Independent Ethical Assessment of Text Classification Models: A Hate Speech Detection Case Study

TL;DR

This paper develops a comprehensive independent ethical assessment process for hate speech detection models, integrating qualitative and quantitative measures to evaluate bias, performance, and interpretability.

Contribution

It bridges the gap between high-level ethical frameworks and quantitative metrics, enabling practical independent ethical assessments of text classification models.

Findings

The process effectively identifies biases in hate speech detection models.

It combines protected attribute mining with counterfactual analysis for bias evaluation.

Demonstrated on a deep hate speech detection model, showing practical applicability.

Abstract

An independent ethical assessment of an artificial intelligence system is an impartial examination of the system's development, deployment, and use in alignment with ethical values. System-level qualitative frameworks that describe high-level requirements and component-level quantitative metrics that measure individual ethical dimensions have been developed over the past few years. However, there exists a gap between the two, which hinders the execution of independent ethical assessments in practice. This study bridges this gap and designs a holistic independent ethical assessment process for a text classification model with a special focus on the task of hate speech detection. The assessment is further augmented with protected attributes mining and counterfactual-based analysis to enhance bias assessment. It covers assessments of technical performance, data bias, embedding bias, classification bias, and interpretability. The proposed process is demonstrated through an assessment of a deep hate speech detection model.