Centram Centre for Applied Artificial Intelligence

AI ASSISTED ANALYTICS AND AUTOMATION COURSE FOR BROADCASTING REGULATION

Course overview

This course is designed for broadcasting regulators to adapts AI/automation techniques to the specific needs of regulators, compliance teams, and policy units that oversee broadcast content, licensing, spectrum use, accessibility, and consumer protection.
Target audience

 Regulatory staff, compliance officers, policy analysts, technical managers in broadcasting authorities, legal advisors with technical interest, and data scientists

Course learning outcomes

  1. By the end of the course participants will be able to:
  2. Design and operate AI‑assisted pipelines to monitor, detect and audit broadcast content and license compliance.
  3. Apply automated tools for content classification, harmful content detection, accessibility (captions/audio description) verification, and broadcast metadata analytics.
  4. Implement reliable, auditable workflows with provenance, explainability and human‑in‑the‑loop review for regulatory decision making.
  5. Quantify system performance and uncertainty and design enforcement workflows minimizing false actions and protecting freedoms.
  6. Understand legal/ethical constraints (privacy, free expression, cross‑border content) and operationalize compliance under relevant statutes.

Course Outline

Introduction: regulatory goals, risks, and AI affordances

  1. – Objectives: Map regulator needs to AI capabilities; establish success criteria and constraints (legal, ethical, operational).
  2. Topics: Typical regulatory tasks (content monitoring, license compliance, spectrum interference), risk of automation (over‑blocking, bias), human oversight models.
  3. Lab: Problem scoping exercise — convert regulatory requirement (e.g., detect hate speech segments in broadcasts) into measurable ML tasks and evaluation metrics.

Data governance, provenance and audit trails for regulatory evidence

  1. Objectives: Build trustworthy data pipelines that preserve chain of custody and metadata required for enforcement and appeals.
  2. Topics: Metadata capture (timestamps, channel, device, ingest path), tamper‑evidence, retention policies, secure storage, access control, logging for audits.
  3. Tools/Patterns: WORM storage concepts, DVC, immutable logs, cryptographic hashes, ELK stack.
  4. Lab: Ingest sample broadcast streams and produce an auditable dataset with provenance metadata and tamper‑detection hashes.

 Audio and video ingestion, preprocessing and transcript generation

  1. Objectives: Automate ingestion and transform raw broadcast streams into searchable artifacts: transcripts, shot boundaries, scene metadata.
  2. Topics: Stream capture, container formats, audio/video codecs, ASR for broadcast audio, speaker diarization, OCR for on‑screen text, time alignment.
  3. Tools: FFmpeg, Whisper/WhisperX, Kaldi, SpeechBrain, pyannote, Tesseract, OpenCV.
  4.  Lab: Build pipeline: capture sample broadcast clip → ASR transcript + speaker segments + subtitle alignment.

Content classification & policy rule automation

  1. Objectives: Use ML to classify content categories relevant to regulation (e.g., political ads, children’s programming, extremist content, misleading claims).
  2. Topics: Multi‑label classification, video+audio+text fusion, temporal localization of segments, thresholding, ensemble methods, rule‑based hybrid systems.
  3. Tools: Hugging Face Transformers, PyTorch, OpenCV, multimodal models (CLIP, VideoCLIP).
  4.  Lab: Train/evaluate classifiers for content categories; produce segment-level labels for a broadcast episode.

Harmful content detection and human-in-the-loop workflows

  1. Objectives: Detect hate speech, disinformation, graphic content and design human review workflows to reduce false positives and ensure due process.
  2. Topics: Definition of harmful categories, error cost analysis (precision vs recall), escalation policies, active learning, annotation strategies.
  3. Tools: Labeling tools (Labelbox, Doccano), active learning libraries, UI prototypes for reviewer workflow.
  4.  Lab: Implement a triage pipeline: automated scoring → prioritized human review queue → feedback loop to retrain model.

Deepfake and manipulated content detection

  1. Objectives: Detect synthetic/manipulated audio/video and estimate confidence, provenance and recency.
  2.  Topics: Face/voice synthesis detection signals, temporal inconsistencies, metadata analysis, provenance verification, watermarking and content authentication.
  3.  Tools/Datasets: FaceForensics++, DFDC, audio deepfake datasets, XceptionNet variants, forgery detection methods.
  4.  Lab: Evaluate detectors on manipulated clips; build a detection + provenance report suitable for enforcement evidence.

Accessibility & broadcast technical compliance

  1. Objectives: Automate verification of closed captions, audio description, loudness (EBU R128), technical parameters and accessibility claim compliance.
  2. Topics: Caption presence/quality, alignment and readability metrics, speech‑to‑text comparison, loudness normalization checks, subtitle language detection.
  3. Tools: WebVTT/SRT parsers, CCS (caption standards), librosa, pyAudioAnalysis.
  4. Lab: Automate caption quality checks across a batch of broadcast segments and flag non‑compliant items.

