Algorithmic Proximate Harm Detection (APHD): A Forensic Framework for Quantifying Causal Exposure and Systemic Design Failure in Algorithmic Platforms

Algorithmic platforms do not merely reflect risk—they actively shape it. From content curation to user matching, optimization systems determine who is seen, silenced, or re-exposed to harm. This paper introduces Algorithmic Proximate Harm Detection (APHD), a forensic framework for quantifying the causal role that algorithms, users, and design systems play in digital trauma. Unlike existing fairness audits or moderation tools, APHD generates two interpretable metrics: the Proximate Harm Score (PHS), which assesses node-level contribution to a specific harm event, and the Systemic Harm Index (SHI), which identifies structural platform failures. The model synthesizes nine variables—including recurrence, exposure delta, and platform negligence—into a graph-based scoring system. We apply an Adaptive Multi-Stage Bat Algorithm (AMSBA) to optimize coefficient tuning and use SHAP values to support legal interpretability. Grounded in lived experience and legal theory, APHD offers a scalable, court-admissible method for identifying and intervening in algorithmic harm—not after it occurs, but as it takes shape.