Advancing Cutting-Edge Technology 

Bringing computational and citizen science/crowdsourcing research together has the potential to reshape our scientific capacity, leading to more accurate and timely discoveries, applications, and monitoring. Already, citizen scientists map biodiversity on iNaturalist, analyze protein folding configurations to advance drug discovery on Foldit, and determine antibiotic resistance through Bash the Bug. These users possess the knowledge, creativity, and skills that make up for what technology cannot do alone and push scientific research forward. 

Convergence offers a unique opportunity to do vital research into building trust, access, and transparency into AI systems by embedding them in real-world participatory contexts. Citizen science projects provide a built-in user base, ultimately making technology like AI safer and more trustworthy through large-scale experimentation. Key to the success of this venture is the breaking down of silos and increased knowledge-sharing between academia and industry — to the benefit of both.

Notably, these contributors can also enrich AI models used in research with contextual insights and provide essential edge-case observations that increase the validity of data, making the most of AI’s strengths while avoiding its downfalls. But their impact has broad applications across industry sectors. Citizen scientist/crowdsourced contributions can improve weather forecasting through on-the-ground reporting, for example; convergence thus opens the door to improving our natural resource management, agriculture, and more. By harnessing the strengths of citizen scientists, crowdsourcers, and computational technologies, we can accelerate the scientific process across disciplines, reliably produce world-leading innovations, and elevate American industry.

Creating a Tech-Minded Populace

Large-scale convergence also promotes the formation of a more technologically-literate society. To make convergence work on the participant side, non-expert users need to understand, in a simpler way, how advanced computing technology like AI works and how to use it. Having such exposure may encourage the use of such technologies in other aspects of life as well as introduce it to brand-new users. Removing the barrier for entry and the legitimizing of these tools through their very visible use in scientific research projects will make their benefits clear to everyday users. 

Read the Full Report

For a full picture of the impact of large-scale convergence, as well as key recommendations across sectors for how to make it a reality, we encourage all members of the computing community to read the Grand Challenges for the Convergence of Computational and Citizen Science Research report below.

Read the Full Report Here

Join the Community Chat

For another way to engage with the findings in this report, join us for an upcoming CCC Community Chat on May 20th, 2026 at 3:30pm. This 45-minute Zoom webinar, hosted by the report’s authors, will dive into key findings, follow up with a series of lightning talks with experts from citizen and computational science, and end with a Q&A. Featuring guest speaker Marc Kuchner, Citizen Science Officer at NASA!

Register and Submit Questions Here

Tune in to the CCC LinkedIn Showcase Page for updates and more reports like this. Stay connected with CCC for the latest insights, publications, and opportunities to engage by subscribing here.

The efforts of citizen scientists have enabled research projects across scientific disciplines. From users mapping biodiversity on

Join us for the second-ever CCC Community Chat: A New Era of Scientific Progress: Uniting Advanced Computing and Citizen Science for Accelerated Research. During this 45-minute webinar, the authors of the CCC Grand Challenges for the Convergence of Citizen Science and Computing Research report will address the strategic value of bringing citizen science research and advanced computing together and how the two might mutually enrich each other. This large-scale convergence promises enhanced results for both disciplines and has the potential to fundamentally reshape how we conduct scientific research. In doing so, it could usher in a new era of scientific progress.

Join for a Live Q&A with the Report’s Authors and Guest Marc Kuchner (NASA)

For this special event, we’re excited to announce guest speaker Marc Kuchner, Citizen Science Officer at NASA. He will join the webinar for a short lightning talk on the citizen science projects underway at NASA that are enhancing our understanding of the universe.

Our second lightning talk will be given by Tanya Berger-Wolf (The Ohio State University), also a lead author on the report. Tanya will present about how computing research converges with citizen science research in conservation biology projects.

Hear key findings from the report, including:

• Critical opportunities for human-centered computing research
• The ways technologies like AI and drones can augment citizen science projects
• How convergence is key to addressing pressing national priorities
• What infrastructural changes are needed to make large-scale convergence happen

At the end of the webinar, Marc and Tanya, along with the report’s other lead authors — including Lucy Fortson (University of Minnesota), Lea Shanley (International Computer Science Institute and GNIES, University of Wisconsin-Madison), Kevin Crowston (Syracuse University), Corey Jackson (University of Wisconsin-Madison), Saiph Savage (Northeastern University) — will be happy to answer any questions during a live Q&A. 

