Process Knowledge-infused Learning for Suicidality Assessment on Social Media

TL;DR

This paper introduces Process Knowledge-infused Learning (PK-iL), a novel AI paradigm that incorporates structured process knowledge to improve suicidality assessment on social media and provide human-understandable explanations.

Contribution

The paper formalizes and develops PK-iL, integrating process knowledge into deep learning for better explanations and competitive performance in suicidality detection.

Findings

Human annotator agreement of 0.72 on explanations

PK-iL performs competitively with state-of-the-art methods

Provides explanations understandable to end-users

Abstract

Improving the performance and natural language explanations of deep learning algorithms is a priority for adoption by humans in the real world. In several domains, such as healthcare, such technology has significant potential to reduce the burden on humans by providing quality assistance at scale. However, current methods rely on the traditional pipeline of predicting labels from data, thus completely ignoring the process and guidelines used to obtain the labels. Furthermore, post hoc explanations on the data to label prediction using explainable AI (XAI) models, while satisfactory to computer scientists, leave much to be desired to the end-users due to lacking explanations of the process in terms of human-understandable concepts. We \textit{introduce}, \textit{formalize}, and \textit{develop} a novel Artificial Intelligence (A) paradigm -- Process Knowledge-infused Learning (PK-iL). PK-iL utilizes a structured process knowledge that explicitly explains the underlying prediction process that makes sense to end-users. The qualitative human evaluation confirms through a annotator agreement of 0.72, that humans are understand explanations for the predictions. PK-iL also performs competitively with the state-of-the-art (SOTA) baselines.