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Context-Driven Data Mining through Bias Removal and Data Incompleteness Mitigation
[article]
2019
arXiv
pre-print
Preserved Fulltext
The results of data mining endeavors are majorly driven by data quality. ...
Throughout these deployments, serious show-stopper problems are still unresolved, such as: data collection ambiguities, data imbalance, hidden biases in data, the lack of domain information, and data incompleteness ...
, and age), weighted modeling, and mitigation of bias. ...
arXiv:1910.08670v1
fatcat:3rzimjokirfjhpswed3gjqrt6a
Data-Driven Management of Outlier Events and their Effects on Agricultural Economics and Policy
2020
Zenodo
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Data-Driven Management of Outlier Events and their Effects on Agricultural Economics and Policy ...
However, the results of data mining endeavors are majorly driven by data quality. ...
In a traditional data science lifecycle, outliers, bias, variance, ensemble machine learning, boosting, over and under sampling, and data wrangling are measures to create context and mitigate output quality ...
doi:10.5281/zenodo.4031701
fatcat:j5wwlo5n4ba6ljiqgkc3tssgdq
Data-Driven Management of Outlier Events and their Effects on Agricultural Economics and Policy
2020
Zenodo
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Data-Driven Management of Outlier Events and their Effects on Agricultural Economics and Policy ...
However, the results of data mining endeavors are majorly driven by data quality. ...
In a traditional data science lifecycle, outliers, bias, variance, ensemble machine learning, boosting, over and under sampling, and data wrangling are measures to create context and mitigate output quality ...
doi:10.5281/zenodo.4031700
fatcat:yx3aon2duzbhbochkdweswq5fi
Open Issues in Combating Fake News: Interpretability as an Opportunity
[article]
2019
arXiv
pre-print
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Although rumor detection and various linguistic analysis techniques are common methods to detect false content in social media, there are other feasible mitigation approaches that could be explored in ...
Lastly, we present research opportunities from interpretable machine learning to mitigate fake news problems with 1) interpretable fake news detection and 2) transparent news feed algorithms. ...
Machine learning methods also analyze falsified context (not limited to news or social media) using various types of data mining and machine learning techniques. ...
arXiv:1904.03016v1
fatcat:lvokaamquvfcxid4p4d6spgqwi
What does it mean to solve the problem of discrimination in hiring? Social, technical and legal perspectives from the UK on automated hiring systems
[article]
2020
arXiv
pre-print
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and attempt to mitigate bias and discrimination. ...
Yet the way decisions are made on who is eligible for jobs, and why, are rapidly changing with the advent and growth in uptake of automated hiring systems (AHSs) powered by data-driven tools. ...
ACKNOWLEDGMENTS The research of Lina Dencik and Javier Sánchez-Monedero has been funded by the ERC Starting Grant DATAJUSTICE (grant no. 759903). ...
arXiv:1910.06144v2
fatcat:uv5bnixnyjdmvhap3sdstcnxsi
Uncertainty in big data analytics: survey, opportunities, and challenges
2019
Journal of Big Data
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Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of Oakland University or other research sponsors. ...
This research has been supported in part by NSF Grant CNS-1657061, the Michigan Space Grant Consortium, the Comcast Innovation Fund, and Oakland University. ...
For example, if training data is biased in any way, incomplete, or obtained through inaccurate sampling, the learning algorithm using corrupted training data will likely output inaccurate results. ...
doi:10.1186/s40537-019-0206-3
fatcat:kp5vl4suwvcu5c6iukwa2o33le
Mitigating Bias in Algorithmic Systems - A Fish-Eye View
2021
Zenodo
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Mitigating bias in algorithmic systems is a critical issue drawing attention across communities within the information and computer sciences. ...
as from the perspective of stakeholders in the broader context. ...
To handle this issue, some approaches remove information about the protected variables from the training data but they also transform the training data using data mining methods. ...
doi:10.5281/zenodo.6240582
fatcat:vftoi4woebhrrp5tlmkclabgf4
Mitigating Bias in Algorithmic Systems – A Fish-Eye View
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2103.16953v1 fatcat:4mqsx2lwbjclvez6uqj2fz3tya
Mitigating bias in algorithmic systems is a critical issue drawing attention across communities within the information and computer sciences. ...
well as from the perspective of stakeholders in the broader context. ...
