacy. This work advances inclusive sports learning and accessible coaching practices, contributing to more equitable access to tactical resources in parasports.
MnemoMaker: Creator, Curator, or Something Else? Exploring Human-AI Mnemonic Co-Creation
Olivia Seow, Elena Sajno, Dongho Shin, Pattie Maes, Samantha W. T. Chan
CHI ’26, April 13-17, 2026, Barcelona, Spain
MnemoMaker is an interactive system enabling learners to co-create personalized verbal and visual mnemonics with AI assistance. Through iterative design with 87 participants across three phases,we found that likeability strongly predicts perceived memorability(𝑅2 = .588, 𝑝 < .001), with effective mnemonics characterized as creative, imaginable, and emotionally engaging. Users reported strong ownership over AI-assisted creations (5.20/7) despite attributing them nearly equally to themselves and the system (3.54 on a 1-7scale from “system’s contributions” to “my contributions”). How people engage with AI, not just what they create, shapes their sense of agency. Our demo invites CHI attendees to create vocabulary mnemonics in any language and discover their collaboration style(e.g., as Creators, Curators, Negotiators, Acceptors, or something in between). A live dashboard visualizes collaboration patterns across all attendees, turning individual experiences into collective insight about human-AI co-creation.
Bayesian Inverse Transition Learning: Learning Dynamics From Near-Optimal Trajectories
Leo Benac, Abhishek Sharma, Sonali Parbhoo, Finale Doshi-Velez
AISTATS ’26, May 2-5, 2026, Tangier, Morocco
We consider the problem of estimating the transition dynamics T∗ from near-optimal expert trajectories in the context of offline model-based reinforcement learning. We develop a novel constraint-based method, Inverse Transition Learning, that treats the limited coverage of the expert trajectories as a feature: we use the fact that the expert is near-optimal to inform our estimate of T∗. We integrate our constraints into a Bayesian approach. Across both synthetic environments and real healthcare scenarios like Intensive Care Unit (ICU) patient management in hypotension, we demonstrate not only significant improvements in decision making, but that our posterior can inform when transfer will be successful.
A Paradigm for Creative Ownership
Tejaswi Polimetla, Katy Ilonka Gero, Elena L. Glassman
CHI ’26, April 13–17, 2026, Barcelona, Spain
As generative AI tools become embedded in creative practice, questions of ownership in co-creative contexts are pressing. Yet studies of human-AI collaboration often invoke “ownership” without definition: sometimes conflating it with other concepts, and other times leaving interpretation to participants. This inconsistency makes findings difficult to compare across or even within studies. We introduce a framework of creative ownership comprising three dimensions- Person, Process, and System- each with three subdimensions, offering a shared language for both system design and HCI research. In semi-structured interviews with 21 creative professionals, we found that participants’ initial references to ownership (e.g., embodiment, control, concept) were fully encompassed by the framework, demonstrating its coverage. Once introduced, however, they also articulated and prioritized the remaining subdimensions, underscoring how the framework expands reflection and enables richer insights. Our contributions include 1) the framework, 2) a web-based visualization tool, and 3) empirical findings on its utility.
Funding AI for Good: A Call for Meaningful Engagement
Lin, Hongjin; Kawakami, Anna; D’Ignazio, Catherine; Holstein, Kenneth; Gajos, Krzysztof Z
CHI ’26, April 13-17, 2026, Barcelona, Spain
Artificial Intelligence for Social Good (AI4SG) is a growing area that explores AI’s potential to address social issues, such as public health. Yet prior work has shown limited evidence of its tangible benefits for intended communities, and projects frequently face real-world deployment and sustainability challenges. While existing HCI literature on AI4SG initiatives primarily focuses on the mechanisms of funded projects and their outcomes, much less attention has been given to the upstream funding agendas that influence project approaches. In this work, we conducted a reflexive thematic analysis of 35 funding documents, representing about $410 million USD in total investments. We uncovered a spectrum of conceptual framings of AI4SG and the approaches that funding rhetoric promoted: from biasing towards technology capacities (more techno-centric) to emphasizing contextual understanding of the social problems at hand alongside technology capacities (more balanced). Drawing on our f indings on how funding documents construct AI4SG, we offer recommendations for funders to embed more balanced approaches in future funding call designs. We further discuss implications for how the HCI community can positively shape AI4SG funding design processes.
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