Register Now!

Event Date: Wednesday, May 20, 2026

Event Time: 3:30pm ET

We hope you will join us for this amazing opportunity to delve deeper into the future of human-centered computing. Please use the link below to register for the webinar, and feel free to submit some questions ahead of time.

Register Here

Tune in to the CCC LinkedIn Showcase Page for updates and more Community Chats like this. Stay connected with CCC for the latest insights, publications, and opportunities to engage by subscribing here.

To address this growing challenge, the U.S. Center for AI Standards and Innovation (CAISI), housed within the National Institute of Standards and Technology (NIST) at the Department of Commerce, released a Request for Information (RFI) on practices and methodologies for measuring and improving the secure development and deployment of AI agent systems. The Computing Community Consortium (CCC) and Computing Research Association (CRA) recently submitted a response to this RFI, providing key insights and recommendations for the future of AI security.

Identifying New Roadblocks

AI agent systems pose unique challenges when it comes to security threats and vulnerabilities. One of the most significant difficulties is precisely what can make them so useful: their ability to adapt and learn. This quality means they are inherently more unpredictable, which can make debugging the system more difficult and can lead to problematic actions created by code errors quickly escalating.

Some of the other questions current security models are grappling with include:

• How to delegate credentials to an AI agent
• How to identify risk levels around highly context-dependent and unpredictable AI agents and their users
• How to determine accountability for security failures
• How to avoid cascade effects when one compromised agent taints the decision-making of downstream agents

The trend towards replacing human workers with AI programs in activities like coding poses its own particular security risk. Humans who understand the underlying systems behind AI agents, how they function, and how they generate their output are essential to tackling emerging threats. To that end, this new RFI recommends that agencies like NIST set expectations around terms like “human-in-the-loop” practices, building on the NIST AI Risk Management Framework and Playbook to provide implementation guidance.

Fostering Security in the Age of AI

Other ways to increase the security of AI agents include fundamentally reevaluating how we approach typical cybersecurity. Some first approaches suggested in this RFI include:

• Examining other periods of major leaps in abstraction in computing for applicable lessons
• Reevaluating the concepts of fuzz testing and input sanitization failures in the dynamic context of autonomous agents
• Looking closely at the extent to which AI agents are capable of policing other AI agents
• Assessing the particular risks of multi-agent systems, especially when the different agents have differing goals
• Investment in research looking for effective guardrails that could be embedded within AI frameworks or could withstand assaults from such systems
• Targeting research efforts on assessing if a human or another AI agent is interacting an AI agent
• Forging stronger connections between the computing research community and industry cybersecurity practitioners

Historically, much of cybersecurity takes place after the design or deployment of an application. This RFI recommends a fundamental shift away from this practice, encouraging NIST to create incentives for industry to make security by design in AI systems rather than a second thought.

Read the Full Response

For the full scope of CCC and CRA findings and analysis on advancing the security of AI, access the full response below. You can also see more CCC responses to the community here.

Read the Full RFI Response Here

Please note any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the authors’ affiliations, or of the National Science Foundation, which funds CCC.

From users mapping biodiversity on iNaturalist to analyzing protein folding configurations to advance drug discovery on Foldit, we have seen the value of engaging the public in scientific research projects. This report also presents the opportunity of citizen science and computing research, together, to maximize the potential of tools with broad applications like artificial intelligence (AI). On a large scale, such convergence is capable of directly addressing some of our most pressing national challenges and priorities.

Supporting National Interests

The impact of the convergence of citizen science with advanced computing aligns directly with a wide array of national priorities:

• It would enhance national competitiveness due to the increased productivity, agility, and reach of scientific disciplines across the board.

• The extensive volunteer efforts involved in convergence would likewise increase economic value and increase government efficiency by reducing the duplication of efforts, streamlining workflows, and lowering the cost of data collection and analysis across state and federal agencies.

• Citizen science offers incredible workforce skills development for those who participate, enhancing scientific literacy among the population as well as serving as accessible entry-points for STEM careers.