 Spectrum and transmission analytics

  1. Objectives: Use analytics and automation to detect spectrum misuse, interference and unauthorized transmissions.
  2. Topics: Signal capture basics, FFT and spectral analysis, anomaly detection in time/frequency, geolocation basics, integration with sensor networks.
  3. Tools: GNU Radio, SDR hardware (RTL‑SDR), spectral analysis libraries, time‑series anomaly detection (Kats, Prophet).
  4. Lab: Simulate spectral data, detect anomalies and produce incident reports mapping suspected interference episodes.

Explainability, uncertainty quantification and defensible decisions

  1. Objectives: Produce explainable model outputs and quantify uncertainty so decisions can withstand scrutiny in appeals/courts.
  2. Topics: SHAP/LIME for text/audio/video features, counterfactual explanations, calibration, confidence intervals, human‑readable evidence bundles.
  3. Tools: SHAP, Captum, model calibration libraries, techniques for multimodal explainability.
  4. Lab: For a flagged broadcast clip, generate an evidence package: model scores, feature attributions, timestamps and transcript excerpts.

Compliance monitoring at scale: orchestration, alerts and dashboards

  1. Objectives: Build production pipelines for continuous monitoring, alerts, SLA handling and public reporting.
  2. Topics: Stream processing vs batch, alerting thresholds, dashboards, SLA metrics, incident lifecycle automation, retention and redaction workflows.
  3. Tools: Kafka, Spark/Flume, Airflow/Prefect, Prometheus + Grafana, ELK stack, Streamlit for quick dashboards.
  4. Lab: Construct a monitoring pipeline that ingests broadcasts, runs classifiers, records violations, and triggers simulated enforcement actions.

 Legal, policy and ethics for automated regulation

  1. Objectives: Understand legal constraints (freedom of expression, privacy, GDPR, cross‑border content), bias mitigation and transparency obligations.
  2. Topics: Domestic broadcasting law (examples: FCC, Ofcom, EU AVMSD), data protection, rights of appeal, minimization, algorithmic accountability, public reporting obligations.
  3. Activity: Draft a policy annex describing how a given automated system will be governed, including redress, auditing and transparency measures.

Capstone projects, audits and stakeholder communication

  1. Objectives: Present integrated projects; perform mock audits and produce stakeholder‑facing reports.
  2. Capstone: Student teams deliver a reproducible pipeline addressing a regulatory use case, plus a policy brief and demo.
  3.  Examples: automated caption audits across channels; deepfake incident detection + evidence handling; spectrum interference detection system.

Suggested labs and practical exercises

  1. Reproducible pipeline: ingest broadcast → transcript → detect restricted content → generate evidence report.
  2. Active learning loop so regulatory labeling effort is minimized.
  3. Triage dashboard with priority queues and appeal simulation.
  4. Synthetic deepfake generation + detection evaluation to measure robustness

Evaluation metrics and risk management

  1. Standard ML metrics: precision/recall/F1, ROC/AUC for classifiers.
  2. Operational metrics: time‑to‑detect, false alarm rates per million minutes, review load per regulator FTE.
  3. Legal/ethical metrics: proportion of automated actions appealed vs upheld, audit trail completeness score.
  4. Risk controls: human‑in‑the‑loop thresholds, conservative default thresholds, periodic recalibration and bias audits.

Tools, datasets and resources (recommended)

  1. Speech/ASR: Whisper, Kaldi, SpeechBrain, Common Voice, LibriSpeech.
  2. Vision & multimodal: OpenCV, CLIP, Video datasets (AVA), YouTube‑8M (where permitted), FaceForensics++, DFDC (deepfake).
  3. Metadata & stream processing: FFmpeg, Kafka, Spark, GNU Radio (for spectrum).
  4.  Labeling & pipelines: Doccano, Labelbox, Label Studio, Airflow/Prefect, DVC.
  5.  Monitoring & observability: Prometheus, Grafana, ELK (Elasticsearch‑Logstash‑Kibana).
  6. Explainability & uncertainty: SHAP, Captum, conformal prediction libs, calibration tooling.
  7.  Legal/policy references: AVMSD (EU), FCC rules (US), Ofcom guidance (UK), GDPR, national Broadcasting Acts.

Capstone project ideas

  1. Automated caption compliance audit across a broadcaster’s schedule, with prioritized remediation.
  2. Real‑time triage for potentially harmful live segments (classifier + human escalation).
  3. Deepfake detection pipeline integrated with provenance logging and public disclosure workflow.
  4. Spectrum anomaly detection for a region using SDR sensor network and automated incident reporting.
  5. Policy simulation tool: evaluate how different detection thresholds affect false positive burden and legal appeals.

Customization and deployment considerations

  1. Domain adaptation: tailor modules for public service broadcasters, commercial broadcasters, or community/local regulators.
  2. Scale: prototype on archived content, then plan for streaming scale and retention/storage costs.
  3.  Red teaming: include adversarial robustness testing (e.g., methods to evade detectors).
  4. Governance: establish cross‑functional teams (legal, technical, ops) and review cadence.