GCRI · Labs
IA en los flujos de trabajo científicos
Machine learning now enters science at every step: screening literature, designing experiments, analysing images and sequences, emulating simulations and drafting text. Each use can speed discovery and each can introduce errors that look like findings. This page sets out how models are evaluated for scientific use, where leakage, benchmark overfitting and silent drift arise, and what evidence editors, funders and regulators need before they rely on a model-assisted result.
Cómo se desarrolla el trabajo.
Testbed operations
AI in scientific workflows · Field lab coast · Campaign 5
Sensor deployment
litoral ilustrativo
Sensors
| S-01 tide | 94 | 0.0 | Nominal |
| S-04 salinity | 76 | 0.4 | Recalibrate |
| S-07 wave | 95 | 0.1 | Nominal |
Sensor vs reference
Fig.Testbed operations: example names and illustrative values, not a record or a live system.
AI in scientific workflows: how the risk develops.
Model evaluation and scientific assistance. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Leakage inflates performance
When information from the test set reaches training, through duplicates, shared patients, neighbouring sites or preprocessing fitted on all the data, a model appears more accurate than it is. Leakage has been documented in many fields that adopted machine learning.
Benchmarks get overfitted
Repeated tuning against the same public benchmark selects models that fit its quirks. Gains on a leaderboard can vanish on new data, and contamination of training corpora with benchmark items makes scores hard to interpret.
Drift and distribution shift
Instruments, protocols, populations and software change. A model validated on last year’s data or another lab’s scanner can degrade without any visible error. Monitoring after deployment is part of the evaluation, not an extra.
Generated text and fabricated sources
Language models can produce fluent text, citations and code that are wrong. Unchecked, these errors enter manuscripts, peer reviews and proposals, and spread through citation. Disclosure and verification policies exist because such errors are hard to see.
How AI in scientific workflows is modelled, measured and tested.
El modelo, sus supuestos, sus datos y su incertidumbre, en la forma en que un científico o un ingeniero los comprobaría. Las estructuras siguen métodos publicados. Los valores representados son ilustrativos, no hallazgos.
Modelo, ecuaciones y supuestos
The model structure used for AI in scientific workflows, the assumptions it makes and how each is tested.
Ri = f(Hi, Ei, Vi, Ci)amenaza, exposición, vulnerabilidad y capacidad de afrontamientoEAL = ∑k pk · Lksobre los eventos k con probabilidad anual pkxt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error| Supuesto | Por qué se hace | Cómo se prueba |
|---|---|---|
| La amenaza y la exposición son independientes dentro de un evento | Mantiene manejable la integral de pérdidas | Comparar con un ajuste de cópula sobre registros conjuntos |
| Los acoplamientos son lineales cerca del punto de operación | Permite que las perturbaciones se propaguen a través de A | Perturbar más allá del rango lineal y comparar |
| Las curvas de vulnerabilidad se transfieren entre lugares | Las curvas locales rara vez están disponibles | Validar frente a las pérdidas observadas cuando existan |
- Entradas
- conjuntos de datos versionados con procedencia
- Resultados
- distribuciones, nunca cifras únicas
- Código
- publicado con la ficha de modelo y las pruebas
- Reproducción
- mismos datos, código, entorno y semilla
AI in scientific workflows changes each domain’s methods.
Findings feed the serie Methods and Technical. AI use follows NIST’s AI RMF and is disclosed; models carry NS-12 cards under the proposed Nexus Standards.
- Workflow evaluations.
- Disclosed AI use cases.
Qué se mide y con qué calidad.
Cada indicador lleva su unidad, su periodicidad, el tipo de fuente que lo respalda y la etiqueta de evidencia que llevaría un valor. No se muestran valores: un programa los aporta bajo su propia autoridad.
| Indicador | Unidad | Cadencia | Tipo de fuente | Etiqueta |
|---|---|---|---|---|
| Held-out evaluation design stated | percent of model-based studies | anual | Audit against the REFORMS checklist | OBSERVADO |
| Code and model weights available | percent of studies with runnable code and weights | anual | Repository and article audit | OBSERVADO |
| External validation | percent of models tested on data from another site or period | anual | Literature review | ESTIMADO |
| Error de calibración | expected calibration error, dimensionless | per model | Held-out evaluation | OBSERVADO |
| Performance gap under shift | difference in the primary metric between internal and external test sets | per model | Validation reports | OBSERVADO |
| AI use disclosure | percent of submissions with a disclosure statement | anual | Journal submission records | DECLARADO |
| Contamination checks reported | percent of benchmark evaluation reports | anual | Evaluation reports | NOTIFICADO |
De dónde procede la evidencia.
Conjuntos de datos públicos y registros institucionales identificados, cada uno con su tipo de licencia. Las fuentes obtenidas por acuerdo solo se utilizan conforme a sus condiciones y nunca se republican.
| Fuente | Lo que aporta | Tipo de licencia |
|---|---|---|
| OpenML | Datasets, tasks and evaluation runs with standardised splits | Open, CC BY |
| Hugging Face Hub model and dataset cards | Documented models and datasets with stated training data and limitations | Licence per model or dataset |
| MLCommons benchmarks | Shared benchmarks and measurement rules for model performance | Open, Apache 2.0 |
| AI Incident Database (Responsible AI Collaborative) | Reported failures and harms of deployed AI sistemas | Abierta, con atribución |
| Software Heritage | Archived source code with persistent intrinsic identifiers | Open archive, licence per repository |
| arXiv metadata | Preprints across physics, computing and quantitative biology | Metadata CC0; licence per paper |
| Zenodo (CERN) | Citable deposits of code, models, data and evaluation outputs | Open, licence per record |
Quién decide y a qué sirve la evidencia.
