Turing-Tasawuf Ethics for AI Bias Prevention
TL;DR
The study concludes that Tasawuf ethics can be translated into auditable computational mechanisms when ethical intention, iterative purification, continuous monitoring, periodic auditing, fairness optimization, and transparent limitation disclosure are embedded into the AI development cycle.
Abstract
Artificial intelligence increasingly mediates decisions in recruitment, finance, facial recognition, public service delivery, and criminal risk assessment, yet algorithmic bias remains a persistent ethical and technical threat. This study aims to develop and evaluate an integrative ethical framework that connects Akhlak Tasawuf with Alan Turing's computational theory for the prevention of digital bias. A mixed-methods design was employed using a convergent parallel strategy. Qualitative data were obtained through textual analysis of classical Sufi sources and Turing-related works, semi-structured interviews with 35 experts, and three interdisciplinary focus group discussions. Quantitative data were obtained from bias testing across 12 machine learning systems using benchmark datasets and fairness metrics. The findings produced the Turing-Tasawuf Ethical Model (TTEM), consisting of the Niyyah Protocol, Tazkiyah Pipeline, Muraqabah Module, Muhasabah Audit, Ihsan Optimization, and Tawadu' Disclosure. The model improved the composite fairness score from 0.412 to 0.712, indicating a 72.8% increase after implementation. Expert acceptance was also strong, with an overall mean score of 4.21 on a five-point scale. The study concludes that Tasawuf ethics can be translated into auditable computational mechanisms when ethical intention, iterative purification, continuous monitoring, periodic auditing, fairness optimization, and transparent limitation disclosure are embedded into the AI development cycle.