Abstract
In this chapter, the authors explore the open challenges and research issues related to XAI in modern smart cities. They begin by providing an overview of XAI and its importance in smart cities. They then discuss the key challenges of developing XAI systems for smart cities, including the need for transparency, interpretability, and trustworthiness. They also examine the challenges of integrating XAI systems with existing infrastructure and data sources in smart cities. Finally, they explore the potential research issues and future research directions for XAI in smart cities, including the development of new XAI techniques and the exploration of ethical and societal implications of XAI in smart cities. Overall, this chapter provides a comprehensive overview of the open challenges and research issues related to XAI in modern smart cities, which can serve as a roadmap for researchers, policymakers, and practitioners working in this field.
TopIntroduction
A smart city is an urban area that uses advanced technologies and data analysis to improve the quality of life of its residents, enhance sustainability, and optimize urban services such as transportation, energy, and public safety. The goal of a smart city is to leverage technology and data to make the city more efficient, accessible, and responsive to the needs of its citizens. A smart city is a concept that integrates technology and data to enhance the quality of life of its citizens, increase sustainability, and improve efficiency of urban services. It aims to use advanced technologies, such as the Internet of Things (IoT), Artificial Intelligence (AI), and Big Data, to address urban challenges and provide better services to its residents. The following are some of the application domains of smart cities:
- 1.
Transportation: Smart transportation systems aim to improve traffic flow, reduce congestion, and enhance safety by using data-driven solutions. These systems include intelligent traffic management systems, smart parking solutions, and electric vehicle charging stations.
- 2.
Energy: Smart energy systems aim to reduce energy consumption and increase the use of renewable energy sources. These systems include smart grid solutions, energy-efficient buildings, and smart home automation.
- 3.
Water management: Smart water management systems aim to reduce water waste, prevent water pollution, and ensure efficient water distribution. These systems include smart water metering, leak detection systems, and water quality monitoring solutions.
- 4.
Waste management: Smart waste management systems aim to reduce waste generation, improve waste collection efficiency, and increase recycling rates. These systems include smart bins, waste tracking systems, and automated waste collection systems.
- 5.
Public safety: Smart public safety systems aim to enhance public safety by using data-driven solutions. These systems include intelligent video surveillance, emergency response systems, and crime prediction models.
- 6.
Health: Smart health systems aim to improve health outcomes by using data-driven solutions. These systems include telemedicine solutions, health monitoring systems, and disease surveillance systems.
- 7.
Education: Smart education systems aim to improve education outcomes by using technology-driven solutions. These systems include online learning platforms, personalized learning solutions, and intelligent tutoring systems.
Key Terms in this Chapter
Transparency: The degree to which the decision-making process of an AI system is open and understandable to human users, enabling them to scrutinize and evaluate its outcomes.
Machine Learning (ML): A type of AI that enables machines to learn from data without being explicitly programmed, and improve their performance over time.
Model Explainability: The ability of an AI model to provide interpretable and transparent explanations of its decision-making process to human users, enabling them to trust and understand the model's outcomes.
Urban Mobility: The movement of people and goods within a city, including transportation modes such as walking, cycling, public transit, and private vehicles.
Accountability: The responsibility of an AI system's developers and operators to ensure that it operates in an ethical and legal manner, and that its outcomes are fair and transparent.
Smart City: A city that uses digital technologies and data-driven approaches to improve the quality of life for its residents, enhance sustainability, and optimize urban services.
Internet of Things (IoT): A network of physical devices, vehicles, buildings, and other objects embedded with sensors, software, and connectivity that enables them to collect and exchange data.
Algorithmic Bias: The systematic and unfair distribution of the benefits or harms of an algorithmic system to certain groups of people based on their race, gender, age, or other personal characteristics.
Fairness: The property of an AI system that ensures that its outcomes are not systematically biased against certain groups of people, and do not perpetuate existing social inequalities.
Explainable AI (XAI): An approach to designing AI models that can provide clear and understandable explanations of their decision-making process to human users.
Artificial Neural Networks (ANN): A computational model that mimics the structure and function of the human brain, consisting of layers of interconnected nodes that process and transmit information.
Interpretability: The ability to understand and explain how an AI system works, including its input-output relationship and decision-making process.
Deep Learning: A subfield of machine learning that uses artificial neural networks to learn patterns from large datasets.