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Context-Driven Data Mining through Bias Removal and Data Incompleteness Mitigation [article]

Feras A. Batarseh, Ajay Kulkarni
2019 arXiv   pre-print
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

Feras Batarseh, Munisamy Gopinath, Ruixin Yang
2020 Zenodo  
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

Feras Batarseh, Munisamy Gopinath, Ruixin Yang
2020 Zenodo  
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]

Sina Mohseni and Eric Ragan and Xia Hu
2019 arXiv   pre-print
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]

Javier Sanchez-Monedero, Lina Dencik, Lilian Edwards
2020 arXiv   pre-print
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

Reihaneh H. Hariri, Erik M. Fredericks, Kate M. Bowers
2019 Journal of Big Data  
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

Kalia Orphanou, Jahna Otterbacher, Styliani Kleanthous, Khuyagbaatar Batsuren, Fausto Giunchiglia, Veronika Bogina, Avital Shulner-Tal, Alan Hartman, Tsvi Kuflik
2021 Zenodo  
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]

Kalia Orphanou, Jahna Otterbacher, Styliani Kleanthous, Khuyagbaatar Batsuren, Fausto Giunchiglia, Veronika Bogina, Avital Shulner Tal, AlanHartman, Tsvi Kuflik
2022 arXiv   pre-print
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]

Andrew Pery, Majid Rafiei, Michael Simon, Wil M.P. van der Aalst
2021 arXiv   pre-print
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

Miguel Luengo-Oroz, Joseph Bullock, Katherine Hoffmann Pham, Cynthia Sin Nga Lam, Alexandra Luccioni
2021 IEEE technology & society magazine  
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]

Amandalynne Paullada, Inioluwa Deborah Raji, Emily M. Bender, Emily Denton, Alex Hanna
2020 arXiv   pre-print
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]

Mariam Guizani
2022 arXiv   pre-print
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

Evangelos Ntentos, Uwe Zdun, Konstantinos Plakidas, Daniel Schall, Fei Li, Sebastian Meixner
2019 Zenodo  
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

Evangelos Ntentos, Uwe Zdun, Konstantinos Plakidas, Daniel Schall, Fei Li, Sebastian Meixner
2019 Zenodo  
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

Peter J. van de Waerdt
2020 Computer Law and Security Review  
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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