Results 11 to 20 of about 145,182 (282)
Bias In, Bias Out? Evaluating the Folk Wisdom [PDF]
, 2020 We evaluate the folk wisdom that algorithmic decision rules trained on data produced by biased human decision-makers necessarily reflect this bias.
Rambachan, Ashesh, Roth, Jonathan
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Algorithmic Fairness and Bias in Machine Learning Systems [PDF]
E3S Web of Conferences, 2023 In recent years, research into and concern over algorithmic fairness and bias in machine learning systems has grown significantly. It is vital to make sure that these systems are fair, impartial, and do not support discrimination or social injustices ...
Chandra Rushil +5 more
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International Journal of Communication, 2021 Algorithms are increasingly used to offer jobs, loans, medical care, and other services, as well as to influence behavior. Decisions that create the algorithms, the data sets that feed them, and the outputs that result from algorithmic decision making ...
Miren Gutierrez
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International Journal of Communication, 2023 Despite a substantial body of scholarship at the intersection of artificial intelligence (AI) and journalism, it remains relatively unexplored as to how AI-generated news is different from news produced by professional journalists in terms of news bias ...
Seungahn Nah +5 more
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Evolution and impact of bias in human and machine learning algorithm interaction.
PLoS ONE, 2020 Traditionally, machine learning algorithms relied on reliable labels from experts to build predictions. More recently however, algorithms have been receiving data from the general population in the form of labeling, annotations, etc.
Wenlong Sun +2 more
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International Journal of Artificial Intelligence in Education, 2021 In this paper, we review algorithmic bias in education, discussing the causes of that bias and reviewing the empirical literature on the specific ways that algorithmic bias is known to have manifested in education. While other recent work has reviewed mathematical definitions of fairness and expanded algorithmic approaches to reducing bias, our review ...
Ryan S. Baker, Aaron Hawn
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Bias does not equal bias: a socio-technical typology of bias in data-based algorithmic systems
Internet Policy Review, 2021 This paper introduces a socio-technical typology of bias in data-driven machine learning and artificial intelligence systems. The typology is linked to the conceptualisations of legal anti-discrimination regulations, so that the concept of structural ...
Paola Lopez
doaj +1 more source
International Journal of Communication, 2023 As artificial intelligence (AI) becomes more seamlessly integrated into our social life, the unfair outcomes and ethical issues associated with AI and its subtechnologies have been widely discussed in scholarly work across disciplines in recent years ...
Seungahn Nah, Jun Luo, Jungseock Joo
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No Free Lunch versus Occam's Razor in Supervised Learning [PDF]
, 2011 The No Free Lunch theorems are often used to argue that domain specific knowledge is required to design successful algorithms. We use algorithmic information theory to argue the case for a universal bias allowing an algorithm to succeed in all ...
Hutter, Marcus, Lattimore, Tor
core +1 more source
Experimental Heat-Bath Cooling of Spins [PDF]
, 2014 Algorithmic cooling (AC) is a method to purify quantum systems, such as ensembles of nuclear spins, or cold atoms in an optical lattice. When applied to spins, AC produces ensembles of highly polarized spins, which enhance the signal strength in nuclear ...
Brassard, Gilles +7 more
core +2 more sources