• Large-scale convergence requires investment in robust infrastructure that may help address local, state, federal, or even international scientific challenges, including security and privacy concerns.

• An increase in convergence harnesses public data in a way that enhances the credibility of scientific research, accessing perspectives and local knowledge that traditional scientific research methods may overlook as well as emphasizing appropriate data sharing and reuse.

• Participation in citizen science establishes a sense of civic identity and public agency by offering participants a feeling of ownership over their contributions and the eventual results and innovations they enable.
  

• Data collected by citizen scientists is especially valuable in improving natural disaster response, where individuals can provide fast and specific data points about the impacts of events like hurricanes, earthquakes, or tornadoes.

Developing Critical Infrastructure

Large-scale convergence requires investment and coordination to reach its full potential. Workshop attendees crafted a strategic roadmap for the sustained platforms, governance systems, and physical/cyber architecture required to support scalable, trustworthy, and nationwide convergence efforts:

• Encourage and incentivize cross-agency collaboration: Break down silos by integrating citizen science and crowdsourcing with government agency AI, cloud, and technology strategies.

• Create permanent federal funding streams for convergence infrastructure: Ensure stable funding for long-term platform and infrastructure maintenance and evolution.

• Build interoperable and AI-ready data infrastructure for participatory sciences: Define national standards (FAIR data, APIs, metadata) for data exchange across platforms and projects.

• Develop scalable provenance frameworks for participatory AI systems: Fund research on lightweight, privacy-aware provenance capture that spans human actions, AI model evolution, and distributed execution, enabling reproducibility, auditing, and long-term reuse at the national scale.

• Invest in privacy and security-preserving data frameworks: Implement and evaluate mechanisms (e.g., federated learning, data trusts, open standards) that are core to participatory science infrastructure, and formalize the necessary governance and legal structures for secure data use.

• Establish national guidance for explainable, transparent, and trustworthy AI in citizen science: Develop research-backed guidelines for interpretable outputs, community consent, and auditability.

• Develop next-generation participatory AI governance: Co-create frameworks that give participatory science communities real agency over AI deployment decisions, data use, and model evolution.

• Launch a National Citizen Science & AI Convergence Hub: Establish a central, virtual hub to share tools, standards, best practices, case studies, and training materials, thus reducing duplication and accelerating adoption.

Read the Full Report

For a full picture of the impact of large-scale convergence, as well as key recommendations across sectors for how to make it a reality, we encourage you to read the Grand Challenges for the Convergence of Computational and Citizen Science Research report below.

Read the Full Report Here

Join the Community Chat

For another way to engage with the findings in this report, join us for an upcoming CCC Community Chat on May 20th, 2026 at 3:30pm. This 45-minute Zoom webinar, hosted by the report’s authors, will dive into key findings, follow up with a series of lightning talks with experts from citizen and computational science, and end with a Q&A. Key speakers to be announced shortly!

Register and submit your questions here.

Tune in to the CCC LinkedIn Showcase Page for updates and more reports like this. Stay connected with CCC for the latest insights, publications, and opportunities to engage by subscribing here.

To help guide senior researchers in this field on how to lead in their labs and on campus, the Computing Community Consortium (CCC) has published a new brief: Leading and Mentoring in Research Supporting At-Risk Users. 

Fostering Research Skills

At the core of a successful research project is the skill of the researchers conducting it. Leading and Mentoring in Research Supporting At-Risk Users reiterates the importance of helping team members develop rigorous research abilities, complementing the respect for and connection to at-risk populations they often already bring to the table. To do so, it is crucial to foster low-stakes learning environments — not only when exposing them to more responsibility in the lab, but when practicing foundational skills.

Crafting Support Plans

This brief also emphasizes the importance of being both proactive and collaborative when it comes to setting up support plans for SARU research teams. Support plans should be in place from the get-go, rather than developed in response to incidents, and should be adapted as the research project evolves. Other ways research leaders can support their team members include:

• Ensuring each team member has a professional support system outside of their supervisor
• Encouraging students to set work-life boundaries and seek mental health support when needed
• Setting expectations around research timelines
• Helping team members handle risks like harassment or doxxing
• Leading by example 

Building Community

Finally, research teams — and projects more broadly — often benefit significantly from collaboration beyond the lab. Interaction with experts in complementary disciplines can be particularly valuable in SARU research, where diverse expertise, resources, and institutional support can be leveraged to tackle complex problems. To do so, this new brief highlights the value of workshops and asynchronous groups that build community across labs and institutions. They have the additional secondary benefit of providing professional pathways for early-career researchers, which an effective mentor can help build.

