Results 21 to 30 of about 4,461,134 (292)
The politics of algorithmic governance in the black box city [PDF]
Big Data & Society, 2020 Everyday surveillance work is increasingly performed by non-human algorithms. These entities can be conceptualised as machinic flâneurs that engage in distanciated flânerie: subjecting urban flows to a dispassionate, calculative and expansive gaze.
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Applied Sciences, 2023 Over the past few decades, machine learning has emerged as a valuable tool in the field of medicine, driven by the accumulation of vast amounts of medical data and the imperative to harness this data for the betterment of humanity.
Bojan Žlahtič +5 more
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Partial Retraining Substitute Model for Query-Limited Black-Box Attacks
Applied Sciences, 2020 Black-box attacks against deep neural network (DNN) classifiers are receiving increasing attention because they represent a more practical approach in the real world than white box attacks.
Hosung Park, Gwonsang Ryu, Daeseon Choi
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A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging
Applied Sciences, 2021 Deep learning has demonstrated remarkable accuracy analyzing images for cancer detection tasks in recent years. The accuracy that has been achieved rivals radiologists and is suitable for implementation as a clinical tool.
Mehmet A. Gulum +2 more
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Black-Box Ripper: Copying black-box models using generative evolutionary algorithms
CoRR, 2020 We study the task of replicating the functionality of black-box neural models, for which we only know the output class probabilities provided for a set of input images. We assume back-propagation through the black-box model is not possible and its training images are not available, e.g. the model could be exposed only through an API.
Antonio Barbalau +3 more
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Users’ trust in black-box machine learning algorithms [PDF]
REGE Revista de GestãoPurpose – This paper aims to investigate whether professional data analysts’ trust of black-box systems is increased by explainability artifacts. Design/methodology/approach – The study was developed in two phases.
Heitor Hoffman Nakashima +2 more
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THE EVIDENTIARY IMPLICATIONS OF INTERPRETING BLACK-BOX ALGORITHMS [PDF]
, 2023 Biased black-box algorithms have drawn increasing levels of scrutiny from the public. This is especially true for those black-box algorithms with the potential to negatively affect protected or vulnerable populations.1 One type of these black-box ...
Bhatnagar, Varun
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Ethical issues of implementing artificial intelligence in medicine
Digital Diagnostics, 2023 Artificial intelligence (AI) systems are highly efficient. However, their implementation in medical practice is accompanied by a range of ethical issues. The black box problem is basic to the AI philosophy, although having its own specificity in relation
Maxim I. Konkov
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IEEE Access, 2020 Estimation of black-box functions often requires evaluating an extensive number of expensive noisy points. Learning algorithms can actively compare the similarity between the evaluated and unevaluated points to determine the most informative subsequent ...
Rajitha Meka +3 more
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Big data and black-box medical algorithms [PDF]
Science Translational Medicine, 2018 New machine-learning techniques entering medicine present challenges in validation, regulation, and integration into practice.
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