About

Krzysztof Z Gajos is the Yahn W. Bernier and N. Elizabeth McCaw Professor of Computer Science at Harvard John A. Paulson School of Engineering and Applied Sciences. His primary teaching area is Computer Science. His research areas include Applied Mathematics, Artificial Intelligence, Human-Computer Interaction, and the intersection of Science, Technology, Innovation, and Public Policy. Gajos is involved in developing intelligent interactive systems and exploring the social impact of technology through multidisciplinary approaches. His work focuses on improving AI-based decision-making tools for public services and addressing the limitations of mental health chatbots within LGBTQ+ communities. He is based at Harvard's Allston campus and is actively engaged in advancing the understanding and application of AI and human-computer interaction to foster more caring and effective institutions.

Research topics

• Computer Science
• Artificial Intelligence
• Psychology
• Medicine
• Machine Learning
• Engineering
• Process management
• Nursing
• Cognitive psychology
• Applied psychology
• Risk analysis (engineering)
• Social psychology
• Knowledge management

Selected publications

• Funding AI for Good: A Call for Meaningful Engagement

  2026-04-13 · 1 citations

  articleOpen accessSenior author

  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 findings 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.

  PublisherDOI

• "It just requires so much more creativity": Barriers and Workarounds to Gathering Information for AI Contestation

  2026-04-13 · 1 citations

  articleOpen access

  Gathering information about AI systems is essential for contesting their use; it forms the basis of arguments about how and to what extent AI is causing harm. Information thus plays a central role for advocates like lawyers, journalists, and auditors contesting harmful AI systems. However, there is little systematic understanding of how these actors, many of whom are newly encountering AI in their advocacy work, access and use information effectively in this process. Understanding this information work can offer valuable insights for supporting effective contestation of harmful AI systems—work that is typically taken on by underresourced advocacy groups to begin with. To better understand information work in AI contestation, we interviewed 18 advocates in the United States (US) who have contested the use of AI in high-stakes domains, such as public benefits and housing. We characterize advocates’ strategies for accessing information that is useful for contestation, including a range of creative yet resource-intensive and risky workarounds that they use to overcome opacity. We discuss implications of our findings for the effectiveness of popular transparency policy strategies in the US and offer additional ways to support the social fabric that makes advocates’ information work effective.

  PublisherDOI

• Offline Reinforcement Learning for Adaptive Support in AI-Assisted Decision-Making

  ACM Transactions on Computer-Human Interaction · 2026-05-21

  articleSenior author

  AI decision-support tools typically offer a fixed type of assistance, like AI recommendations and explanations, regardless of the specific decision, individual, or broader context. This fixed design has been shown to hinder both human-AI decision accuracy and human skill improvement in the task. We posit that AI assistance needs to be dynamic, changing in response to contextual factors (e.g., AI uncertainty, task difficulty), individual differences, and specified objectives (e.g., decision accuracy, skill improvement). To enable such adaptive support, we propose reinforcement learning (RL) as a general approach for modeling human-AI decision-making to optimize human-AI interaction for diverse objectives. RL enables optimizing various objectives in AI-assisted decision-making by tailoring and adaptively providing decision support to humans — the right type of assistance, to the right person, at the right time. We instantiated our approach with two objectives: human-AI accuracy on the decision-making task and human skill improvement (i.e., learning about the task) and learned decision support policies from previous human-AI interaction data. We compared the optimized policies against several baselines in AI-assisted decision-making. Across two experiments (N = 316 and N = 964), our results consistently demonstrated that people interacting with policies optimized for accuracy achieve significantly higher accuracy — and even human-AI complementarity — compared to those interacting with any other type of AI support. Our results further indicated that human learning was more difficult to optimize than accuracy. While the policies learned the best available actions to optimize learning, participants who interacted with learning-optimized policies showed significant learning improvement only at times. Our research (1) demonstrates offline RL to be a promising approach to model the dynamics of human-AI decision-making, leading to policies that may optimize various objectives and provide novel insights about the AI-assisted decision-making space, and (2) emphasizes the importance of considering skill improvement and other human-centric objectives beyond accuracy in AI-assisted decision-making, opening up the novel research challenge of optimizing human-AI interaction for such objectives.

  PublisherDOI

• Toward accounting for the effects of gender socialization in quantitative research in human–computer interaction

  Interacting with Computers · 2025-06-12 · 1 citations

  articleOpen accessSenior author

  Abstract In quantitative HCI research, gender is typically represented as a single categorical variable and data from non-binary participants are frequently excluded from analyses. Meanwhile, many scholars argue that gender is a complex, multidimensional construct, and that overly simplistic operationalization of gender risks that our theories will generalize poorly have limited explanatory power, and will exclude experiences of individuals whose gender identities are not included in our analyses. In this work, we modeled gender as inclusive of multiple dimensions of gender socialization and we operationalized gender socialization through a subset of the items from the Conformity to Masculine Norms Inventory (CMNI). We replicated three studies of basic cognitive abilities (theory of mind, mental rotation, spatial working memory) that previously showed gender differences. For two of the studies, adding CMNI variables significantly and substantially improved the explanatory power of regression models. Also, in those studies, more than half of the effect of binary gender was mediated through the CMNI variables. These results suggest that gender socialization rather than categorical gender explain a substantial part of the individual differences on some cognitive tasks. Consequently, differences in task performance associated with gender categories may not be universal, i.e., they may not generalize to people from other cultures or eras where people are socialized into their gender roles differently. Instead, including multidimensional representations of gender may produce more accurate and more generalizable models. Given that our results also showed that CMNI might not model non-binary participants the same way as men and women, it remains an open question what specific instruments should be used to represent gender in quantitative analyses.

