The IR-for-Good track at ECIR is dedicated to gathering impactful, high-quality, and societally-motivated IR research, fostering a common platform for engagement among researchers, practitioners, and civil society from various backgrounds and sectors. The focus of this track is to facilitate conversations within the IR community and in conjunction with other disciplines on how IR research and practices can contribute towards realizing more equitable, emancipatory, and sustainable futures.

This year, we are revamping the dedicated IR-for-Good track to be a core conference track that will run alongside the main conference (not on workshop day). We want this special track to be a platform that highlights top societally-motivated IR research at ECIR. We strongly encourage authors to submit societally-motivated papers to this special track.

Also, we are making another change to ensure that the contributions we are soliciting have the desired societal impact. We require all submissions that propose new IR tools, methods, datasets, and interventions to explicitly and rigorously argue how the work contributes towards positive social outcomes (see the section on "Theories of Change" below for more details). Position papers and critiques are exempted from this requirement as these arguments should anyways be a core contribution of those submissions.

Scope

IR-for-Good includes IR research that:

1. Explicitly concerns with new research directions and system design to achieve specific societally beneficial outcomes,
2. Develops new fairness, privacy, transparency, accessibility, sustainability, and other similar societally-motivated interventions, and/or
3. Identifies and critiques the ways in which existing IR methods and systems and how we do IR research may contribute to systemic harm or impede social progress.

Topics of Interest

We invite contributions that explore new positions, critiques, tools, methods, datasets, and interventions for IR-for-Good. We also welcome IR contributions informed by interdisciplinary perspectives, such as human-computer interaction, information sciences, media studies, design, science and technology studies, social and political sciences, philosophy, law, environmental sciences, public health, educational sciences, and machine learning.

Specific areas of interest include, but are not limited to, how IR intersects with and/or can support:

• Accessibility and disability justice
• Art, culture, and representation
• Crisis and disaster management
• Decolonization and racial justice
• Emancipation, anti-oppression, and social justice
• Gender and sexuality justice
• Informed citizenry, democracy, and collective decision making
• Law and restorative justice
• Literacy and knowledge production
• Privacy and dignity
• Public health and community health
• Social, political, and economic equity
• Sustainability and environmental justice
• Worker rights and labor movements

All submissions must be relevant to IR. For clarity on what should be considered relevant to IR please check the Call for Full papers for ECIR 2026.

We welcome contributions focusing on algorithmic bias, fairness, transparency, interpretability, explainability, trustworthiness, misinformation, disinformation, hate speech, replicability, transferability, robustness, uncertainty, security, and ethics. However, all contributions are required to explicitly articulate how the work contributes towards positive social outcomes and not implicitly assume that all research on these topics contribute to social good. As a corollary, certain IR topics that may not have historically been seen as socially focused (e.g., designing distributed information access platforms or developing more effective ranking models without the use of user behavior data) would also be welcome in this track if they can appropriately argue that the work is likely to contribute to social good, e.g., by making platforms more robust to authoritarian capture or disincentivizing mass ubiquitous user surveillance, respectively.

Theories of Change

IR research that tries to affect positive social change needs to be grounded in rigorous understanding of the sociotechnical challenges and the complex socio-political context in which they exist. We require every submission that proposes new IR tools, methods, datasets, and interventions to explicitly include a separate section elaborating how the work contributes towards desired social outcomes, i.e., their theory of change. This section should not be an afterthought, instead it should be a critical part of the core motivation of the work. It is not mandatory to name the section "Theory of change" and position papers and critiques are exempted from this requirement.

We encourage authors and reviewers to critically engage with this section while acknowledging the real uncertainty of how any well-intentioned research may impact society in practice. Our goal is not to encourage authors to inflate their claims of social impact but to rigorously deliberate on their sociotechnical assumptions and enumerate the necessary preconditions for the work to have its desired impact and also potential negative externalities.

We recommend that the "Theory of Change" section should explicitly state:

1. What is the identified societal need / problem, and how are the core contributions from this current work expected to address them?
2. What preconditions are necessary or what assumptions need to hold for this work to have its desired effect, and how likely are they to hold true in practice?
3. What are possible negative externalities of this approach and is it plausible that this may lead to new or different harms?

Authors are encouraged to include any additional discussions that they may deem relevant in this section.

Example considerations

Next, we present a few example cases to illustrate the kind of critical reflections we want to encourage authors and reviewers to engage in.

Claim: Our work that proposes a method for making expensive machine learning models for IR more efficient contributes towards sustainability and reducing impact on the environment.

• Considerations for this claim: What preconditions are necessary for the efficiency improvements to translate to reduced impact on the environment? E.g., according to the Jevons paradox in economics, when technological advancements make a resource more efficient to use (thereby reducing the amount needed for a single application) it often results in overall increase in demand, causing total resource consumption to rise instead of falling. Is it more likely then that more efficient models may in fact lead to a false sense of mitigation and result in much wider adoption contributing to increased harm to the environment?

Claim: Our work that develops new assistive tools for document authoring increases worker productivity and contributes towards reduced labor for workers.

