Nishpaksh: TEC Standard-Compliant Framework for Fairness Auditing and Certification of AI Models

TL;DR

Nishpaksh is a standardized, web-based fairness auditing framework for AI models in telecom, aligning with TEC standards to ensure transparent, reproducible, and regulation-compliant bias assessment.

Contribution

It introduces Nishpaksh, a novel indigenous tool that operationalizes TEC standards for AI fairness evaluation, integrating risk quantification and certification-ready reporting.

Findings

Effective bias detection on COMPAS dataset

Compliance with TEC fairness scoring standards

Bridges research and regulatory AI governance in India

Abstract

The growing reliance on Artificial Intelligence (AI) models in high-stakes decision-making systems, particularly within emerging telecom and 6G applications, underscores the urgent need for transparent and standardized fairness assessment frameworks. While global toolkits such as IBM AI Fairness 360 and Microsoft Fairlearn have advanced bias detection, they often lack alignment with region-specific regulatory requirements and national priorities. To address this gap, we propose Nishpaksh, an indigenous fairness evaluation tool that operationalizes the Telecommunication Engineering Centre (TEC) Standard for the Evaluation and Rating of Artificial Intelligence Systems. Nishpaksh integrates survey-based risk quantification, contextual threshold determination, and quantitative fairness evaluation into a unified, web-based dashboard. The tool employs vectorized computation, reactive state management, and certification-ready reporting to enable reproducible, audit-grade assessments, thereby addressing a critical post-standardization implementation need. Experimental validation on the COMPAS dataset demonstrates Nishpaksh's effectiveness in identifying attribute-specific bias and generating standardized fairness scores compliant with the TEC framework. The system bridges the gap between research-oriented fairness methodologies and regulatory AI governance in India, marking a significant step toward responsible and auditable AI deployment within critical infrastructure like telecommunications.