Technical assessment service
Technical assessment of data models, algorithms and machine-learning systems with attention to quality, fairness, robustness and traceability.
Technical expert assessment of data-driven systems
Data Science and AI systems combine data, statistical assumptions, model artefacts and runtime environments. A defensible technical assessment therefore identifies the relevant data and model state before performance, fairness or robustness are interpreted.
Typical examination areas
- Data sources, provenance, completeness, consistency and transformation history.
- Training/test separation, evaluation metrics, cross-validation, overfitting and possible leakage.
- Model architecture, artefact version, parameters, preprocessing and runtime environment.
- Representativeness, subgroup coverage, data skew and distribution shift.
- Bias and fairness metrics such as selection rates, true-positive and false-positive rates.
- Robustness under perturbations, edge cases, degraded input and adversarial conditions.
- Documented technical implementation of privacy- or AI-related requirements where technically examinable.
Methodological approach
The assignment defines target population, model and data versions, metrics, acceptance criteria and the evidence required for each technical statement. Statistical tests, model validation and audit techniques are selected for the concrete system. Findings, counter-hypotheses, uncertainty and limitations are documented separately from regulatory or legal evaluation.
Public frameworks such as NIST AI RMF may provide technical orientation where relevant to the assignment. Their use does not itself establish conformity of a particular system.
Typical applications
- Assessment of ML models embedded in products or enterprise systems.
- Technical examination of scoring, recommendation or forecasting systems.
- Fairness, robustness and reproducibility reviews.
- Technical preparation for internal AI or compliance audits.
Objective and limits
The report can provide a professional technical basis for management, regulatory or legally influenced decisions. It does not constitute legal advice and does not guarantee any particular regulatory or procedural outcome.
Example scope of an assessment
- Analysis of data sources and model architecture.
- Examination of training and test data for quality, representativeness and possible bias.
- Assessment of model validity, documentation and reproducibility.
- Report with findings, professional assessment, limitations and technically justified improvement options.
Indicative fee range: approximately €1,500–€4,000 net, depending on model complexity, data volume and evidence quality. The amount is for orientation only and is not a binding offer.
Related technical knowledge
Constructed case study: Data Science and AI assessment
A structured examination of data, model state, validation evidence and technical limitations.
Constructed examination scenario: This is a methodological example, not a real mandate, a real court appointment or a report of actual findings.
Diagnosis – establish data and model state
Identify data sources, transformations, model artefacts, model version, preprocessing and runtime environment. Record missing lineage, potential bias and reproducibility gaps.
Objectives – define metrics and reference criteria
State the target population, relevant performance or fairness metrics, acceptance criteria and the technical questions to be answered before analysing results.
Analysis – test data and model behaviour
Examine data quality, validation design, leakage, subgroup behaviour, robustness and reproducibility using methods appropriate to the system and available evidence.
Expert synthesis – technical findings and boundaries
Connect observed results to the predefined criteria and document uncertainty, distribution limits and plausible alternative explanations. Legal or regulatory compliance is a separate assessment.
Methodological inspiration: Nicolai Andler, Tools für Projektmanagement, Workshops und Consulting – Kompendium der wichtigsten Techniken und Methoden, 6th revised and expanded edition, Publicis, Erlangen, 2015, ISBN 978-3-89578-453-8. The examination sequence and expert synthesis used here are independently formulated for technical expert work.
Initial technical classification of an enquiry
Before accepting an assignment, the technical question, subject of examination, available records, potential conflicts of interest and the required scope are reviewed. An individual non-binding proposal can then be prepared.
Contact Mathias Ellmann