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Bias and ethical considerations in machine learning and the automation of perioperative risk assessment
10 references·3 citing·37 second hop
Bias and ethical considerations in machine learning and the automation of perioperative risk assessment (2020)Bias and ethical considerations in machin…A Survey on Bias and Fairness in Machine Learning (2021)A Survey on Bias and Fairness…Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure (2019)Uncovering and Mitigating Alg…Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed (2019)Reporting guidelines for clin…Identifying and Correcting Label Bias in Machine Learning (2019)Identifying and Correcting La…Dissecting racial bias in an algorithm used to manage the health of populations (2019)Dissecting racial bias in an …Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification (2018)Gender Shades: Intersectional…Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings (2016)Man is to Computer Programmer…FairML : ToolBox for diagnosing bias in predictive modeling (2016)FairML : ToolBox for diagnosi…Simpson's paradox in psychological science: a practical guide (2013)Simpson's paradox in psycholo…Learning Fair Representations (2013)Learning Fair RepresentationsBiases in Artificial Intelligence Application in Pain Medicine (2025)Biases in Artificial Intellig…Ethical Considerations in AI Development (2024)Ethical Considerations in AI …Racial underrepresentation in dermatological datasets leads to biased machine learning models and inequitable healthcare (2022)Racial underrepresentation in…Fairness through awareness (2012)Algorithmic Decision Making and the Cost of Fairness (2017)Counterfactual Fairness (2017)AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias (2019)Latent Class Cluster Analysis (2002)Investigating population heterogeneity with factor mixture models. (2005)Development and Distortion of Malaysian Public- Private Partnerships: Patronage, Privatised Profits and Pitfalls DOI: 10.1111/j.1467- 8500.2009.00655.x (2010)Mental disorders as networks of problems: a review of recent insights (2016)Maintenance, reserve and compensation: the cognitive neuroscience of healthy ageing (2018)Big Data's Disparate Impact (2014)Certifying and Removing Disparate Impact (2015)The role of artificial intelligence in achieving the Sustainable Development Goals (2020)Equality of Opportunity in Supervised Learning (2016)Towards Debiasing Fact Verification Models (2019)A survey on bias in visual datasets (2022)Fairness in Graph Mining: A Survey (2023)Semantics derived automatically from language corpora contain human-like biases (2017)Investigating Bias and Fairness in Facial Expression Recognition (2020)Understanding and Mitigating Annotation Bias in Facial Expression Recognition (2021)AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications (2023)Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments (2017)Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence (2020)Addressing bias in big data and AI for health care: A call for open science (2021)Avoidable waste in the production and reporting of research evidence (2009)CONSORT 2010 statement: Updated guidelines for reporting parallel group randomised trials (2011)SPIRIT 2013: new guidance for content of clinical trial protocols (2013)Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques (2020)Artificial intelligence for older people receiving long-term care: a systematic review of acceptability and effectiveness studies (2022)PRISMA AI reporting guidelines for systematic reviews and meta-analyses on AI in healthcare (2023)LiFT (2020)Dbias: detecting biases and ensuring fairness in news articles (2022)Toward Involving End-users in Interactive Human-in-the-loop AI Fairness (2022)The Ethics of AI Ethics: An Evaluation of Guidelines (2020)ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope (2023)Algorithmic Bias in Education (2021)Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation (2023)Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies (2023)
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References

  • 2019Dissecting racial bias in an algorithm used to manage the health of populations7,086 cited
  • 2021A Survey on Bias and Fairness in Machine Learning5,798 cited
  • 2018Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification3,509 cited
  • 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings1,615 cited
  • 2013Learning Fair Representations1,126 cited
  • 2013Simpson's paradox in psychological science: a practical guide539 cited
  • 2019Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure205 cited
  • 2019Identifying and Correcting Label Bias in Machine Learning152 cited
  • 2019Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed142 cited
  • 2016FairML : ToolBox for diagnosing bias in predictive modeling70 cited

Cited by

  • 2022Racial underrepresentation in dermatological datasets leads to biased machine learning models and inequitable healthcare41 cited
  • 2024Ethical Considerations in AI Development39 cited
  • 2025Biases in Artificial Intelligence Application in Pain Medicine19 cited

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