Results 11 to 20 of about 4,461,134 (292)
Black box algorithms in mental health apps: an ethical reflection [PDF]
Bioethics, 2023 Mental health apps bring unprecedented benefits and risks, raising questions of whether the kind of algorithms they deploy and the functions they perform are relevant to assess them as tools for mental health. We focus on mental health apps based on black box algorithms, explore their forms of opacity, discuss the implications derived from their ...
Manriquez Roa, Tania +1 more
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Unbiased black box search algorithms [PDF]
Proceedings of the 13th annual conference on Genetic and evolutionary computation, 2011 We formalize the concept of an unbiased black box algorithm, which generalises the idea previously introduced by Lehre and Witt. Our formalization of bias relates to the symmetry group of the problem class under consideration, establishing a connection with previous work on No Free Lunch. Our definition is motivated and justified by a series of results,
Jonathan E. Rowe, Michael D. Vose
openaire +2 more sources
Be Wary of Black-Box Trading Algorithms [PDF]
The Journal of Investing, 2019 Black-box algorithms now account for nearly a third of all U. S. stock trades. It is a mistake to think that these algorithms possess superhuman intelligence. In reality, computers do not have the common sense and wisdom that humans have accumulated by living.
Smith, Gary N
core +4 more sources
Faster black-box algorithms through higher arity operators [PDF]
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms, 2011 To appear at FOGA ...
Benjamin Doerr +5 more
openaire +5 more sources
We might be afraid of black-box algorithms [PDF]
, 2021 Fears of black-box algorithms are multiplying. Black-box algorithms are said to prevent accountability, make it harder to detect bias and so on. Some fears concern the epistemology of black-box algorithms in medicine and the ethical implications of that ...
Veliz, Carissa +3 more
core +4 more sources
Black-Box Test Generation from Inferred Models [PDF]
, 2015 Automatically generating test inputs for components without source code (are ‘black-box’) and specification is challenging. One particularly interesting solution to this problem is to use Machine Learning algorithms to infer testable models from program ...
Petros Papadopoulos (1911) +1 more
core +7 more sources
Black-box tests for algorithmic stability
Information and Inference: A Journal of the IMA, 2023 Abstract Algorithmic stability is a concept from learning theory that expresses the degree to which changes to the input data (e.g. removal of a single data point) may affect the outputs of a regression algorithm. Knowing an algorithm’s stability properties is often useful for many downstream applications—for example, stability is known ...
Byol Kim, Rina Foygel Barber
openaire +3 more sources
Proceedings of the 23rd ACM Conference on Economics and Computation, 2022 What should regulators of complex algorithms regulate? We propose a model of oversight over 'black-box' algorithms used in high-stakes applications such as lending, medical testing, or hiring. In our model, a regulator is limited in how much she can learn about a black-box model deployed by an agent with misaligned preferences.
Laura Blattner +2 more
openaire +4 more sources
Using Black-Box Compression Algorithms for Phase Retrieval [PDF]
IEEE Transactions on Information Theory, 2020 Compressive phase retrieval refers to the problem of recovering a structured $n$-dimensional complex-valued vector from its phase-less under-determined linear measurements. The non-linearity of measurements makes designing theoretically-analyzable efficient phase retrieval algorithms challenging.
Milad Bakhshizadeh +2 more
openaire +2 more sources
Better fixed-arity unbiased black-box algorithms [PDF]
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018 An extended abstract will appear at GECCO ...
Nina Bulanova, Maxim Buzdalov 0001
openaire +3 more sources