The rise of explainable AI in data analytics: Making complex models transparent for business insights

13^th Global Summit on Artificial Intelligence and Neural Networks

March 14, 2025 | Webinar

Shafeeq Ur Rahaman

Associate Director, Analytics, CA, USA

Scientific Tracks Abstracts: J Comput Sci Syst Biol

Abstract :

Advanced machine-learning pipelines deliver unrivaled predictive power, yet their black-box nature inhibits adoption in highÃ-stakes domains where accountability and trust are non-negotiable. This study charts the rise of Explainable AI (XAI) as a pragmatic bridge between model complexity and stakeholder comprehension, enabling organisations to convert opaque outputs into actionable, auditable business insights. I present a three-layer framework that integrates: (1) Intrinsic and post-hoc interpretability techniques including SHAP, LIME and counterfactual narratives to surfaces feature attributions and decision paths; (2) Bias-detection and error-analysis loops that quantify disparate impact and highlight risky model regions; and (3) Governance artefacts such as explanation logs and transparency checkpoints that embed accountability in the MLOps life-cycle. Methodologically, the work triangulates evidence from a systematic review of 2013-2024 literature, practitioner interviews across finance, healthcare, retail and manufacturing, and a quantitative evaluation of XAI-enabled platforms versus traditional black-box deployments. Across ten industry case studies, XAI lifted decision accuracy by 10 to 30 percent, reduced model-drift incidents by 22 percentage, and in exemplar scenarios cut inventory overstock costs by 18 percentage and manufacturing downtime by 25percentage, without sacrificing predictive AUC (greater than 0.88). Findings crystallise into a practical roadmap for data leaders: adopt bias-auditing checklists, insert explainability gates in CI/CD, and align transparency metrics with regulatory man dates such as GDPRs right-to-explanation. By demystifying complex algorithms, XAI accelerates stakeholder buy-in, mitigates compliance risk, and closes the credibility gap between data scientists and business decision-makers. Ultimately, transparent analytics emerges not as a nice-to-have, but as a strategic imperative for organisations seeking sustainable, ethical and high-impact AI adoption. Key industry applications include: Finance: Real-time credit-risk scoring with transparent feature attributions that satisfy regulators. Healthcare: Clinical-decision support systems that explain patient-specific recommendations to clinicians. Retail E-commerce: Interpretable recommender engines that boost conversion while surfacing bias alerts. Manufacturing: Predictive-maintenance models whose explanations localize component failure drivers. Telecommunications: Fraud-detection pipelines where feature importance traces enable rapid root-cause analysis.

Biography :

Shafeeq Ur Rahaman is Associate Director of Analytics & Data Infrastructure at Monks, where he architects cloud-native data platforms and directs a 60-person global team supporting media operations with annual budgets between $80 million and $110 million. Over more than twelve years in digital advertising, supply-chain logistics, and finance, he has designed high-volume pipelines that ingest data from 30-plus marketing channels, cut reporting latency from weeks to minutes, and embedded predictive models that raise campaign-evaluation accuracy by up to 40 percent. A hands-on technologist fluent in Google BigQuery, Python, R, Spark, Looker Studio, and AppScript, Shafeeq complements engineering rigor with robust data-governance practices. He has published more than twenty peer-reviewed articles, serves as a reviewer and session chair for IEEE and Elsevier venues, and actively mentors the analytics community. Holding a Master of Science in Management Information Systems from the University of Illinois Springfield, he is a Senior Member of IEEE and a Full Member of Sigma Xi, among other professional societies that reflect his commitment to advancing trustworthy, high-impact data science.