Tag: Machine Learning

Human Bias in Machine Learning: How Well Do You Really Know Your Model?

Artificial Intelligence, Biased Data, Biased Results, Local Interpretable Model-Agnostic Explanations, Machine Learning, Objective Algorithm, Representation in Training Datasets, Training Data

Author: Jim Box, Elena Snavely, and Hiwot Tesfaye; Publisher: SAS Global Forum; Publication Year: 2022. The following paper discusses how it is a common consensus that artificial intelligence (AI) and machine learning (ML) are going…
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fairmodels: a Flexible Tool for Bias Detection, Visualization, and Mitigation in Binary Classification Models

Black Box Algorithms, Explainable Models, Fairness, Machine Learning, Unexplainable Models

Author: Jakub Wisniewski, Przemyslaw Biecek; Publisher: The R Journal; Publication Year: 2022. The following article discusses how as more sophisticated machine learning methods become more ubiquitous, a culture of classifying between “explainable” and “unexplainable” models…
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Data Ethics in Oil and Gas Operations

Artificial Intelligence, Data Safety, Machine Learning

Author: Iraj Ershaghi, Milad A. Ershaghi, Andrei Popa; Publisher: One Petro; Publication Year: 2018. The following article discusses how machine learning and artificial intelligence are being used in the oil and gas industry, and they…
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Health Data Poverty: An Assailable Barrier to Equitable Digital Health Care

Data Collection, Data Privacy, Equity Gap, Health Data, Health Data Poverty, Healthcare Inequality, Healthcare Services, Machine Learning, Representation, Transparency

Author: Hussein Ibrahim, Xiaoxuan Liu, Nevine Zariffa, Andrew D. Morris, Alastair K. Denniston; Publisher: The Lancet Digital Health; Publication Year: 2021. The following study identifies how machine learning and advanced technologies can now be used…
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Manipulating SHAP via Adversarial Data Perturbations (Student Abstract)

Black Box Algorithms, Data Distributions, Machine Learning, Shapley Additive Explanations

Author: Hubert Baniecki, Przemyslaw Biecek; Publisher: Proceedings of the AAAI Conference on Artificial Intelligence; Publication Year: 2022. The following article discusses how Shapley Additive Explanations (SHAP) values are a popular metric for identifying variable importance…
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Bringing Principles of Ethics to AI and Drug Design

Artificial Intelligence, Chemical Compound Data, Drug Development, Historical Medical Data, Machine Learning, Medication Research, Predictions

Author: Helen Santoro; Publisher: Scope; Publication Year: 2022. The following article discusses how artificial intelligence (AI) has proven potent in analyzing historical medical data, chemical compound data, and using it to predict viable treatments. As…
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Digital Epidemiology: The Ethics of Using Health Data in a Pandemic

Contract Tracing, Epidemiology, Machine Learning

Author: N/A Publisher: Health Europa Publication Year: 2020 Summary: The following article brings attention to ethical concerns present in our digital world and epidemiology. It discusses the ethics of contract tracing. There has to be…
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The Problem with “Biased Data”

Machine Learning, Mental Framework, Terminology & Language

Author: Harini Suresh; Publisher: Medium; Publication Year: 2019. In the following article, the author points out that language matters and that the right terminology forms a mental framework. The author also provides a framework for…
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The Danger of Predictive Algorithms in Criminal Justice

Algorithms, Machine Learning, Predictive Algorithms

Author: Hany Farid; Publisher: TEDx Talks; Publication Year: 2018. The following video discusses how predictive algorithms are widespread and have ethical issues. He gave one study example where random internet people were given prompts and…
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