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Fair and accurate age prediction using distribution aware data curation and augmentation

Yushi Cao, David Berend, Palina Tolmach, Guy Amit, Moshe Levy, Yang Liu, Asaf Shabtai, Yuval Elovici
2022 Zenodo  
In this work, we present two novel approaches for dataset curation and data augmentation in order to increase fairness through balanced feature curation and increase diversity through distribution aware  ...  Age prediction is an especially difficult application with the issue of fairness remaining an open research problem (e.g. predicting age for different ethnicity equally accurate).  ...  This research is partially supported by the European Union's Horizon 2020 research and innovation programme (Award No. 830927) and the National Research Foundation (Award No.  ... 
doi:10.5281/zenodo.5833886 fatcat:4tbsjvbkhzajlkgt6denfkhwde

Fair and accurate age prediction using distribution aware data curation and augmentation [article]

Yushi Cao, David Berend, Palina Tolmach, Guy Amit, Moshe Levy, Yang Liu, Asaf Shabtai, Yuval Elovici
2021 arXiv   pre-print
In this work, we present two novel approaches for dataset curation and data augmentation in order to increase fairness through balanced feature curation and increase diversity through distribution aware  ...  Age prediction is an especially difficult application with the issue of fairness remaining an open research problem (e.g., predicting age for different ethnicity equally accurate).  ...  Acknowledgments We thank Professor Lei Ma from University of Alberta, Canada, Prof Zhang Tianwei and Dr. Xiaofei Xie from Nanyang Technological University, Singapore, for the helpful discussions.  ... 
arXiv:2009.05283v6 fatcat:6m63tatuobfppm3rdzrud5geoa

Data-Centric Factors in Algorithmic Fairness

Nianyun Li, Naman Goel, Elliott Ash
2022 Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society  
The methodology and the results in the paper provide a useful reference point for a data-centric approach to studying algorithmic fairness in recidivism prediction and beyond.  ...  Notwithstanding the widely held view that data generation and data curation processes are prominent sources of bias in machine learning algorithms, there is little empirical research seeking to document  ...  Słowik and Bottou [64] study the relation between distributionally robust optimization (DRO) and data curation.  ... 
doi:10.1145/3514094.3534147 fatcat:fkg6bf5w7jb4xfkaybx3zyk6ii

Characterizing machine learning process: A maturity framework [article]

Rama Akkiraju, Vibha Sinha, Anbang Xu, Jalal Mahmud, Pritam Gundecha, Zhe Liu, Xiaotong Liu, John Schumacher
2018 arXiv   pre-print
for data scientists, and how to continuously improve AI applications in term of accuracy and fairness, and how to customize general purpose machine learning models with industry, domain, and use case  ...  specific data to make them more accurate for specific situations etc.  ...  Sudhir Koka, Michael Picheny, Ruchir Puri, Beth Smith, Samuel Thomas, Olivia Buzek, Pawan Lakshmanan, Laura Chiticariu, Padma Malladi, Majid Irani, Yuankun Song, Yingbei Tong, Gary Diamanti, Inkit Padhi and  ... 
arXiv:1811.04871v1 fatcat:oebjocpc3bgtdfpdmrqxlk3k2y

Algorithmic Fairness Datasets: the Story so Far [article]

Alessandro Fabris, Stefano Messina, Gianmaria Silvello, Gian Antonio Susto
2022 arXiv   pre-print
Progress in fair Machine Learning hinges on data, which can be appropriately used only if adequately documented.  ...  Finally, we analyze these datasets from the perspective of five important data curation topics: anonymization, consent, inclusivity, sensitive attributes, and transparency.  ...  Acknowledgements The authors would like to thank the following researchers and dataset creators for the useful feedback on the data briefs: Alain Barrat, Luc Behaghel, Asia Biega, Marko Bohanec, Chris  ... 
arXiv:2202.01711v3 fatcat:kd546yklwjhvtkrbhtzgbzb2xm

Fairness and Bias in Robot Learning [article]

Laura Londoño, Juana Valeria Hurtado, Nora Hertz, Philipp Kellmeyer, Silja Voeneky, Abhinav Valada
2022 arXiv   pre-print
Using examples from different robot learning domains, we examine scenarios of unfair outcomes and strategies to mitigate them.  ...  We present early advances in the field by covering different fairness definitions, ethical and legal considerations, and methods for fair robot learning.  ...  Funding Law of the Ministry of Science, Research and Arts of the State of Baden-Wurttemberg).  ... 
arXiv:2207.03444v1 fatcat:qaezec6sujbtzceionczbqjify

FairyTED: A Fair Rating Predictor for TED Talk Data

Rupam Acharyya, Shouman Das, Ankani Chattoraj, Md. Iftekhar Tanveer
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Experimental results show that while prediction accuracy is comparable to recent work on this dataset, our predictions are counterfactually fair with respect to a novel metric when compared to true data  ...  This causal model contributes in generating counterfactual data to train a fair predictive model. Our framework is general enough to utilize any assumption within the causal model.  ...  the data and our setup can take care of it by inferring a posterior distribution over the unobserved attributes. 5) Any dataset with a simple embedding scheme can use this model.  ... 
doi:10.1609/aaai.v34i01.5368 fatcat:icthupcxe5fubkgyajolm3tauy

A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions [article]