Thoughtful leadership and mentorship in SARU research spaces helps set early-career researchers up for long-term success. In doing so, it also creates an increasingly impactful next generation capable of instigating and executing change-making research in the future.

View the SARU Leadership and Mentorship Brief Here

Tune in to the CCC LinkedIn Showcase Page for updates and more resources like this. Stay connected with CCC for the latest insights, publications, and opportunities to engage by subscribing here.

Call for Papers: ACM International Conference on Multimodal Interaction

Track: Blue Sky Track (A.K.A. Vision Track)

Date: November 12-15, 2026

Location: Shenyang, China

Website: http://icdm2026.neu.edu.cn/main.htm

Aims and Scope

The Blue Sky Track invites the research community to present visionary propositions that could inspire impactful directions and compelling new research opportunities for the ICDM community within the foreseeable future. Unlike traditional research papers, these submissions should:

• Expose gaps in current data mining research
• Propose bold, forward-looking ideas that tackle fundamental challenges
• Introduce unprecedented algorithmic or methodological opportunities
• Question prevailing assumptions in the community

The goal is to stimulate innovation and foster new research trajectories that will result in paradigm changes and enable a “quantum leap” in knowledge.

What is a Blue Sky Paper?

A Blue Sky paper presents a bold vision with high potential to expand the current ICDM research agenda by defining new research topics and transformative problem areas. Authors are encouraged to look beyond existing methodologies and technologies to present “out-of-the-box” thoughts.

What is NOT a Blue Sky Paper?

• Work proposing slightly better solutions to well-studied problems.
• Papers dominated by a specific solution with limited discussion on broader future impact.
• Applications of existing techniques to a specific domain without significant novel challenges for the ICDM community.

Why Submit?

Shape the Future: Influence long-term research visions and trajectories in the data mining community.

High Visibility: Accepted papers will appear in the conference workshop proceedings.

Recognition: Best submissions will be considered for awards sponsored by the CCC.

Submission Guidelines

Length: Limited to 4 pages, excluding references.

Format: IEEE 2-column format (see IEEE Templates).

Review Process: Single-blind; submissions must include author names, affiliations, and email addresses.

Submission Link: Submit via CyberChair.

Originality: Papers must not have been published elsewhere or be under consideration at other venues.

Important Dates

Submission Deadline: July 1st, 2026 (AoE)

Notification Date: August 30, 2026

Camera-Ready Deadline: September 15, 2026

Conference Dates: November 12-15, 2026 (in-person presentation required)

Presentation and Awards

At least one author of each accepted paper must complete registration and present the paper in person to be included in the proceedings and program. Remote presentations are not permitted.

With generous support from the Computing Community Consortium (CCC), the best Blue Sky papers will receive awards in the form of travel grants:

• First Prize: $1,000
• Second Prize: $750
• Third Prize: $500

Questions?

For questions regarding submissions, please email the track co-chairs with the subject “ICDM 2026 BlueSky.”

BlueSky Track Co-Chairs:

Jipeng Qiang, Yangzhou University (jpqiang@yzu.edu.cn)

Mengke Li, Shenzhen University (mengkeli@szu.edu.cn)

The Computing Community Consortium has released a new brief, titled The Problem of Problem Selection, to help researchers in the beginning of their journey into SARU research. This new brief is an outcome of the December 2024 visioning workshop Supporting At-Risk Users Through Responsible Computing. The workshop convened 49 researchers and practitioners from academia, industry, and civil society who work directly on issues affecting at-risk users of technology, discussing how to build a more rigorous and coordinated research agenda in this space.

Selecting a Research Topic

This brief is intended to assist all SARU researchers, from the student level to senior researchers. It proposes a thoughtful return to the fundamentals of problem selection, one that reminds us that research isn’t always the right solution to a problem. Rather, the goal of topic selection is to “pick the right problem at the right time.” One must evaluate how research itself will contribute to a desired outcome as well as who stands to benefit from that outcome. 