• Considerations for this claim: What preconditions are necessary for the improvement in productivity to benefit the workers? In other words, who gets to benefit from the surplus provided by technology here? Does it benefit workers or does it lead to further reduction in their compensation and changes in job expectations that lead to lower status? Does the proposed approach provide any enforceable mechanisms to ensure that the surplus primarily benefits the workers?

Claim: Our work that improves alignment of LLMs towards specific social values contributes towards user safety by preventing exposure to harmful content.

• Considerations for this claim: Who gets to decide what is harmful or select the values the system should be aligned with? Could these approaches in fact further centralize power and control over what is deemed "acceptable" vs. "harmful" speech? Could this stifle the voices of marginalized people and social activists? Could this incentivize authoritarian capture of information access platforms to manipulate public opinion? Does the proposed approach consider both the social and technical aspects of this problem to ensure democratic oversight and emancipatory outcomes?

Claim: Our work that proposes new methods for generating explanations for model outputs contributes towards increasing user trust in the system.

• Considerations for this claim: Is that trust beneficial or harmful for the user? Is that trust warranted or could it in fact draw users into a false sense of safety and distract them from noticing how the system surveils them and subtly manipulates their behavior? How can this explainability intervention actually help reveal and challenge existing power structures?

Claim: Our work that proposes a new ranking approach for gender fairness contributes towards gender justice.

• Considerations for this claim: How does the adopted definition of "gender fairness" in this work translate to mitigating real-world "gender discrimination"? Does this work assume that gender is binary, erasing other identities? Does this work assume that gender is known for all users / subjects and incentivize further intensification of surveillance and collection of private demographic data from members of historically marginalized communities? How can this work be operationalized in practice towards gender and sexuality justice? Are there example use-cases where this can be reliably demonstrated (e.g., ranking in hiring or job recommendation applications)?

Submission Guidelines

The IR-for-Good track provides the opportunity for researchers to present state-of-the-art research on the track topic, which makes, or has the potential to make, a significant contribution to the field.

Submissions of papers must be at least 6 pages (the sixth page should have at least some content but not necessary to fill it) and at most 12 pages in length plus additional pages for references. The "Theory of Change" section and Appendices count toward the page limit. Please put appendices before the references for paper submission. While this year we do not set separate submission tracks for full and short papers in the IR-for-Good track, the assessment of each submission will be based on whether the paper length is commensurate with its contribution. For example, a 6-page paper would be accepted if its scientific contribution is worth 6 pages. However, a 12-page paper would be considered weak if it only contains the substance of a 6-page paper.

All submissions must be written in English. All papers should be submitted electronically through the EasyChair submission system: https://easychair.org/conferences/?conf=ecir2026. Select the IR-for-Good track.

For the preparation of their papers, the authors should consult Springer's authors' guidelines and use their proceedings templates, either for LaTeX or for Word (https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines). Please also note that Springer encourages the authors to include their ORCIDs in their submitted papers (https://www.springer.com/gp/authors-editors/orcid). Once the paper has been submitted, changes relating to its authorship cannot be made. Submissions will be refereed via a double-blind peer review, with an initial first stage review followed by a second stage of discussion.

Accepted papers will be published in the main conference proceedings in the Springer Lecture Notes in Computer Science series. The corresponding author of each accepted paper, acting on behalf of all of the authors of that paper, must complete and sign a Consent-to-Publish form. The corresponding author signing the copyright form should match the corresponding author marked on the paper. The proceedings will be distributed to all delegates at the conference. The accepted papers will have to be presented at the conference and at least one author for each accepted paper is required to register.

Dual Submission Policy

Papers submitted to IR-for-Good track should be substantially different from papers that have been previously published, or accepted for publication, or that are under review at other venues.

Exceptions to this rule are:

• Submission is permitted for papers presented or to be presented at conferences or workshops without proceedings.
• Submission is permitted for papers that have previously been made available as a technical report (e.g., in institutional archives or preprint archives like arXiv). However, we discourage this since it places anonymity at risk; in particular, please do not publish your paper at arXiv and submit to ECIR at the same time, some days before, or during the reviewing period of ECIR.
• If your paper already is available as a technical report:
  • You might not want to use the exact same title and abstract for your ECIR submission (in case of acceptance at ECIR, the title of your submission still might be changed "back").
  • Please do not cite your technical report and make some effort to avoid any issues that may harm the double-blindness of your submission. Reviewers will receive guidance that ask them to refrain from trying to break blindness if at all possible too, but be aware that the availability of a technical report for an ECIR submission can cause issues.

Ethics and Professional Conduct

IR-for-Good track expects the authors (as well as the PC, and the organizing committee) to adhere to accepted standards on ethics and professionalism in our community, namely:

• The ACM's Policy on Authorship,
• The ACM's Code of Ethics and Professional Conduct,
• The ACM's Conflict of Interest Policy,
• The ACM's Policy on Plagiarism, Misrepresentation, and Falsification,
• The ACM's Policy Against Harassment.