I describe a phenomenon that has not yet been described in the epistemology literature. I label this phenomenon expression-style exclusion. Expression-style exclusion is an example of how s...

Urban parking systems are a significant contributor to traffic congestion and driver frustration, with studies showing that up to 30% of urban traffic is caused by drivers searching for parking. Traditional parking systems often lack real-time data and adaptability, leading to inefficiencies such as overfilled lots and underutilized spaces. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban parking by enabling real-time parking space detection, demand forecasting, and dynamic pricing. By integrating data from IoT sensors, traffic (...) systems, and mobile applications, cities can reduce congestion, improve parking availability, and enhance the overall urban mobility experience. Experimental results demonstrate significant improvements in parking efficiency, traffic flow, and user satisfaction, offering a sustainable blueprint for smart urban parking systems. (shrink)

Ensuring public safety is a critical challenge for rapidly growing urban areas. Traditional policing and emergency response systems often struggle to keep pace with the complexity and scale of modern cities. Artificial Intelligence (AI) offers a transformative solution by enabling real-time crime prediction, optimizing emergency resource allocation, and enhancing situational awareness through IoT-enabled systems. This paper explores how AI-driven analytics, combined with data from surveillance cameras, social media, and environmental sensors, can improve public safety in smart cities. By addressing challenges (...) such as data privacy, algorithmic bias, and system scalability, this study demonstrates the potential of AI to create safer, more resilient urban environments. Experimental results highlight improvements in crime detection rates, emergency response times, and resource utilization, paving the way for smarter public safety systems. (shrink)

Water scarcity and inefficient water management are critical challenges for rapidly growing urban areas. Traditional water distribution systems often suffer from leaks, wastage, and inequitable access, exacerbating resource shortages. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban water management by enabling real-time monitoring, predictive maintenance, and efficient resource allocation. By integrating data from smart meters, pressure sensors, and weather forecasts, cities can reduce water losses, improve distribution efficiency, and ensure equitable access. Experimental results demonstrate significant (...) improvements in leak detection, water conservation, and infrastructure reliability, offering a sustainable blueprint for urban water management in smart cities. (shrink)

Buildings account for a significant portion of global energy consumption and carbon emissions, making energy efficiency a critical focus for urban sustainability. Traditional building management systems often lack the adaptability and precision needed to optimize energy usage dynamically. This paper explores how Artificial Intelligence (AI) and IoT technologies can enhance energy efficiency in smart buildings by enabling real-time monitoring, predictive maintenance, and adaptive control systems. By integrating data from smart meters, occupancy sensors, and environmental monitors, cities can reduce energy waste, (...) lower carbon footprints, and improve occupant comfort. Experimental results demonstrate significant improvements in energy savings, operational costs, and environmental impact, offering a sustainable blueprint for smart building management. (shrink)

The growing volume of urban waste poses significant environmental and economic challenges for cities worldwide. Traditional waste management systems often rely on inefficient collection routes, inadequate recycling processes, and excessive landfill usage. This paper explores how Artificial Intelligence (AI) and IoT technologies can revolutionize waste management in smart cities by enabling real-time monitoring, automated sorting, and optimized collection routes. By integrating data from smart bins, robotic sorting systems, and predictive analytics, cities can achieve zero-waste goals and promote circular economy practices. (...) Experimental results demonstrate significant improvements in waste segregation accuracy, collection efficiency, and recycling rates, offering a sustainable blueprint for urban waste management. (shrink)

The growing demand for energy in urban environments, coupled with the urgent need to reduce carbon emissions, necessitates innovative approaches to power generation, distribution, and consumption. Artificial Intelligence (AI)-driven smart grids offer a transformative solution by optimizing energy efficiency, integrating renewable resources, and ensuring grid stability. This paper explores how machine learning and IoT-enabled predictive analytics can enhance smart grid performance in urban areas. By addressing challenges such as demand forecasting, load balancing, and renewable energy intermittency, this study demonstrates the (...) potential of AI to revolutionize sustainable energy management. Experimental results highlight improvements in grid reliability, cost reduction, and carbon footprint minimization, paving the way for resilient and eco-friendly urban energy systems. (shrink)