Many SARU researchers are also drawn to research problems because of a personal connection. This brief highlights the advantages of such connections as well as the challenges they present, reminding researchers that it is necessary to strike a balance between personal investment and disinterested perspective.  

Assessing SARU Research Risks

Beyond these guiding principles, the authors provide a comprehensive reference framework for assessing the risks of potential SARU research questions. Their Socio-Ecological Process Model poses specific questions that encourage researchers to consider the resources required to research their topic from all of the following areas:

• Individuals
• Labs
• Institutions
• Research Communities
• Policy, Funding, and Advocacy

These questions span the entire research life cycle (ideation, study design, study execution, paper-writing, and post-publication publicity). For instance, in the ideation phase, it asks what challenges will be present for individual researchers immersing themselves in the proposed work. And in the publicity phase, it asks what institutional supports are in place to handle potential media attention — both positive and negative.

By considering the risks of SARU research from all angles, researchers can approach their topics both more prepared to mitigate or eliminate them and more certain that their research is ultimately beneficial to affected communities.

Read the SARU Research Problem Selection Brief Here

Tune in to the CCC LinkedIn Showcase Page for updates and more resources like this. Stay connected with CCC for the latest insights, publications, and opportunities to engage by subscribing here.

The Computing Community Consortium (CCC) recently published a new report on how advanced computing, especially artificial intelligence (AI), can extend that impact even further while at the same time contribute to human-in-the-loop computational research. The report, titled Grand Challenges for the Convergence of Computational and Citizen Science Research, assesses the ways that such technology can increase the potential of citizen science, ultimately enhancing scientific capability more broadly. It also lays out the necessary next steps in computing research to make this convergence a reality. 

Increasing Scientific Output

One of the biggest strengths of technology like AI is its ability to process and interpret large amounts of complex data. In addition to generating such datasets, citizen scientists possess the knowledge, creativity, and skills that make up for what such technology cannot do alone. They can enrich models with contextual insights and provide essential edge-case observations that increase the reliability of processed data, making the most of machine learning models while avoiding their downfalls. 

This symbiotic relationship has the potential to support more accurate and timely scientific outputs across fields. For computing researchers specifically, it also creates a unique opportunity to do vital computational research into building trust, access, and transparency into AI systems by embedding them in real-world participatory contexts.

The applications of faster, more reliable data processing are broad. Other areas of scientific research that the report highlights as standing to benefit from convergence include supporting endangered species and protecting them from poachers, accelerating medical research, and improving our knowledge of astrophysics.

A Long-Term Research Roadmap

Further research into advanced computing technologies is required for convergence to truly flourish. Below are some of the foundational investigations required to advance the field, focusing on developing new models, metrics, and frameworks for human-AI interaction, trust, and accountability.

• Incentivize cross-disciplinary and cross-sectoral collaboration: Break down silos by investing in strategies that incentivize collaboration and knowledge sharing between computational and citizen science researchers. Encourage collaboration between academic researchers, federal agencies, industry, and local communities to co-design participatory platforms.
• Develop Human-AI teaming frameworks for public participation: Prototype and study new models for collaboration between AI and citizen scientists, particularly incorporating large language models. Specifically investigate novel systems where complementary roles are driven both by multi-agent decisions and community needs. Explore how these systems balance efficiency with human contextual insight in problems such as task assignment, anomaly detection, and data collection or labeling of large datasets.
• Develop explainable AI for non-expert users: Research novel user interface and data visualization techniques to make AI decision-making transparent and interpretable to the general public, as well as developers or researchers, enabling the general public to gain fluency with AI concepts and better understand how AI is used. Determine to what extent these techniques build trust and enable broader public engagement with AI-driven platforms. 
• Design real-time feedback systems for citizen science: Research systems that enable adaptive, multilingual, and just-in-time digital feedback loops that guide users during data collection and analysis. This includes experimentation with LLMs, AR/VR, edge computing, and mobile-first design. Study these systems for improvement in data quality, user engagement, and learning outcomes.
• Institutionalize evaluation and trust metrics: Fund research on trust diagnostics, engagement dynamics, and societal benefit indicators to guide iterative improvement and accountability.
• Advance participatory AI governance models: Research mechanisms to ensure communities have real governance over how AI is deployed and data are used, including mechanisms for consent, opt-out, accountability, and oversight. Incentivize co-development of toolkits that help projects explain AI behavior to users, including uncertainty visualization, explainable model outputs, and citizen-led model critique workflows. Offer funding (e.g., challenge grants) for co-designed AI tools that are built with community organizations, encouraging broad public participation in scientific research.
• Pilot Human-AI teaming systems across multiple scales and domains: Fund research as well as deployment testbeds at local and global scales in domains where humans and AI collaborate in real time, including disaster response, health, and environmental monitoring, building scalable models for multi-agent systems.

