EAAMO 2026: The Sixth ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization

The Sixth ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO ‘26) will be held from November 5–7, 2026, at LMU Munich, Germany.

The event will highlight work across the research-to-practice pipeline that aims to ensure algorithmic systems play a broadly beneficial role in society, advancing equity and expanding access to opportunity for underserved communities. We invite submissions that draw on techniques from artificial intelligence and machine learning, mechanism design, optimization, operations, and economics, as well as perspectives from human-computer interaction and the social sciences that examine the societal embedding of these technologies. The conference will provide an inter-disciplinary forum for presenting research papers, problem pitches, survey and position papers, new datasets, and software demonstrations with the goal of bridging research and practice.

Submissions will have the option to be either archival or non-archival.

Important Dates# Abstract Submission Deadline: May 8, 2026 (AoE) Paper Submission Deadline: May 15, 2026 (AoE) Submission Notification: August 1, 2026 Event Dates: November 5 - 7, 2026 Paper Submission Page: https://openreview.net/group?id=ACM.org/EAAMO/2026/Conference

Conference website: https://conference.eaamo.org/

Areas of Interest# We invite submissions on topics, methodologies, and approaches including, but not limited to:

ethical, economic, legal, philosophical, and societal considerations of algorithmically-driven interventions redistributive mechanisms for improving access to opportunity and equitable outcomes micro- and macroeconomic consequences of inequality and market inefficiencies determinants and causes of harm, including inequitable outcomes, market failures, exploitative behavior, and economic inefficiencies machine learning, optimization, and mechanism design for alleviating inefficiencies, inequitable, and unsafe economic and social outcomes human-AI interaction for improving accessibility and outcomes AI and algorithm evaluation uncertainty, safety, privacy, and equity in allocative and representational systems algorithmic, ethical, policy, and societal challenges in computing in resource-constrained settings reliable, trustworthy, and valid learning and inference in societally-consequential domains data collection, curation, governance, protection, and sharing efforts for work related to improving access to opportunity algorithmic approaches and tools to encourage participation, empower, and organize communities for the collective good regulation and policy design related to data, privacy, equity, fairness, and access to opportunity Application areas of interest include, among others, civic participation, data economies, discrimination and bias, digital and economic inequalities, economic development, education, environment and climate, food security, healthcare, housing, infrastructure, labor markets, law and policy, low- and under-resourced computing, social and economic mobility, privacy, public service provision, recommender systems, social work, sustainable development, and transportation.

Tracks# Submissions can introduce new theories and applications or can be position papers providing a method-driven literature review, synthesizing perspectives, or highlighting future directions. We welcome contributions that propose new general methodologies and those that emphasize applied work, policy issues, or practical problems. Submissions will be reviewed in one of six tracks:

AI, HCI and Machine Learning, including AI and HCI for social impact, bias and fairness considerations in statistical reasoning, learning and decision making in an economic context, optimization, fair division and allocation in resource-constrained settings, interactions in multi-agent and socio-technical systems, causal analysis of algorithmic interventions and their long-term effects, social choice, work at the intersection with privacy and security, human factors in algorithms, and sociotechnical considerations in AI deployment. Applied and Quantitative Modeling, including proposing and analyzing applied and structural models for novel practical problems in data science, computational social science, industrial organization, market and mechanism design, micro- and macroeconomics, and operations management (e.g., supply chain, service operations). Datasets and Benchmarks involving submissions of novel datasets and benchmarks collected and documented to foster new research in different application domains, creating new standards for data collection, transparency, and accountability and fostering interdisciplinary collaborations. The submission should include the dataset, which will be uploaded as a supplementary file, and metadata information. The dataset may also be uploaded to external data-sharing services, and the conference will provide an additional option for a data portal upon acceptance of the submission. The submission should be accompanied by a document detailing the application domain, the potential for use of the data, and any known issues, limitations, and ethical considerations for the collection, curation, access, and use. Authors are encouraged to consider questions presented in the datasheets for dataset recommendations. Empirical Studies and AI Evaluation including 1) empirical analysis of existing social systems, policies, and mechanisms, and other domains such as empirical industrial organization/health/education/labor; 2) empirical evaluation of systems that incorporate computational tools such as algorithms, mechanisms, or optimization; and 3) description and qualitative or quantitative analyses of deployments of systems by the researchers. Position Papers and Problem Pitches that present a novel position, idea or open a new area of discussion regarding a particular research topic related to the scope of the conference. Submissions will be evaluated based on their novelty and potential to of