Medical Expert System to Diagnose the Most Common Psychiatric Diseases

Alsagheer, Eman A; Rajab, Hamzah A; Elnajar, Khadiga M

doi:10.1145/3492547.3492593

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…The application of these systems has been extended to critical domains such as criminal justice, healthcare, automateddriving, nance, and more (Ehsan et al 2021). AI technology has great potential to provide professionals with results, building the capacity to enhance decision-making (Alsagheer et al 2021). Despite the swift recorded achievements of AI, the absence of a human-centered approach and the lack of explainability for practitioners while developing AI systems have remained obstacles to AI adoption.…”

Section: Human-centric Explanationsmentioning

confidence: 99%

Human-centric and Semantics-based Explainable Event Detection: A Survey

Kolajo

Daramola

2023

Preprint

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In recent years, there has been a surge in interest in artificial intelligent systems that can provide human-centric explanations for decisions or predictions. No matter how good and efficient a model is, users or practitioners find it difficult to trust such model if they cannot understand the model or its behaviours. Incorporating explainability that is human-centric in event detection systems is significant for building a decision-making process that is more trustworthy and sustainable. Human-centric and semantics-based explainable event detection will achieve trustworthiness, explainability, and reliability, which are currently lacking in AI systems. This paper provides a survey on the human-centric explainable AI, explainable event detection, and semantics-based explainable event detection by answering some research questions that bother on the characteristics of human-centric explanations, the state of explainable AI, methods for human-centric explanations, the essence of human-centricity in explainable event detection, research efforts in explainable event solutions, and the benefits of integrating semantics into explainable event detection. The findings from the survey show the current state of human-centric explainability, the potential of integrating semantics into explainable AI, the open problems, and the future directions which can serve as steppingstones for researchers in the explainable AI domain.

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Section: Human-centric Explanationsmentioning

confidence: 99%

Human-centric and Semantics-based Explainable Event Detection: A Survey

Kolajo

Daramola

2023

Preprint

View full text Add to dashboard Cite

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Human-centric and semantics-based explainable event detection: a survey

Kolajo

Daramola

2023

Artif Intell Rev

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In recent years, there has been a surge of interest in Artificial Intelligence (AI) systems that can provide human-centric explanations for decisions or predictions. No matter how good and efficient an AI model is, users or practitioners find it difficult to trust it if they cannot understand the AI model or its behaviours. Incorporating explainability that is human-centric in event detection systems is significant for building a decision-making process that is more trustworthy and sustainable. Human-centric and semantics-based explainable event detection will achieve trustworthiness, explainability, and reliability, which are currently lacking in AI systems. This paper provides a survey on human-centric explainable AI, explainable event detection, and semantics-based explainable event detection by answering some research questions that bother on the characteristics of human-centric explanations, the state of explainable AI, methods for human-centric explanations, the essence of human-centricity in explainable event detection, research efforts in explainable event solutions, and the benefits of integrating semantics into explainable event detection. The findings from the survey show the current state of human-centric explainability, the potential of integrating semantics into explainable AI, the open problems, and the future directions which can guide researchers in the explainable AI domain.

show abstract