Of course, the success of convergence also depends on the skills and preparedness of the humans involved — including researchers and citizen scientists themselves. Multilingual, psychology-informed, and adaptive systems incorporating AI that train and guide users must be developed and embedded in citizen science projects; such systems will build user capacity and skills while also improving data quality. And in continuing education, it’s essential to build both ML/AI skills and civic literacy, fostering a new generation of convergence-ready scientists, engineers, and public leaders.

Read the Full Report

For a full picture of the impact of large-scale convergence, as well as key recommendations across sectors for how to make it a reality, we encourage all members of the computing community to read the Grand Challenges for the Convergence of Computational and Citizen Science Research report below.

Read the Full Report Here

Tune in to the CCC LinkedIn Showcase Page for updates and more reports like this. Stay connected with the CCC Blog for the latest insights, publications, and opportunities to engage by subscribing here.

Call for Papers: ACM International Conference on Multimodal Interaction

Track: Blue Sky Ideas

Date: October 5-9, 2026

Location: Napoli, Italy

Website: https://icmi.acm.org/2026/call-for-papers-2/

Aims and Scope

The Blue Sky Ideas Track at the ACM International Conference on Multimodal Interaction seeks visionary contributions at the intersection of multimodal artificial intelligence (AI) and social interaction. This track encourages submissions that push the boundaries of conventional research, challenging existing methodologies, proposing novel theories or applications, and presenting high-risk, controversial ideas. Papers should exhibit deep, interdisciplinary insights, robust argumentation, and a synthesis of ideas from various methodologies. 

This year’s theme is “Context and Cultural Awareness for Multimodal Interaction.” A unique aspect of ICMI is its multidisciplinary nature, bringing together research in AI, multimodal data processing, and human-human and human-computer interaction to bridge behavioural understanding with technology, with an eye towards impactful applications that benefit people and society.

Why Submit?

Shape bold, long-term research visions in multimodal AI and human-centered computing

Engage with leaders across the computing field

Gain recognition through CCC and ACM-sponsored awards

Submission Guidelines

Submissions must be no more than 4 pages, excluding references

Please use the double column ACM conference template

Author names and affiliations should be not included

Submit via: https://new.precisionconference.com/submissions/icmi26a

Important Dates

Submission Deadline: Friday, May 8, 2026

Notification of Acceptance: Tuesday, June 23, 2026

Camera-Ready Papers: Friday, July 10, 2026

Presentation Dates: October 6-8, 2026

Publication Policies

As of January 1, 2026, ACM has fully transitioned to Open Access. All ACM publications, including those from ACM-sponsored conferences, will be 100% Open Access. Authors will have two primary options for publishing Open Access articles with ACM: the ACM Open institutional model or by paying Article Processing Charges (APCs). With over 1,800 institutions already part of ACM Open, the majority of ACM-sponsored conference papers will not require APCs from authors or conferences (currently, around 70-75%).

Authors from institutions not participating in ACM Open will need to pay an APC to publish their papers, unless they qualify for a financial or discretionary waiver. To find out whether an APC applies to your article, please consult the list of participating institutions in ACM Open and review the APC Waivers and Discounts Policy. Keep in mind that waivers are rare and are granted based on specific criteria set by ACM.

