Conscientious Classification: A Data Scientist's Guide to Discrimination-Aware Classification

D'Alessandro, B; O’Neil, Cathy; LaGatta, Tom

doi:10.1089/big.2016.0048

Cited by 154 publications

(74 citation statements)

References 18 publications

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“…Sample size, inclusion, and selection of participants will have an overall effect on the performance of the algorithms due to the common issue of underestimation that was faced in previous studies that have utilized ML. Although the sample is not representative of the population in Qatar, it would have been adequate for generalization of results and proper assessment of the performance of the algorithms if our study had a larger sample size [ 60 , 61 ].…”

Section: Discussionmentioning

confidence: 99%

Predicting hypertension using machine learning: Findings from Qatar Biobank Study

et al. 2020

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Background and objective Hypertension, a global burden, is associated with several risk factors and can be treated by lifestyle modifications and medications. Prediction and early diagnosis is important to prevent related health complications. The objective is to construct and compare predictive models to identify individuals at high risk of developing hypertension without the need of invasive clinical procedures. Methods This is a cross-sectional study using 987 records of Qataris and long-term residents aged 18 + years from Qatar Biobank. Percentages were used to summarize data and chi-square tests to assess associations. Predictive models of hypertension were constructed and compared using three supervised machine learning algorithms: decision tree, random forest, and logistics regression using 5-fold cross-validation. The performance of algorithms was assessed using accuracy, positive predictive value (PPV), sensitivity, F-measure, and area under the receiver operating characteristic curve (AUC). Stata and Weka were used for analysis. Results Age, gender, education level, employment, tobacco use, physical activity, adequate consumption of fruits and vegetables, abdominal obesity, history of diabetes, history of high cholesterol, and mother's history high blood pressure were important predictors of hypertension. All algorithms showed more or less similar performances:

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Section: Discussionmentioning

confidence: 99%

Predicting hypertension using machine learning: Findings from Qatar Biobank Study

et al. 2020

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“…Underestimation occurs when a learning algorithm is trained on insufficient data and fails to provide estimates for interesting or important cases, instead approximating mean trends toavoid overfitting. 14 Low sample size and underestimation of minority groups are not unique to machine learning or electronic health record data but a common issue in other types of studies, such as randomized clinical trials and genetic studies. For instance, genetic studies have been criticized for not fully accounting for genetic diversity in non-European populations.…”

Section: Sample Size and Underestimationmentioning

confidence: 99%

Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data

et al. 2018

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A promise of machine learning in health care is the avoidance of biases in diagnosis and treatment; a computer algorithm could objectively synthesize and interpret the data in the medical record. Integration of machine learning with clinical decision support tools, such as computerized alerts or diagnostic support, may offer physicians and others who provide health care targeted and timely information that can improve clinical decisions. Machine learning algorithms, however, may also be subject to biases. The biases include those related to missing data and patients not identified by algorithms, sample size and underestimation, and misclassification and measurement error. There is concern that biases and deficiencies in the data used by machine learning algorithms may contribute to socioeconomic disparities in health care. This Special Communication outlines the potential biases that may be introduced into machine learning–based clinical decision support tools that use electronic health record data and proposes potential solutions to the problems of overreliance on automation, algorithms based on biased data, and algorithms that do not provide information that is clinically meaningful. Existing health care disparities should not be amplified by thoughtless or excessive reliance on machines.

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“…Muller et al summarized and extended this analysis, concluding that humans may intervene between data and analysis in five ways, according to each data science worker's professional discernment: discovery of data, capture of data, design of data, curation of data, and even creation of data [48]. In these ways, data science workers engage deeply with data before data science modeling activities, during model selection [62,68] and after model refinement, such as when testing for bias [5,72].…”

Section: Data Science Teams and Disciplinary Diversitymentioning

confidence: 99%

Human-AI Collaboration in Data Science

Wang

Weisz

Müller

et al. 2019

Proc. ACM Hum.-Comput. Interact.

211

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The rapid advancement of artificial intelligence (AI) is changing our lives in many ways. One application domain is data science. New techniques in automating the creation of AI, known as AutoAI or AutoML, aim to automate the work practices of data scientists. AutoAI systems are capable of autonomously ingesting and pre-processing data, engineering new features, and creating and scoring models based on a target objectives (e.g. accuracy or run-time efficiency). Though not yet widely adopted, we are interested in understanding how AutoAI will impact the practice of data science. We conducted interviews with 20 data scientists who work at a large, multinational technology company and practice data science in various business settings. Our goal is to understand their current work practices and how these practices might change with AutoAI. Reactions were mixed: while informants expressed concerns about the trend of automating their jobs, they also strongly felt it was inevitable. Despite these concerns, they remained optimistic about their future job security due to a view that the future of data science work will be a collaboration between humans and AI systems, in which both automation and human expertise are indispensable.

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Conscientious Classification: A Data Scientist's Guide to Discrimination-Aware Classification

Cited by 154 publications

References 18 publications

Predicting hypertension using machine learning: Findings from Qatar Biobank Study

Predicting hypertension using machine learning: Findings from Qatar Biobank Study

Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data

Human-AI Collaboration in Data Science

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