ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology
Title	ACM Transactions on Intelligent Systems and Technology
Discipline	Computer science
Abbreviation	ACM Trans. Intell. Syst. Technol.
Publisher	Association for Computing Machinery
History	2010–present
Frequency	Quarterly
Openaccess	Hybrid

ACM Transactions on Intelligent Systems and Technology is a peer-reviewed academic journal publishing research on intelligent systems, machine learning, human–computer interaction, and applied artificial intelligence. The journal serves as a venue for interdisciplinary work connecting algorithm design, systems engineering, and user-centered studies, attracting submissions from academia, industry, and government laboratories. Authors associated with institutions such as Massachusetts Institute of Technology, Stanford University, University of California, Berkeley, Carnegie Mellon University, and University of Oxford have contributed to its pages, alongside researchers from Google, Microsoft Research, IBM Research, Facebook AI Research, and DeepMind.

History

The journal was established in 2010 under the auspices of the Association for Computing Machinery to provide a dedicated outlet for applied and systems-oriented artificial intelligence research following growth in venues such as NeurIPS, ICML, KDD, CHI, and AAAI Conference on Artificial Intelligence. Early editorial leadership included scholars with ties to University of Illinois Urbana-Champaign, Princeton University, University of Cambridge, and University of Toronto, reflecting cross-Atlantic collaboration comparable to networks linking École Polytechnique Fédérale de Lausanne, ETH Zurich, Tsinghua University, and Peking University. Development of the journal paralleled policy discussions at organizations like the National Science Foundation, European Research Council, Office of Science and Technology Policy, and collaborations with laboratories such as Lawrence Berkeley National Laboratory and Sandia National Laboratories.

Scope and Topics

The journal's remit spans intelligent systems and technology with emphasis on systems integration, algorithmic innovation, and empirical evaluation. Typical topics include machine learning methodologies related to venues such as COLT, ICLR, and AISTATS; recommender systems connected to work at Netflix and Amazon; natural language processing reflecting contributions from groups at OpenAI, Google DeepMind, and Microsoft Research Cambridge; robotics and perception intersecting with research from Boston Dynamics, Honda Research Institute, and MIT CSAIL; and human–computer interaction influenced by studies emerging from University College London, Indiana University Bloomington, and Cornell University. The journal also publishes articles relevant to deployment contexts associated with National Institutes of Health, Department of Defense, European Commission, and industry consortia including W3C and IEEE standardization activities.

Editorial Board and Peer Review

Editorial governance mirrors practices at leading journals such as Communications of the ACM, IEEE Transactions on Pattern Analysis and Machine Intelligence, and Journal of Machine Learning Research. The editorial board comprises editors and associate editors affiliated with institutions like Harvard University, Yale University, University of Washington, Georgia Institute of Technology, Imperial College London, National University of Singapore, and University of Melbourne. Peer review is single- or double-blind depending on submission track and follows guidelines comparable to those at Nature Machine Intelligence, Science Robotics, and PLOS ONE, with external referees drawn from conferences and labs including SIGIR, SIGCHI, SIGKDD, Apple Machine Learning Research, and national research councils such as NSERC and DFG.

Publication and Access Model

Published by the Association for Computing Machinery on a quarterly schedule, the journal operates a hybrid access model that allows authors to choose open access options consistent with mandates from funders like the Wellcome Trust, Horizon 2020, and the Bill & Melinda Gates Foundation. Production workflows align with services used by other publishers such as Springer Nature, Elsevier, and Wiley, and the online platform integrates indexing and DOI assignment coordinated with CrossRef and metadata practices promoted by ORCID and CLOCKSS.

Abstracting and Indexing

The journal is indexed in major bibliographic databases and citation services paralleling inclusion patterns exhibited by titles in Scopus, Web of Science, Google Scholar, and INSPEC. Abstracting and archiving interoperability references normative frameworks from DOAJ, Portico, and initiatives involving Project COUNTER and CrossRef to ensure discoverability relevant to researchers at institutions such as Columbia University, University of Michigan, Johns Hopkins University, and University of California, San Diego.

Impact and Reception

The journal has been cited in interdisciplinary research spanning computer science and applied domains, cited alongside influential works from Geoffrey Hinton, Yann LeCun, Andrew Ng, Jürgen Schmidhuber, and Fei-Fei Li. Its impact metrics are discussed in contexts similar to analyses published by Eigenfactor, Scimago, and bibliometric studies from Clarivate Analytics. Community reception highlights the journal's role in bridging conference-led rapid dissemination at venues like NeurIPS and ICML with the more expansive narrative and evaluation depth expected in venues such as ACM Computing Surveys and ACM Transactions on Graphics.

Notable Articles and Special Issues

Noteworthy contributions have included applied evaluation studies referencing datasets and benchmarks originating from ImageNet, COCO, GLUE, SQuAD, and OpenAI Gym, as well as special issues devoted to topics paralleling community emphases at workshops and symposia organized by IJCAI, AAAI, Robotics: Science and Systems, and thematic programs funded by DARPA and European Research Council. Guest-edited issues have featured editors affiliated with Stanford University Medical School, MIT Media Lab, Max Planck Institute for Intelligent Systems, and Riken focusing on ethics, interpretability, and human–AI collaboration, intersecting with policy dialogues involving UNESCO and OECD.

Category:Computer science journals