Understanding that this change could present financial challenges, ACM has approved a temporary subsidy for 2026 to ease the transition and allow more time for institutions to join ACM Open. The subsidy will offer:

$250 APC for ACM/SIG members

$350 for non-members

This represents a 65% discount, funded directly by ACM. Authors are encouraged to help advocate for their institutions to join ACM Open during this transition period.

This temporary subsidized pricing will apply to all conferences scheduled for 2026, including ICMI.

Awards

The first, second, and third place winners will have the opportunity to present their papers at the conference and will be included in the proceedings. Travel grants will be awarded to support their travel to the conference. CCC sponsors awards to honor the first ($1,000), second ($750), and third ($500) place winners in the form of travel grants. In addition, we will further distribute and publicize the three Blue Sky award papers.

Questions?

Contact the organizers at: icmi2026-blueskypapers-chairs@acm.org

Blue Sky Paper Chairs:

Catherine Pelachaud (CNRS – ISIR, Sorbonne University)

Timothy Bickmore (Northeastern University)

To that end, the U.S. Department of Health and Human Services (HHS), together with the Assistant Secretary for Technology Policy and Office of the National Coordinator for Health Information Technology (ASTP/ONC), recently put out a request for information (RFI) on the advancement of AI use in clinical care. It asked what HHS can do to foster public trust and confidence in modern technology solutions, to reduce uncertainty that impedes AI innovation, and to align federal incentives so that AI is deployed in ways that enhance productivity, reduce burden, lower health care costs, and improve health outcomes for patients, caregivers, and communities.

The Computing Community Consortium (CCC), in conjunction with the Computing Research Association – Industry (CRA-I), recently assembled a response to this RFI. In it, computing experts and healthcare professionals come together to present their recommendations for how to make the most of AI advancements in a clinical setting while prioritizing continuing quality of care, patient privacy, and other essential concerns. Below are some of the prevailing themes from the response.

The Potential and Limitations of Clinical AI

One of the biggest barriers facing AI adoption is public hesitancy. Many Americans are reluctant to engage with, or have their healthcare providers engage with, AI tools in a clinical setting because of reliability and privacy concerns. Training AI models with more patient data is likely to improve their output’s usefulness; yet as of now, little assurance exists that that data will remain secure in a HIPAA-compliant way. Because of this, investing in making AI tools secure and trustworthy — and helping the public see this effort — is paramount.

At the same time, studies have found immense potential for AI to enhance clinical care, like assisting in recording patient visits, differential diagnosis, creating plans for therapy and alternatives, and administrative functions like appointment scheduling. Especially in cases where AI can assist providers in avoiding documentation-related burnout, it has incredible potential to enhance the doctor-patient relationship.

This response also highlights ways, moving forward, to improve the overall usefulness and usability of AI tools in healthcare, such as:

• Making sure to consult healthcare professionals on what would best support their work
• Keeping models up-to-date with local, not just aggregate, data
• Avoiding relying on autonomous models
• Investing in industry-academia partnerships across AI and clinical care
• Reforming regulations to improve access to anonymized health data and incentivizing health systems to collaborate with researchers to contextualize that data
• Creating and implementing standard safety evaluation frameworks

For these actions to have maximum impact, it is also crucial to address a growing “AI gap” between clinical settings in urban and rural communities. Devoting resources to supporting clinical care in states in the Established Program to Stimulate Competitive Research (EPSCoR) and Institutional Development Award (IDeA) programs will help to address this divide and promote quality care for all.

Future Research Directions

This RFI response recommends three key areas of research to advance clinical AI use:

• AI-Driven Clinical Decision Support: Developing new end-to-end models that include reasoning and inference to assist in clinical decision-making
• Human-AI Collaboration: Understanding how best to keep humans, including patients, caregivers, clinicians, and more, engaged as key stakeholders in AI tools
• Development and Implementation Trajectory Evaluation Frameworks: Ensuring the information produced by AI models is accurate, relevant, and understandable through the in-context evaluation of clinical AI use.

By investing in these research areas, we can move beyond experimental models and toward systems that are mindful, technologically sound, and seamlessly woven into the complex reality of modern healthcare.

Read the Full Response

For the full scope of CCC, CRA, and CRA-I findings and analysis on advancing AI in clinical care, access the full response below. You can also see more CCC responses to the community here.

Read the Full RFI Response Here