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 scoring framework for quantifying the causal role that algorithms, users, and design systems play in digital trauma. 

Unlike existing fairness audits or moderation tools, APHD produces two interpretable metrics: the Proximate Harm Score (PHS), which assesses node-level contribution to specific harm events, and the Systemic Harm Index (SHI), which captures broader structural failures in platform governance. These metrics are derived from nine normalized variables—including recurrence, exposure delta, silence suppression, and platform negligence—operating on a harm graph constructed from platform logs, moderation pathways, and social signals.

We introduce an Adaptive Multi-Stage Bat Algorithm (AMSBA) to optimize variable weighting, and use SHAP for post-hoc attribution and forensic explainability, ensuring alignment with legal evidentiary standards (e.g., Daubert, Frye). APHD also integrates a predictive behavioral module (SignatureProfiler) and a Cooperative Memory Replication (CMR) system for tracing distributed harm across linked accounts. 

Grounded in lived experience, graph theory, and tort logic, APHD offers a scalable, court-admissible method for detecting and explaining algorithmic harm—not only after it occurs, but as it begins to unfold.