Counterfactual Explanation Robustness to Adversarial Noise

Establish a privacy‑preserving, domain‑aware causal graph that will guide all downstream counterfactual generation.

Steps

• Domain Analysis & Data Audit(4 wks)
  Collect and audit multimodal datasets (image, text, graph) for quality, bias, and privacy constraints.
• Causal Discovery(6 wks)
  Apply fast, graph‑free algorithms (FCI, GAC) and expert‑in‑the‑loop validation to learn a causal structure.
• Differential Privacy & Feature Selection(4 wks)
  Implement DP‑aware feature pruning to ensure individual‑level privacy while preserving causal fidelity.
• Causal Graph Validation(4 wks)
  Run simulation tests (interventional queries) and cross‑validation against known causal benchmarks.

Milestones

Risks

• Causal graph overfitting to spurious correlations
• Insufficient privacy guarantees leading to regulatory issues

Build and fine‑tune a DDPM backbone that can project adversarial perturbations onto the data manifold for each modality.

Steps

• Diffusion Backbone Selection(4 wks)
  Benchmark DDPM, DDIM, and DPM‑Solver variants on image, text, and graph encoders.
• Modality‑Specific Fine‑Tuning(8 wks)
  Train diffusion models on domain‑specific datasets with guidance strength tuning.
• Manifold Projection Engine(6 wks)
  Implement Fτ filtering and integrate with causal steering to generate on‑manifold counterfactuals.
• Performance Benchmarking(4 wks)
  Measure fidelity, speed, and artifact suppression against baseline gradient‑based methods.

Milestones

Risks

• Diffusion training instability on high‑dimensional graph data
• Inference latency exceeding real‑time constraints

Dependencies

• Phase 1 Causal Graph Release

Develop a unified recourse engine that generates actionable counterfactuals across image, text, and graph modalities while respecting cross‑modal causal constraints.

Steps

• Cross‑Modal Embedding Alignment(4 wks)
  Train shared latent space with contrastive loss and cross‑modal consistency regularization.
• Adversarial Recourse Generator(6 wks)
  Extend diffusion projection to jointly perturb multimodal inputs under causal steering.
• Actionability Scoring(4 wks)
  Implement cost models (semantic, clinical, operational) and integrate with RO‑Lp optimizer.
• User‑Facing API Design(4 wks)
  Expose MARM as a RESTful service with HL7/FHIR adapters for healthcare use cases.

Milestones

Risks

• Cross‑modal alignment failure leading to incoherent explanations
• Regulatory compliance gaps in healthcare data handling

Dependencies

• Phase 2 Diffusion Model Release

Implement Lp‑bounded optimization and a robustness oracle that simulates adversarial model shifts to validate CE fidelity.

Steps

• RO‑Lp Optimizer Implementation(4 wks)
  Translate min‑max formulation into a convex‑relaxation solver with GPU acceleration.
• Oracle Simulation Engine(6 wks)
  Generate adversarial model variants (poisoning, fine‑tuning, distribution shift) and evaluate counterfactual validity.
• Robustness Metric Suite(4 wks)
  Implement multiplicity‑based score, fairness audit, and bias detection modules.
• End‑to‑End Validation Pipeline(4 wks)
  Automate oracle testing and report generation for continuous integration.

Milestones

Risks

• Solver convergence issues under high‑dimensional constraints
• Oracle mis‑simulation leading to false confidence

Dependencies

• Phase 3 MARM API Prototype

Deploy the FCA system in a controlled multi‑agent environment, gather real‑world feedback, and prepare for full production.

Steps

• System Integration(4 wks)
  Integrate causal graph, diffusion engine, MARM, optimizer, and oracle into a unified micro‑service architecture.
• Pilot Deployment(6 wks)
  Run the system in a simulated autonomous driving or clinical decision support pilot with live adversarial monitoring.
• User Study & Trust Metrics(4 wks)
  Collect qualitative and quantitative data on explanation usefulness, actionability, and trust.
• Production Readiness & Scaling(4 wks)
  Implement autoscaling, monitoring dashboards, and CI/CD pipelines for continuous delivery.

Milestones

Risks

• Unanticipated latency spikes in production
• Pilot participants not representative of target user base

Dependencies

• Phase 4 Robustness Score Threshold