Searched for: subject%3A%22Bias%255C+mitigation%22
(1 - 13 of 13)
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Li, Roger Zhe (author)
doctoral thesis 2023
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Li, Zirui (author)
End-to-end Automatic Speech Recognition (ASR) systems improved drastically in recent years and they work extremely well on many large datasets. However, research shows that these models failed to capture the variability in speech production and have biases against the variant caused by the regional accented speech. Moreover, ASR research on...
master thesis 2023
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Witting, Emiel (author)
Domain adaptation allows machine learning models to perform well in a domain that is different from the available train data. This non-trivial task is approached in many ways and often relies on assumptions about the source (train) and target (test) domains. Unsupervised domain adaptation uses unlabeled target data to mitigate a shift or bias...
bachelor thesis 2023
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Zhang, Y. (author), Herygers, Aaricia (author), Patel, T.B. (author), Yue, Z. (author), Scharenborg, O.E. (author)
Automatic speech recognition (ASR) should serve every speaker, not only the majority “standard” speakers of a language. In order to build inclusive ASR, mitigating the bias against speaker groups who speak in a “non-standard” or “diverse” way is crucial. We aim to mitigate the bias against non-native-accented Flemish in a Flemish ASR system....
conference paper 2023
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Rieger, A. (author), Bredius, F. (author), Tintarev, N. (author), Pera, M.S. (author)
We often use search engines when seeking information for opinion-forming and decision-making on debated topics. However, searching for resources on debated topics to gain well-rounded knowledge is cognitively demanding, leaving us vulnerable to cognitive biases, such as confirmation bias. This can impede well-informed decision-making, and on...
conference paper 2023
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Li, Roger Zhe (author), Urbano, Julián (author), Hanjalic, A. (author)
Mainstream bias, where some users receive poor recommendations because their preferences are uncommon or simply because they are less active, is an important aspect to consider regarding fairness in recommender systems. Existing methods to mitigate mainstream bias do not explicitly model the importance of these non-mainstream users or, when...
conference paper 2023
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Zhang, Yuanyuan (author)
Automatic Speech Recognition (ASR) systems have seen substantial improvements in the past decade; however, not for all speaker groups. Recent research shows that bias exists against different types of speech, including non-native accents, in state-of-the-art (SOTA) ASR systems. To attain inclusive speech recognition, i.e., ASR for everyone...
master thesis 2022
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Zhang, Y. (author), Zhang, Yixuan (author), Halpern, B.M. (author), Patel, T.B. (author), Scharenborg, O.E. (author)
Automatic speech recognition (ASR) systems have seen substantial improvements in the past decade; however, not for all speaker groups. Recent research shows that bias exists against different types of speech, including non-native accents, in state-of-the-art (SOTA) ASR systems. To attain inclusive speech recognition, i.e., ASR for everyone...
journal article 2022
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Balayn, A.M.A. (author), Lofi, C. (author), Houben, G.J.P.M. (author)
The increasing use of data-driven decision support systems in industry and governments is accompanied by the discovery of a plethora of bias and unfairness issues in the outputs of these systems. Multiple computer science communities, and especially machine learning, have started to tackle this problem, often developing algorithmic solutions to...
journal article 2021
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van Stijn, Jip J. (author), Neerincx, M.A. (author), ten Teije, Annette (author), Vethman, Steven (author)
Increasing automation in the healthcare sector calls for a Hybrid Intelligence (HI) approach to closely study and design the collaboration of humans and autonomous machines. Ensuring that medical HI systems' decision-making is ethical is key. The use of Team Design Patterns (TDPs) can advance this goal by describing successful and reusable...
journal article 2021
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Rieger, A. (author), Draws, T.A. (author), Theune, Mariët (author), Tintarev, N. (author)
During online information search, users tend to select search results that confirm previous beliefs and ignore competing possibilities. This systematic pattern in human behavior is known as confirmation bias. In this paper, we study the effect of obfuscation (i.e., hiding the result unless the user clicks on it) with warning labels and the...
conference paper 2021
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Khaleghi, Aaron (author)
The consumer lending domain has increasingly leveraged Artificial Intelligence (AI) to make loan approval processes more efficient and to make use of larger amount of information to predict their applicants’ repayment ability. Over time, however, valid concerns have been raised about whether decisions made about individuals using these data...
master thesis 2020
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Dimitropoulos, George (author)
Machine Learning models are increasingly used to assist or replace humans in a variety of decision-making domains. However, a lot of concerns have been raised about the impact of these decisions on people’s lives. In this work we focus on two main problems. The first one is that there might be discrimination between different groups of people...
master thesis 2019
Searched for: subject%3A%22Bias%255C+mitigation%22
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