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+# BiasXplainer — Roadmap (expanded)
+
+This roadmap outlines planned work, priorities and potential milestones for BiasXplainer. Use this as a living document to coordinate development and contributions.
+
+## Short-term (next 1-3 months)
+
+1. Improve documentation and tests
+   - Add unit tests for `CounterfactualGenerator` template expansions and replacements.
+   - Add examples for SHAP fallback behavior and batch export flows.
+2. Packaging & CI
+   - Add GitHub Actions for linting (black/isort), tests (pytest) and build checks.
+   - Create a minimal PyPI package configuration (check `setup.py`) and publish a beta release.
+3. Better PDF/Export support
+   - Replace the lightweight PDF stub with a proper PDF renderer (WeasyPrint or ReportLab).
+
+## Medium-term (3-9 months)
+
+1. Model improvements
+   - Provide a small fine-tuned model checkpoint trained to detect gendered stereotypes more accurately.
+   - Add model calibration utilities and evaluation scripts (precision/recall by subgroup).
+2. Explainability & UX
+   - Improve SHAP sampling strategy to be faster and provide deterministic seeds for reproducibility.
+   - Add a compact REST API wrapper (FastAPI) for programmatic access with rate limiting for batch jobs.
+3. Evaluation & datasets
+   - Collect a small labeled dataset for bias detection (ethical, consented sources) to benchmark model performance.
+
+## Long-term (9-18 months)
+
+1. Integration & plugins
+   - Build a plugin system to allow domain-specific rule-sets or templates for counterfactuals.
+   - Provide browser extensions or editor plugins (VS Code) to highlight biased text in the editor.
+2. Governance & community
+   - Create contribution guidelines, a Code of Conduct and a roadmap board for public issue prioritization.
+
+## Optional / Nice-to-have
+
+- Multi-lingual support: extend detectors and templates for non-English languages.
+- Real-time inference: WebSocket-backed service that highlights text as the user types.
+- Active learning loop: allow human reviewers to confirm/correct model predictions and use those labels to refine the model.
+
+## Contributors & Ownership
+
+Primary owner: dyra-12 (repository)
+Open to contributors: yes — accept PRs with tests and clear descriptions.
+
+## How to help
+
+- File issues with concrete reproduction steps.
+- Provide small PRs: docs, tests, or bugfixes are the most welcome.
+- Validate exports and edge-cases with real sample text from target domains.
+
+---
+
+This roadmap is intentionally lightweight — if you want a project board or milestones created in GitHub I can scaffold a GitHub Projects board and open issues for the short-term items.