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

2026-01-23

AI Summary

This study addresses the misalignment between existing AI fairness evaluation tools and the Telecom Engineering Centre (TEC) of India’s national standards, which hinders compliance auditing in high-risk applications. To bridge this gap, the work presents the first operational web-based auditing framework that implements TEC’s AI assessment criteria. The framework integrates vectorized computation, reactive state management, survey-informed risk quantification, and standardized reporting to enable reproducible and auditable fairness evaluations. Validated on the COMPAS dataset, it effectively identifies attribute-specific biases and generates TEC-compliant fairness scores alongside certification-ready reports. This approach significantly narrows the implementation gap between academic fairness methodologies and localized AI governance requirements in the Indian regulatory context.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Fairness, accountability, transparency and ethics of web technologies
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

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.

Problem

• AI fairness
• regulatory compliance
• TEC standard
• bias auditing
• telecom AI

Innovation

• TEC Standard
• Fairness Auditing
• Vectorized Computation
• Regulatory AI Governance
• Certification-Ready Reporting