Results 11 to 20 of about 295,552 (260)
Abstracting Fairness: Oracles, Metrics, and Interpretability [PDF]
, 2020 It is well understood that classification algorithms, for example, for deciding on loan applications, cannot be evaluated for fairness without taking context into account.
Dwork, Cynthia +3 more
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Analyzing Employee Attrition Using Explainable AI for Strategic HR Decision-Making
Mathematics, 2023 Employee attrition and high turnover have become critical challenges faced by various sectors in today’s competitive job market. In response to these pressing issues, organizations are increasingly turning to artificial intelligence (AI) to predict ...
Gabriel Marín Díaz +2 more
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Entropy, 2021 The adaptation of deep learning models within safety-critical systems cannot rely only on good prediction performance but needs to provide interpretable and robust explanations for their decisions.
Domjan Barić +3 more
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Prediction or interpretability?
Emerging Themes in Epidemiology, 2019 The journal published a review of the literature on recursive partition in epidemiological research comparing two decision tree methods: classification and regression trees (CARTs) and conditional inference trees (CITs).
Stefano Nembrini
doaj +1 more source
Re-interpreting rules interpretability
International Journal of Data Science and Analytics, 2022 Abstract Trustworthy machine learning requires a high level of interpretability of machine learning models, yet many models are inherently black-boxes. Training interpretable models instead—or using them to mimic the black-box model—seems like a viable solution. In practice, however, these interpretable models are still unintelligible
Adilova, L. +3 more
openaire +2 more sources
MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis [PDF]
, 2018 Interpretability has emerged as a crucial aspect of machine learning, aimed at providing insights into the working of complex neural networks. However, existing solutions vary vastly based on the nature of the interpretability task, with each use case ...
Anirudh, Rushil +3 more
core +2 more sources
Patterns, 2021 Summary: Free-text clinical notes in electronic health records are more difficult for data mining while the structured diagnostic codes can be missing or erroneous. To improve the quality of diagnostic codes, this work extracts diagnostic codes from free-
Xianghao Zhan +3 more
doaj +1 more source
Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond
Knowledge and Information Systems, 2022 Deep neural networks have been well-known for their superb handling of various machine learning and artificial intelligence tasks. However, due to their over-parameterized black-box nature, it is often difficult to understand the prediction results of deep models.
Li, Xuhong +7 more
openaire +2 more sources
A New Interpretable Unsupervised Anomaly Detection Method Based on Residual Explanation
IEEE Access, 2022 Despite the superior performance in modeling complex patterns to address challenging problems, the black-box nature of Deep Learning (DL) methods impose limitations to their application in real-world critical domains.
David F. N. Oliveira +8 more
doaj +1 more source
Double Prior Network for Multidegradation Remote Sensing Image Super-Resolution
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Image super-resolution (SR) is widely used in remote sensing because it can effectively increase image details. Neural networks have shown remarkable performance in recent years, benefitting from their end-to-end training.
Mengyang Shi +3 more
doaj +1 more source