Urbanization poses significant challenges to healthcare systems, including overcrowded hospitals, inequitable access to care, and rising costs. Artificial Intelligence (AI) and the Internet of Things (IoT) offer transformative solutions for optimizing healthcare delivery in smart cities. This paper explores how AI-driven predictive analytics, combined with IoT-enabled wearable devices and telemedicine platforms, can enhance patient outcomes, streamline resource allocation, and reduce urban health disparities. By analyzing real-time health data and predicting disease outbreaks, this study demonstrates the potential of AI to revolutionize (...) urban healthcare systems. Experimental results highlight improvements in diagnostic accuracy, emergency response times, and operational efficiency, paving the way for equitable and sustainable healthcare in smart cities. (shrink)

Urban transportation systems face significant challenges due to increasing congestion, inefficient routes, and fluctuating passenger demand. Traditional public transportation networks often struggle to adapt dynamically to these challenges, leading to delays, overcrowding, and environmental inefficiencies. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban mobility by enabling real-time route optimization, demand forecasting, and passenger flow management. By integrating data from GPS trackers, fare collection systems, and environmental sensors, cities can reduce travel times, enhance commuter satisfaction, and (...) promote sustainable transportation. Experimental results demonstrate improvements in route efficiency, passenger load balancing, and operational costs, offering a blueprint for AI-driven smart urban mobility. (shrink)

Urban green spaces are vital for mitigating climate change, enhancing biodiversity, and improving citizen well-being. However, traditional methods of designing and managing these spaces often lack the precision and scalability needed to address modern urban challenges. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban green spaces in smart cities. By integrating satellite imagery, soil sensors, and machine learning models, cities can dynamically monitor plant health, predict ecological impacts, and design green zones that maximize biodiversity and (...) sustainability. Experimental results demonstrate improvements in air quality, heat island reduction, and community engagement, offering a blueprint for AI-driven ecological urban planning. (shrink)

The rapid urbanization of modern cities necessitates innovative approaches to data collection and integration for smarter urban management. With the Internet of Things (IoT) at the core of these advancements, the ability to efficiently gather, analyze, and utilize data becomes paramount. Generative Artificial Intelligence (AI) is revolutionizing data collection by enabling intelligent synthesis, anomaly detection, and real-time decision-making across interconnected systems. This paper explores how generative AI enhances IoT-driven data collection in smart cities, focusing on applications in transportation, energy, public (...) safety, and environmental monitoring. By addressing challenges such as data privacy, scalability, and ethical considerations, the study highlights how generative AI transforms urban governance and paves the way for sustainable and citizen-centric development. Key trends, case studies, and future research directions are discussed, showcasing the potential of generative AI as a cornerstone of smart city initiatives. (shrink)

Air pollution is a critical challenge for urban areas, contributing to public health crises and environmental degradation. Traditional air quality monitoring systems often lack the granularity and adaptability needed to address dynamic pollution sources and patterns. This paper explores how Artificial Intelligence (AI) and IoT technologies can enhance air quality management in smart cities by enabling real-time monitoring, pollution source identification, and adaptive mitigation strategies. By integrating data from IoT sensors, satellite imagery, and traffic systems, cities can reduce pollution levels, (...) improve public health outcomes, and promote sustainable urban environments. Experimental results demonstrate significant improvements in pollution detection accuracy, mitigation efficiency, and public awareness, offering a sustainable blueprint for urban air quality management. (shrink)

Urban wastewater management is a critical component of sustainable water cycles, but traditional systems often struggle with inefficiencies such as high operational costs, resource wastage, and environmental pollution. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban wastewater management by enabling real-time monitoring, predictive maintenance, and resource recovery. By integrating data from IoT sensors, water quality monitors, and treatment plants, cities can improve water quality, reduce operational costs, and recover valuable resources such as energy and nutrients. (...) Experimental results demonstrate significant improvements in treatment efficiency, resource recovery rates, and environmental impact, offering a sustainable blueprint for smart urban wastewater systems. (shrink)

