Behave-XAI: Deep Explainable Learning of Behavioral Representational Data

TL;DR

This paper introduces Behave-XAI, a deep learning framework for behavioral data analysis that emphasizes explainability, using neural networks to interpret user engagement based on contextual data and providing human-understandable explanations.

Contribution

It develops a novel deep neural network architecture for behavioral mining and integrates explainability techniques tailored for user-centric decision explanations.

Findings

Effective neural network models for behavioral data analysis.

User preference favors explainable AI over traditional models.

Enhanced credibility of AI decisions through explanations.

Abstract

According to the latest trend of artificial intelligence, AI-systems needs to clarify regarding general,specific decisions,services provided by it. Only consumer is satisfied, with explanation , for example, why any classification result is the outcome of any given time. This actually motivates us using explainable or human understandable AI for a behavioral mining scenario, where users engagement on digital platform is determined from context, such as emotion, activity, weather, etc. However, the output of AI-system is not always systematically correct, and often systematically correct, but apparently not-perfect and thereby creating confusions, such as, why the decision is given? What is the reason underneath? In this context, we first formulate the behavioral mining problem in deep convolutional neural network architecture. Eventually, we apply a recursive neural network due to the presence of time-series data from users physiological and environmental sensor-readings. Once the model is developed, explanations are presented with the advent of XAI models in front of users. This critical step involves extensive trial with users preference on explanations over conventional AI, judgement of credibility of explanation.