<ul><li>Organizations developing machine learning-based (ML) technologies face the challenge of achieving high predictive performance while respecting the law.</li><li>ML model behavior cannot be directly operationalized in source code to meet legal obligations.</li><li>A five-stage interdisciplinary framework is introduced to integrate legal and ML-technical analysis during ML model development.</li><li>The framework helps design legally aligned ML models and identify high-performing models that are legally justifiable.</li></ul>

Engineering the Law-Machine Learning Translation Problem: Developing Legally Aligned Models

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