Noise pollution is a growing concern in urban areas, contributing to public health issues such as stress, sleep disturbances, and hearing loss. Traditional noise monitoring systems often lack the granularity and adaptability needed to address dynamic noise sources and patterns. This paper explores how Artificial Intelligence (AI) and IoT technologies can enhance noise pollution management in smart cities by enabling real-time monitoring, source identification, and adaptive mitigation strategies. By integrating data from IoT sensors, traffic systems, and urban infrastructure, cities can (...) reduce noise levels, improve public health outcomes, and enhance the quality of life for residents. Experimental results demonstrate significant improvements in noise detection accuracy, mitigation efficiency, and public awareness, offering a sustainable blueprint for urban noise pollution management. (shrink)

Urban lighting systems are essential for safety, security, and quality of life, but they often consume significant energy and lack adaptability to changing conditions. Traditional lighting systems rely on fixed schedules and manual adjustments, leading to inefficiencies such as over-illumination and energy waste. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban lighting by enabling real-time adjustments, energy savings, and adaptive illumination based on environmental conditions and human activity. By integrating data from motion sensors, weather forecasts, (...) and traffic systems, cities can reduce energy consumption, enhance safety, and improve the quality of life for residents. Experimental results demonstrate significant improvements in energy efficiency, lighting quality, and operational costs, offering a sustainable blueprint for smart urban lighting systems. (shrink)

I present distinctness conditions that an epistemic-injustice phenomenon should meet to count as distinct from other such phenomena and I use these conditions to evaluate anticipatory-epistemic injustice’s distinctness in relation to testimonial smothering. Even though I argue that the phenomenon that Lee helpfully describes may not be distinct from testimonial smothering, I argue that the notion of distinctness itself should not be the primary or most important criterion that epistemic-injustice theorists use to determine whether such phenomena should feature in the (...) literature. This implies that anticipatory-epistemic injustice and other phenomena like it should indeed feature in the epistemic-injustice literature because they promote epistemic-injustice theorists’ development of remedies and prescriptive theses in relation to epistemic injustice. (shrink)

I submit that the critical epistemology of race and standpoint literature has not explicitly focused on the properties of information about, say, racial or gender injustice in a way similar to how epistemologists have focused on propositions and information when they describe propositional justification. I describe information in the racial-injustice-information domain in a way similar to how epistemologists describe propositional justification. To this end, I argue (C1) that if subjects in racially unjust societies tend to violate norms that promote a (...) community’s reliable information flow because racial prejudice is widely held in racially unjust societies, then racial injustice can make information flow less reliably in a community. I also argue (C2) that if racial prejudice can make information flow less reliably in a community, then information that non-dominant subjects are more likely to have will less reliably flow to community members who lack it. (shrink)

I argue that according to Kristie Dotson, non-epistemic motives such as social, ethical and material harm can motivate a speaker to smother her testimony. I present this exegesis of Dotson's view of testimonial smothering in response to J. L. Lee's and Trystan Goetze's reply to my commentary of Lee's view that anticipatory epistemic injustice is distinct from testimonial smothering.

I will argue that just and accurate assessment must involve taking into account how racial injustice affects students’ performance in their work. To this end, I will motivate what I call the RACIAL-INJUSTICE-ASSESSMENT THESIS. According to this thesis, instructors must account for how racial injustice affects a student’s work for an instructor’s judgment of her work to count as just. To motivate the RACIAL-INJUSTICE ASSESSMENT THESIS, I will defend the ACCURACY THESIS and the JUSTICE THESIS. According to the ACCURACY THESIS, (...) the accuracy of an instructor’s judgment of a student’s work will covary with the degree to which she considers how racial injustice affects the performance enshrined in the student’s the work. According to the JUSTICE THESIS, the justness of instructors’ judgments of student work covary with the degree to which instructors consider racial injustice’s effect on student work. (shrink)