Scope
Aims and scope
Artificial intelligence and the systems built from it, examined through technology, economics and law. The journal welcomes rigorous work within and across these dimensions.
Composition
Technology, economics and law, read together
Articles are considered across three dimensions: what was built, what changed for the organisation, and what must be demonstrable to an authority.
- TechnologyWhat was built, and what it measurably does
- The model, the data, the architecture and the engineering: how the system was trained or composed, and the measurements that show what it does.
- Economics and organisationWhat it costs, and what it changed
- The cost of running the system, the process it alters, the organisation that has to operate it, and the evidence of the effect it produced.
- Law and regulationWhat has to be demonstrable to an authority
- The rules the system answers to: supervisory expectation, liability, data protection and auditability, down to the record an authority can ask for.
Subject areas
Work on the systems themselves
The scope comprises eight subject areas. Editors place cross-disciplinary manuscripts at their primary intersection.
- FoundationsThe theory the systems rest on
- Learning theory, model architectures, algorithms, reasoning and representation.
- Systems and infrastructureTraining and inference at the scale they actually run
- Compute, data pipelines, deployment and operation, from a training run to a service under load.
- Building with themWhat it takes to keep a system correct in production
- Software engineering practice, agents and tool use, retrieval and orchestration.
- EvaluationMeasurement, and what a number is worth
- Benchmarks, reproducibility, statistical practice and the interpretation of results.
- Safety and securityHow a system fails, and what assurance is available
- Alignment, failure modes, adversarial behaviour, assurance and verification.
- Governance and standardsAccountability that can be evidenced
- Auditability, compliance architecture, assurance regimes, standards and identity.
- Society and economicsWho carries the cost, and who holds the benefit
- Labour, distribution, institutions, environmental cost and the public interest.
- Human and machineWhere a person and a system share the decision
- Interfaces, autonomy, delegation, oversight and trust.
Domains
Where these systems meet a field
Domain-specific data, regulation and consequences determine whether a system claim is testable.
- FinanceSystems inside a supervised institution
- Banking, insurance, capital markets, valuation, financial crime and supervisory practice.
- Audit and riskEvidence an auditor can act on
- Internal control, model risk, assurance, and the working papers behind an opinion.
- Legal practiceWhere the procedure itself is the system
- Legal technology, contracting, notarial and judicial process, and compliance architecture.
- Public sectorServices delivered under public authority
- Administration, fiscal policy and public finance, procurement and the delivery of public services.
- HealthDecisions that reach a patient
- Diagnosis, care pathways, clinical evidence and patient safety.
- Industry and engineeringSystems with a physical consequence
- Manufacturing, mechatronics, energy, logistics and the industrial control estate.
- Enterprise architectureMany systems, one accountable organisation
- Multi-agent systems, protocol design, ledgers, and running these systems across an organisation.
- Work and organisationThe roles built around a system
- Task composition, human oversight, leadership, skills and the design of roles.
- EducationWhat was learned, and how it was measured
- Curriculum, learning design, credentialing and the assessment of what was learned.
- Research practiceArtificial intelligence inside science itself
- Artificial intelligence in the conduct of research, in methodology, and in publishing and peer review.
Boundaries
Where the boundary runs
A subject belongs here when it concerns artificial intelligence or a system built from it, and carries evidence a reader can weigh.
- Capability claimsThe measurement and its conditions travel together
- A capability claim belongs here when the article states what was measured, under which operating conditions, and which evidence supports it.
- Adjacent fieldsEditorial criteria apply across disciplines
- Statistics, control, operations research, law and the social sciences belong here wherever they bear on artificial intelligence or on a system built from it. Editors assess their method, evidence and contribution under the journal's criteria.
- Argument and positionA single point the field can act on
- A literature-based argument belongs here as a Note. It makes one point and situates that point in the literature it addresses.
- Surveys and reviewsA synthesis that changes how the field reads itself
- A survey belongs here when it contributes a stated search, explicit inclusion criteria and a synthesis that gives the reader a reading of the literature.
Standing call
Artificial intelligence, energy and sustainability
The journal publishes work that reports compute and energy alongside measured performance, including results from the energy systems to which models are applied.
- Efficiency as a resultWhat a result costs to obtain is part of the result
- Smaller models, distillation, quantisation, sparsity, retrieval in place of scale, better scheduling and higher hardware utilisation are findings in their own right. A method that matches a baseline at a fraction of the compute is reported here as the contribution it is.
- Energy systemsGeneration, grids and storage, with the evidence from service
- Forecasting, dispatch and demand response, the integration of renewable capacity, and efficiency in buildings, industry and transport. What changed is shown by the operating record of the system itself.
- Measured footprintThe cost of computation itself, attributed and comparable
- How energy, carbon intensity and water use are attributed to training and inference, how the figures are made comparable across sites and hardware, and where attribution requires evidence beyond a datacentre's own numbers.
- ESG evidence and assuranceA disclosure is worth the evidence behind it
- The provenance of the data behind a disclosure, the auditability of a claim across a supply chain, and what regulatory reporting regimes require a company to be able to demonstrate.
Readership
Who the journal is written for
The journal's assessment covers method, cost and legal exposure within a single article.
- ResearchBuilding the systems and measuring what they do
- Researchers and engineers in universities, laboratories and industry, who read an article for its method and its numbers.
- Regulated practiceAnswering for a system already in service
- Practitioners in finance, audit, law, health, industry and public administration, who read an article for what it would take to run the same thing and stand behind it.
- Oversight and policyJudging whether an organisation's use of one is defensible
- Boards, supervisors and policy specialists, who set the rules a system runs under and read an article for the evidence it would produce under scrutiny.
Review the submission requirements, then send the manuscript to the editors.