  PublisherDOI

• To Recommend or Not to Recommend: Designing and Evaluating AI-Enabled Decision Support for Time-Critical Medical Events

  Proceedings of the ACM on Human-Computer Interaction · 2025-10-16

  articleOpen access

  AI-enabled decision-support systems aim to help medical providers rapidly make decisions with limited information during medical emergencies. A critical challenge in developing these systems is supporting providers in interpreting the system output to make optimal treatment decisions. In this study, we designed and evaluated an AI-enabled decision-support system to aid providers in treating patients with traumatic injuries. We first conducted user research with physicians to identify and design information types and AI outputs for a decision-support display. We then conducted an online experiment with 35 medical providers from six health systems to evaluate two human-AI interaction strategies: (1) AI information synthesis and (2) AI information and recommendations. We found that providers were more likely to make correct decisions when AI information and recommendations were provided compared to receiving no AI support. We also identified two socio-technical barriers to providing AI recommendations during time-critical medical events: (1) an accuracy-time trade-off in providing recommendations and (2) polarizing perceptions of recommendations between providers. We discuss three implications for developing AI-enabled decision support used in time-critical events, contributing to the limited research on human-AI interaction in this context.

  PublisherDOI

• Tensions of Occupational Identity and Patterns of Identity Protection: Preliminary Insights on Generative AI in the Software Engineering Domain

  Journal of the Association for Information Systems · 2025-12-14

  article

  The adoption of Generative Artificial Intelligence (GenAI) in organizations calls into question implications for workers’ roles and domain expertise. While the impact of GenAI on human work is much debated, it is unclear how this paradigm shift is experienced by affected workers. In our qualitative study, we build on the theoretical lenses of occupational identity and self-determination theory to understand how and why software engineers make sense of GenAI for their own occupation. We find that engineers’ identity work, i.e., the process of dealing with the impact of GenAI for their own work, is contingent on domain experience: juniors and seniors felt their needs for competence, autonomy, and relatedness to be differently impacted by GenAI. We shed light on the importance of identity work’s role in preserving engineers’ tacit domain knowledge. We illustrate the organization’s role in further shaping engineers’ identity work.

  Publisher

• To Recommend or Not to Recommend: Designing and Evaluating AI-Enabled Decision Support for Time-Critical Medical Events

  ArXiv.org · 2025-05-17

  preprintOpen access

  AI-enabled decision-support systems aim to help medical providers rapidly make decisions with limited information during medical emergencies. A critical challenge in developing these systems is supporting providers in interpreting the system output to make optimal treatment decisions. In this study, we designed and evaluated an AI-enabled decision-support system to aid providers in treating patients with traumatic injuries. We first conducted user research with physicians to identify and design information types and AI outputs for a decision-support display. We then conducted an online experiment with 35 medical providers from six health systems to evaluate two human-AI interaction strategies: (1) AI information synthesis and (2) AI information and recommendations. We found that providers were more likely to make correct decisions when AI information and recommendations were provided compared to receiving no AI support. We also identified two socio-technical barriers to providing AI recommendations during time-critical medical events: (1) an accuracy-time trade-off in providing recommendations and (2) polarizing perceptions of recommendations between providers. We discuss three implications for developing AI-enabled decision support used in time-critical events, contributing to the limited research on human-AI interaction in this context.

  PublisherOA PDFDOI

• "ChatGPT, Don't Tell Me What to Do": Designing AI for Context Analysis in Humanitarian Frontline Negotiations

  2025-06-21

  articleOpen access
  PublisherDOI

• Bridging Ontologies of Neurological Conditions: Towards Patient-Centered Data Practices in Digital Phenotyping Research and Design

  Proceedings of the ACM on Human-Computer Interaction · 2025-10-16

  articleOpen access

  Amidst the increasing datafication of healthcare, deep digital phenotyping is being explored in clinical research to gather comprehensive data that can improve understanding of neurological conditions. However, participants currently do not have access to this data due to researchers' apprehension around whether such data is interpretable or useful. This study focuses on patient perspectives on the potential of deep digital phenotyping data to benefit people with neurodegenerative diseases, such as ataxias, Parkinson's disease, and multiple system atrophy. We present an interview study (n=12) to understand how people with these conditions currently track their symptoms and how they envision interacting with their deep digital phenotyping data. We describe how participants envision the utility of this deep digital phenotyping data in relation to multiple stages of disease and stakeholders, especially its potential to bridge different and sometimes conflicting understandings of their condition. Looking towards a future in which patients have increased agency over their data and can use it to inform their care, we contribute implications for shaping patient-driven clinical research practices and deep digital phenotyping tools that serve a multiplicity of patient needs.

  PublisherOA PDFDOI

• Personalising AI Assistance Based on Overreliance Rate in AI-Assisted Decision Making

  2025-03-19 · 14 citations

  article
  PublisherDOI

Recent grants

• NIH Grant R01ES017514

  NIH · $2.8M · 2016

• HCC: Medium: Improving Human-AI Collaboration on Decision-Making Tasks

  NSF · $1.2M · 2021–2025

Frequent coauthors

• Hanspeter Pfister

  Harvard University

  18 shared

• Bernd Huber

  Harvard University

  15 shared

• Anoopum S. Gupta

  Massachusetts General Hospital

  14 shared

• Daniel S. Weld

  Allen Institute

  14 shared

• Michelle A. Borkin

  Northeastern University

  14 shared

• Philip J. Guo

  University of California, San Diego

  11 shared

• Barbara J. Grosz

  10 shared

• Pao Siangliulue

  10 shared

Labs

• Intelligent Interactive SystemsPI