Acknowledgement This project is partially funded by the European Union's Horizon 2020 research and innovation programme under grant agreement No. 810105 (CyCAT). ...
arXiv:2103.16953v2
fatcat:b27zb3zusnfmzcspyl2njbivkq
Trustworthy Artificial Intelligence and Process Mining: Challenges and Opportunities
[article]
2021
arXiv
pre-print
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In this paper, we demonstrate that process mining can provide a useful framework for gaining fact-based visibility to AI compliance process execution, surfacing compliance bottlenecks, and providing for ...
Moreover, there are complexities associated with meeting the specific dimensions of Trustworthy AI best practices such as data governance, conformance testing, quality assurance of AI model behaviors, ...
Acknowledgments Funded under the Excellence Strategy of the Federal Government and the Länder. We also thank the Alexander von Humboldt Stiftung for supporting our research. ...
arXiv:2110.02707v1
fatcat:bttiuuexqzcjriqrmefp52rywe
From Artificial Intelligence Bias to Inequality in the Time of COVID-19
2021
IEEE technology & society magazine
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Acknowledgments The United Nations Global Pulse is supported in part by the Governments of Sweden and Germany and in part by the William and Flora Hewlett Foundation. ...
The work of Joseph Bullock was supported by the United Kingdom Research and Innovation -Science and Technology Facilities Council (UKRI-STFC) under Grant ST/P006744/1. ...
In all of these examples, AI-driven biases and their implications for inequality should be assessed from the initial project development stages through the presentation and use of outputs. ...
doi:10.1109/mts.2021.3056282
fatcat:zl6c27fvqfazbonlfeowfnyjle
Data and its (dis)contents: A survey of dataset development and use in machine learning research
[article]
2020
arXiv
pre-print
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In this paper, we survey the many concerns raised about the way we collect and use data in machine learning and advocate that a more cautious and thorough understanding of data is necessary to address ...
They form the basis for the models we design and deploy, as well as our primary medium for benchmarking and evaluation. ...
Instances of data reuse in benchmarks are often seen in the scraping and mining context, especially when it comes to Flickr, Wikipedia, and other openly licensed data instances. ...
arXiv:2012.05345v1
fatcat:alaedbafjbgezisb27taavkmtq
A Decade of Information Architecture in HCI: A Systematic Literature Review
[article]
2022
arXiv
pre-print
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in the context of Human Computer Interaction (IAinHCI). ...
We found 25 papers that utilized Information Architecture in the context of Human Computer Interaction. ...
The four most frequently encountered threats to validity are: • Bias in study selection. • Bias in data extraction. • Inappropriate or incomplete search terms in automatic search. • Non comprehensive venues ...
arXiv:2202.13412v1
fatcat:s7yeukucvjcethqgvz5xvhpd5q
Supporting Architectural Decision Making on Data Management in Microservice Architectures
2019
Zenodo
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Uploaded here in compliance with the requirements of the Austrian Research Promotion Agency (FFG) and Austrian Science Fund (FWF). ...
Our rough evaluation underlines that the knowledge in microservice data management is complex and scattered, and existing knowledge sources are inconsistent and incomplete, even if they attempt to systematically ...
like informal pattern mining. ...
doi:10.5281/zenodo.3244004
fatcat:htbrw26n4zhhfjlydwrt2dyrva
Supporting Architectural Decision Making on Data Management in Microservice Architectures
2019
Zenodo
Preserved Fulltext
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The file type is application/pdf.
Uploaded here in compliance with the requirements of the Austrian Research Promotion Agency (FFG) and Austrian Science Fund (FWF). ...
Our rough evaluation underlines that the knowledge in microservice data management is complex and scattered, and existing knowledge sources are inconsistent and incomplete, even if they attempt to systematically ...
like informal pattern mining. ...
doi:10.5281/zenodo.3484435
fatcat:6i3giz3ur5aafmdkkdrg5o5pfq
Information asymmetries: recognizing the limits of the GDPR on the data-driven market
2020
Computer Law and Security Review
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When providing such services significant information asymmetries arise: data-driven companies collect much more personal data than the consumer knows or can reasonably oversee, and data-driven companies ...
Online search engines, social media platforms, and targeted advertising services often employ a "data-driven" business model based on the large-scale collection, analysis, and monetization of personal ...
the GDPR succeeds in mitigating the information asymmetries on the data-driven market. ...
doi:10.1016/j.clsr.2020.105436
fatcat:z4ek66qgd5d25jrmkalfw7g72a
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