Richard Osuala, Kaisar Kushibar, Lidia Garrucho, Akis Linardos, Zuzanna Szafranowska, Stefan Klein, Ben Glocker, Oliver Diaz, Karim Lekadir
2021 arXiv   pre-print
In this review, we assess the potential of GANs to address a number of key challenges of cancer imaging, including data scarcity and imbalance, domain and dataset shifts, data access and privacy, data  ...  Despite technological and medical advances, the detection, interpretation, and treatment of cancer based on imaging data continue to pose significant challenges.  ...  Acknowledgments This project has received funding from the European Union's Horizon 2020 research and innovation pro-  ... 
arXiv:2107.09543v1 fatcat:jz76zqklpvh67gmwnsdqzgq5he

Astraea: Grammar-based Fairness Testing [article]

Ezekiel Soremekun and Sakshi Udeshi and Sudipta Chattopadhyay
2022 arXiv   pre-print
Using probabilistic grammars, ASTRAEA also provides fault diagnosis by isolating the cause of observed software bias. ASTRAEA's diagnoses facilitate the improvement of ML fairness.  ...  Fairness testing is challenging; developers are tasked with generating discriminatory inputs that reveal and explain biases.  ...  This work is also partially supported by OneConnect Financial grant number RGOCFT2001, Singapore Ministry of Education (MOE), President's Graduate Fellowship and MOE grant number MOE2018-T2-1-098.  ... 
arXiv:2010.02542v5 fatcat:n6ka7pbchrdczpnsgcjpomybfm

FairyTED: A Fair Rating Predictor for TED Talk Data [article]

Rupam Acharyya, Shouman Das, Ankani Chattoraj, Md. Iftekhar Tanveer
2019 arXiv   pre-print
Experimental results show that while prediction accuracy is comparable to recent work on this dataset, our predictions are counterfactually fair with respect to a novel metric when compared to true data  ...  This causal model contributes in generating counterfactual data to train a fair predictive model. Our framework is general enough to utilize any assumption within the causal model.  ...  the data and our setup can take care of it by inferring a posterior distribution over the unobserved attributes. 5) Any dataset with a simple embedding scheme can use this model.  ... 
arXiv:1911.11558v1 fatcat:gxy6ysg3o5e5tp76c5yxnbhxni

Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare

Davide Cirillo, Silvina Catuara-Solarz, Czuee Morey, Emre Guney, Laia Subirats, Simona Mellino, Annalisa Gigante, Alfonso Valencia, María José Rementeria, Antonella Santuccione Chadha, Nikolaos Mavridis
2020 npj Digital Medicine  
In this review we examine the current sex and gender gaps in a subset of biomedical technologies used in relation to Precision Medicine.  ...  Despite the significant scientific advances achieved so far, most of the currently used biomedical AI technologies do not account for bias detection.  ...  research, debate and public engagement.  ... 
doi:10.1038/s41746-020-0288-5 pmid:32529043 pmcid:PMC7264169 fatcat:dnzmvw65ije75dpd4wkuetutlu

Then and Now: Quantifying the Longitudinal Validity of Self-Disclosed Depression Diagnoses [article]

Keith Harrigian, Mark Dredze
2022 arXiv   pre-print
Our findings motivate three practical recommendations for improving mental health datasets curated using self-disclosed diagnoses: 1) Annotate diagnosis dates and psychiatric comorbidities; 2) Sample control  ...  We also provide expanded evidence for the presence of personality-related biases in datasets curated using self-disclosed diagnoses.  ...  Acknowledgements We thank Ayah Zirikly and Carlos Aguirre for contributing annotations to use for evaluating interrater reliability.  ... 
arXiv:2206.11155v1 fatcat:lenatqngfvcbpkqgfyqslz23pe

Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools

Oliver Diaz, Kaisar Kushibar, Richard Osuala, Akis Linardos, Lidia Garrucho, Laura Igual, Petia Radeva, Fred Prior, Polyxeni Gkontra, Karim Lekadir
2021 Physica medica (Testo stampato)  
privacy, (iii) data curation to control for image and associated information quality, (iv) image storage, and (v) image annotation.  ...  The pillar supporting the development of reliable and robust AI algorithms is the appropriate preparation of the medical images to be used by the AI-driven solutions.  ...  -B-C21) and Catalan government (grant SGR1742).  ... 
doi:10.1016/j.ejmp.2021.02.007 pmid:33684723 fatcat:ygrrorchbrautnwlaixrqq25hy

Parametric Bayesian Rejuvenation in Ambient Assisted Living through Software-based Thematic 5G Management [article]

Rossi Kamal, Choong Seon Hong
2016 arXiv   pre-print
Eventually, the major goal of this paper is to develop a context-aware model in predicting engagement of elderly care.  ...  The recent proliferation of IoT with the advent of smart-objects/things and personalized services pave the way for context-aware service management.  ...  cloudy, snowy, fair, sunny) and time (i.e. morning, lunch,evening, night), etc. Questionnaires are originally set  ... 
arXiv:1601.06258v1 fatcat:nnm4tg4uxnd3ho5yskxhptgos4

Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions

Eike Petersen, Yannik Potdevin, Esfandiar Mohammadi, Stephan Zidowitz, Sabrina Breyer, Dirk Nowotka, Sandra Henn, Ludwig Pechmann, Martin Leucker, Philipp Rostalski, Christian Herzog
2022 IEEE Access  
We notice that distribution shift, spurious correlations, model underspecification, uncertainty quantification, and data scarcity represent severe challenges in the medical context.  ...  Promising solution approaches include the use of large and representative datasets and federated learning as a means to that end, the careful exploitation of domain knowledge, the use of inherently transparent  ...  Neither the funding agency nor the involved private companies have had any influence on the planning and design of this survey, the writing of the manuscript, or the publication process.  ... 
doi:10.1109/access.2022.3178382 fatcat:cwpkgkx2ibcgbdatd4aidwa4xy
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