GPSRL: Learning Semi-Parametric Bayesian Survival Rule Lists from Heterogeneous Patient Data

Shakur, Ameer Hamza; Qian, Xiaoning; Wang, Zhangyang; Mortazavi, Bobak J.; Huang, Shuai

doi:10.1109/icpr48806.2021.9413157

Cited by 3 publications

(2 citation statements)

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“…According to some XAI researchers, the understandability of models for reliability is essential. Meanwhile, some believe that explainability is not required in reality for every purpose because human reasoning is considered a black box [168]. For example, in the daily life activities of smart cities, someone would be more interested in why a loan was approved than a doctor who prefers a specific medical treatment.…”

Section: Open Issues and Research Challengesmentioning

confidence: 99%

“…Before training models to address the smart cities problem, one should address what is being sought and whether there is a real need for a black-box model. According to [168], it is a problem that the field of XAI is exploding with research into prediction models for precise decisions. They argued that the accuracy of black-box models is worth considering in building interpretable models.…”

Section: Black-box Versus Interpretable Models-reconsideration Of The...mentioning

confidence: 99%

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A Survey of Explainable Artificial Intelligence for Smart Cities

et al. 2023

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The emergence of Explainable Artificial Intelligence (XAI) has enhanced the lives of humans and envisioned the concept of smart cities using informed actions, enhanced user interpretations and explanations, and firm decision-making processes. The XAI systems can unbox the potential of black-box AI models and describe them explicitly. The study comprehensively surveys the current and future developments in XAI technologies for smart cities. It also highlights the societal, industrial, and technological trends that initiate the drive towards XAI for smart cities. It presents the key to enabling XAI technologies for smart cities in detail. The paper also discusses the concept of XAI for smart cities, various XAI technology use cases, challenges, applications, possible alternative solutions, and current and future research enhancements. Research projects and activities, including standardization efforts toward developing XAI for smart cities, are outlined in detail. The lessons learned from state-of-the-art research are summarized, and various technical challenges are discussed to shed new light on future research possibilities. The presented study on XAI for smart cities is a first-of-its-kind, rigorous, and detailed study to assist future researchers in implementing XAI-driven systems, architectures, and applications for smart cities.

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Section: Open Issues and Research Challengesmentioning

confidence: 99%

Section: Black-box Versus Interpretable Models-reconsideration Of The...mentioning

confidence: 99%

A Survey of Explainable Artificial Intelligence for Smart Cities

et al. 2023

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A rule-based exploratory analysis for discovery of multimodal biomarkers of ADHD using eye movement and EEG data

Shakur

Sun

Kim

et al. 2022

IISE Transactions on Healthcare Systems Engineering

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Modern Smart Cities and Open Research Challenges and Issues of Explainable Artificial Intelligence

Sindiramutty,

Tan,

Tee

et al. 2024

Advances in Computational Intelligence and Robotics

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This chapter's purpose is to review the modern smart cities and open research challenges and issues of explainable artificial intelligence (XAI). With the advent of XAI, people's lives have been improved, and the idea of smart urban has been created. Although the anticipated advantages, the adoption of AI differs between smart cities in part because of challenges and issues that can prevent the adoption of AI in smart cities. This chapter will explore the importance of XAI in smart cities and what is the current state of the art in various XAI applications in smart cities, the open challenges of XAI in smart cities, the research issue of XAI in smart cities, case studies and examples, and evaluations and analysis XAI models in smart city application. The open challenges will be covering developing and Novel XAI with ontologies, assurance in ML algorithms, scalability of XAI models in smart cities, etc.

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GPSRL: Learning Semi-Parametric Bayesian Survival Rule Lists from Heterogeneous Patient Data

Cited by 3 publications

References 20 publications

A Survey of Explainable Artificial Intelligence for Smart Cities

A Survey of Explainable Artificial Intelligence for Smart Cities

A rule-based exploratory analysis for discovery of multimodal biomarkers of ADHD using eye movement and EEG data

Modern Smart Cities and Open Research Challenges and Issues of Explainable Artificial Intelligence

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