Laboratorios prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Quién decide | La pregunta | Evidencia a la que sirve esta página |
|---|---|---|
| Journal editors and reviewers | Whether a model-based claim rests on an evaluation that excludes leakage and tests generalisation | REFORMS or TRIPOD+AI checklist, split design, external validation, code availability |
| Financiadores de la investigación | Whether applicants and reviewers may use generative AI, and on what disclosure terms | Funder policy, disclosure records, documented verification of generated content |
| Medicines and device regulators | Whether an AI component in a medical product is validated for its intended use | Prespecified evaluation, external validation, predetermined change control plan |
| Research integrity offices | Whether an allegation involving fabricated or generated content is substantiated | Provenance of text, data and code; version history; tool logs |
Productos que genera este tema
- Evaluation protocols that fix splits, metrics and baselines before training
- Model cards and datasheets with intended use, limits and known failure modes
- Leakage and contamination audits of model-assisted studies
- Reproducible pipelines with code, weights, environment and data versions archived
What experts ask about AI in research.
- What task is the model doing, and what is it compared against?
- Was the model evaluated on data independent of the data it was trained or tuned on?
- How does performance change when conditions shift away from the training data?
- Can the output be checked by a person or a simpler method, and is it?
- Which steps of the workflow were assisted, and is that disclosed in the result?
Where AI helps and where it misleads.
Machine learning models learn statistical regularities from data. They can be very effective where those regularities are stable and data are plentiful, for example classifying land cover from satellite imagery or emulating a climate model to explore many scenarios. They are weaker where data are sparse, where the system changes over time or where rare events matter most, which describes much of risk science.
Common failure modes are well understood: leakage, where information from the test set reaches training; cambio de distribución, where conditions in use differ from those in training; shortcut learning, where a model relies on a spurious cue; and overconfident outputs without calibrated uncertainty. Generative models add the risk of fluent but fabricated content, including citations and code that looks correct but is not.
Workflow effects matter as much as model accuracy. When a tool is used at several stages, small errors can accumulate, and reviewers may give automated outputs more trust than they deserve.
How AI tools are evaluated.
- 01
Held-out and out-of-distribution tests
Evaluation on data separated by time, place or source. This is the minimum for any claim of generalisation.
- 02
Baselines
Comparison with simple statistical methods and expert judgement. A complex model that does not beat a simple baseline is not adding value.
- 03
Calibration and uncertainty
Checks that stated confidence matches observed accuracy, using cuantificación de la incertidumbre.
- 04
Provenance and disclosure
A record of which model, version and prompts or settings were used at each step, so the workflow can be re-run or audited.
Benchmark scores rarely transfer directly to a new research setting, and a model’s behaviour can change between versions. Evaluation is repeated when either the task or the model changes.
Cómo se estudia y lo que los métodos no pueden decir.
- 01Inteligencia artificial y aprendizaje automáticoCribado asistido para revisiones sistemáticas · Modelos sustitutos para guiar experimentosAplicado aquí
- 02Cuantificación de la incertidumbre y validación de modelosPresupuestos de incertidumbre de medición · Análisis de sensibilidad de modelos de simulaciónAplicado aquí
- 03Probabilidad y estadísticaPlanificación del tamaño muestral y la potencia · Metaanálisis con evaluación de sesgosAplicado aquí
- 04Inferencia causal y evaluación de impactoPilotos aleatorizados y despliegues escalonados · Evaluación cuasiexperimental de programasAplicado aquí
- 05Ingeniería de sistemasTrazabilidad de los requisitos a la verificación · Revisiones de preparación por etapasAplicado aquí
Where this page’s references lead.
Los organismos y documentos con los que se lee esta página. El estado y las ediciones cambian; cada enlace lleva al organismo emisor.
Preguntas y respuestas.
What is data leakage?
Is a high benchmark score enough to publish a method?
Can a generative AI tool be listed as an author?
Why publish model weights and code?
Does Labs use generative AI to write findings?
Can Labs evaluate a commercial AI model?
Why compare with simple baselines?
Aporte evidencia desde esta página.
serie Reports is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
- Correo electrónicoPasarela GW-GCRIEnvíelo desde esta páginaEvidencia, una corrección, un caso, un método, una revisión, una traducción o un aporte a un perfil. Llega etiquetado con esta página, de modo que lo recibe el editor responsable.
- ZenodoDepósito abiertoDeposítelo y envíelo a la comunidadPublique su propio registro en Zenodo con su ORCID iD y luego envíelo a la comunidad de Reports allí. Los curadores lo revisan; el DOI sigue siendo suyo.
- serie ReportsGCRIVer todas las formas de contribuirLos tipos de contribución, cómo se revisa cada uno, qué crédito recibe, la licencia y qué nunca se acepta.
No envíe nunca datos personales, detalles sensibles de infraestructura ni nada que no tenga derecho a compartir. Un aporte se revisa antes de su uso; enviarlo no confiere autoría ni derechos editoriales, y el patrocinio nunca compra un hallazgo.
Aporte un método, un conjunto de datos o una pregunta.
La información y las consultas están abiertas hoy. Una consulta recibe una referencia e indica el siguiente paso; las solicitudes, pagos, asignaciones de cómputo y registros solo se abren cuando existen su equipo, sus condiciones y la capacidad de revisión.
Documentos jurídicos.
Los mapas, datos y cifras de este sitio se muestran como contexto e información general. No son consejos, pronósticos ni avisos; los límites de la información se exponen en los Descargos de responsabilidad y los Avisos sobre datos.