Centram Centre for Applied Artificial Intelligence

Records & Archive Management

AI ANALYTICS AND AUTOMATION COURSE FOR RECORDS AND ARCHIVE MANAGEMENT

Course Outline

This course is designed for Information professionals, Archivists, Records managers, and IT staff who need practical skills in applying AI, machine learning, NLP, OCR, and automation to records lifecycle, discovery, compliance, and preservation.

Course Title

 AI Analytics & Automation for Records and Archive Management

Learning Objectives

By the end of the course learners will be able to:

  1. Apply AI/NLP/ML techniques to classify, extract, and analyze records across formats.
  2. Design and implement automated workflows (RPA, rules, ML pipelines) for records lifecycle tasks (capture, indexing, retention, disposition).
  3. Use OCR, entity extraction, and search technologies to improve discovery and access to archival content.
  4.  Define governance, privacy, ethical, and legal frameworks needed for AI use in records contexts.
  5.  Build a practical prototype or production-ready workflow that automates a records management process with measurable outcomes.

Target Audience

Records Managers, Archivists, Digital preservationists, Information Governance professionals, Data stewards, IT staff

Course Duration and Delivery Methods

This is for 2-weeks intensive physical boot camp training plus 3 weeks of online training OR 4 weeks of intensive physical training

Course Content

 Records & Archives in the Age of AI

  1. Topics: Records lifecycle, archival principles, digital curation; opportunities & limits of AI in records management
  2. Outcomes: Map records lifecycle to potential automation points
  3. Lab: Inventory a sample records series; outline automation opportunities
  4. Data, Metadata and Governance
  5. Topics: Metadata standards (Dublin Core, EAD, METS), data quality, provenance, retention policies, legal & compliance basics
  6. Outcomes: Design a metadata schema for automated processing
  7. Lab: Create/clean a dataset of metadata records (OpenRefine)

Intro to Machine Learning & NlP for Records

  1. Topics: Supervised vs unsupervised learning, evaluation metrics, tokenization, NER, text classification
  2. Outcomes: Choose ML/NLP approaches for typical RM tasks
  3. Lab: Text pre-processing pipeline in Python (pandas, scikit-learn)

OCR, Handwriting Recognition & Content Ingestion

  1. Topics: OCR engines (Tesseract, Google Vision), layout analysis, handwritten text recognition, ingest pipelines
  2. Outcomes: Implement OCR + post-processing workflow
  3. Lab: OCR scans -> searchable text; measure OCR accuracy; error correction heuristics

Classification & Automated Filing

  1. Topics: Automated classification, taxonomies, supervised models, active learning, model monitoring
  2. Outcomes: Train & evaluate a classifier for records types
  3. Lab: Build/evaluate classifier (scikit-learn or Hugging Face transformer)

Named Entity Recognition & Information Extraction

  1. Topics: NER, relation extraction, redaction, structured data extraction from unstructured records
  2. Outcomes: Extract key fields (names, dates, identifiers) and validate against rules
  3. Lab: Use spaCy/transformers to extract entities; build simple validation rules

Search, Indexing & Semantic Discovery

  1. Topics: Full‑text search, faceted search, ElasticSearch/Solr, embeddings & semantic search (vector DBs)
  2.  Outcomes: Set up a search index for archival content; implement semantic search
  3. Lab: Index OCR/text; implement keyword + semantic search UX

Automation & Workflow Orchestration

  1. Topics: RPA (UiPath, Power Automate), workflow engines, event-driven architectures, API integrations
  2. Outcomes: Design an automated records intake-to-retention workflow
  3. Lab: Build a simple RPA or serverless workflow to route/label incoming records

Automated Retention, Disposition & Legal Holds

  1. Topics: Rule-based vs ML-driven retention, disposition automation, audit trails, legal holds and defensible disposal
  2. Outcomes: Define retention automation policies and implement a proof-of-concept
  3.  Lab: Implement retention rules engine; simulate disposition cycle and audit log

Digital Preservation & Long-Term Access

  1. Topics: Preservation strategies, checksums, format migration, Archivematica/Preservica integrations
  2. Outcomes: Integrate preservation steps into automated pipelines
  3. Lab: Run a preservation ingest in Archivematica; automate fixity checks

Privacy, Ethics & Risk Management

  1. Topics: Bias, explainability, data minimization, privacy-preserving ML, regulatory compliance (GDPR, HIPAA)
  2. Outcomes: Create an AI/ethics checklist and risk mitigation plan
  3. Lab: Audit an ML model for privacy risks and propose mitigations (pseudonymization, access controls)

Capstone: Prototype Deployment & Presentations

  1. Activities: Final project demos, peer review, deployment checklist, roadmaps for adoption
  2.  Outcomes: Deploy prototype or present a roadmap with ROI and governance plan

Hands-On Tools & Technologies (Recommended Stack)

  1. Programming: Python (pandas, scikit-learn), Jupyter notebooks
  2. NLP/ML: spaCy, Hugging Face Transformers, scikit-learn
  3. OCR: Tesseract, Google Cloud Vision, AWS Textract (choose per cloud preference)
  4.  Search/index: ElasticSearch, OpenSearch
  5.  Preservation: Archivematica, BagIt, Fixity tools
  6. Automation/RPA: UiPath, Microsoft Power Automate, Apache NiFi, AWS Step Functions or simple serverless functions
  7. Vector DBs/semantic search: Milvus, Pinecone, FAISS
  8.  Data cleaning: OpenRefine
  9.  Deployment: Docker, simple cloud instances (AWS/GCP/Azure)
  10.  Logging & monitoring: ELK stack, Prometheus, or managed equivalentsPractical

Lab Exercises

  1. Lab: Batch OCR pipeline -> clean text -> extract names/dates -> write metadata CSV
  2. Lab: Train a classifier to tag records as public/confidential/internal using 1,000 labeled documents
  3. Lab: Build an automated intake flow that ingests email attachments, extracts metadata, classifies, and routes to a record store
  4. Lab: Implement semantic search using embeddings to find related records across collections
  5. Lab: Simulate legal hold: tag related documents automatically and freeze disposition processes

Datasets & Sample Content

Use anonymized organizational documents, FOIA redactions, public archives (e.g., U.S. National Archives public datasets), synthetic datasets created via data generation scripts, or publicly available corpora (Enron emails for email records patterns; public meeting minutes).

Governance, Ethics & Risk Frameworks

  1. Include templates: AI use policy for archives, model approval checklist, data retention + disposition policy, audit trail specification, privacy impact assessment (PIA) template.

Instructor Notes & Logistics

  1. Infrastructure: cloud lab environment or preconfigured Docker images/JupyterHub
  2. Practicing Records Managers to present case studies

Module Learning

  1. Module: OCR & Ingest — Learners will run OCR on scanned records, evaluate accuracy, and implement post‑OCR cleanup to produce reliable searchable text.
  2. Module: Classification — Learners will build and validate a model to automatically assign records to retention categories with precision/recall reporting.
  3.  Module: Automation — Learners will create an automated workflow to ingest, classify, assign retention, and create audit logs for records.

Recommended Reading & Resources

  1. “Records Management and Information Culture” — professional texts and standards
  2.  NARA, ISO 15489, ISO 14721 (OAIS)
  3.  spaCy and Hugging Face tutorials, ElasticSearch documentation, Archivematica guides
  4.  Research papers on NLP for legal/archival domains, privacy-preserving ML

Deliverable Templates Provided to Learners

  1. Metadata schema template (Dublin Core + custom fields)
  2.  Model risk assessment form
  3.  Retention rule / disposition script examples
  4. Workflow design canvas (inputs, logic, outputs, monitoring