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.

翻译：随着人工智能（AI）模型在高风险决策系统（尤其是新兴电信与6G应用领域）中的日益普及，对透明化、标准化公平性评估框架的需求变得尤为迫切。尽管全球性工具包（如IBM AI Fairness 360和Microsoft Fairlearn）已在偏见检测方面取得进展，但它们往往未能与特定地区的监管要求和国家优先事项保持一致。为弥补这一空白，我们提出了Nishpaksh——一款本土化的公平性评估工具，该工具将《电信工程中心（TEC）人工智能系统评估与评级标准》进行了操作化实施。Nishpaksh将基于调查的风险量化、情境化阈值确定和定量公平性评估集成至统一的基于Web的仪表板中。该工具采用向量化计算、响应式状态管理和符合认证要求的报告生成机制，以实现可复现、审计级评估，从而满足标准化后关键的实施需求。在COMPAS数据集上的实验验证表明，Nishpaksh能有效识别特定属性偏见，并生成符合TEC框架的标准化公平性分数。该系统弥合了研究导向的公平性方法与印度监管性AI治理之间的鸿沟，标志着在电信等关键基础设施领域迈向负责任、可审计的AI部署的重要一步。