Machine Learning Interpretability and Its Impact on Smart Campus Projects

TL;DR

This paper explores how machine learning interpretability influences the development and deployment of smart campus projects, highlighting its role in enhancing system transparency and decision-making.

Contribution

It introduces the application of ML interpretability techniques specifically within smart campus projects, demonstrating their impact on system effectiveness and stakeholder trust.

Findings

ML interpretability improves decision transparency in smart campus systems

Enhanced interpretability leads to better stakeholder trust and system adoption

Interpretability techniques facilitate more effective IoT and SDN integration

Abstract

Machine learning (ML) has shown increasing abilities for predictive analytics over the last decades. It is becoming ubiquitous in different fields, such as healthcare, criminal justice, finance and smart city. For instance, the University of Northampton is building a smart system with multiple layers of IoT and software-defined networks (SDN) on its new Waterside Campus. The system can be used to optimize smart buildings energy efficiency, improve the health and safety of its tenants and visitors, assist crowd management and way-finding, and improve the Internet connectivity.