AI in Business and Society
Loading...
Paper Number
1846
Paper Type
Completed
Description
Literature on algorithmic bias identifies its source in either biased data or statistical methods, more rarely in the development of algorithmic solutions as a potential factor. Because of the prior unknowability of algorithms, data scientists developing such solutions have to take various design decisions. Drawing from the flow-oriented approach, we study algorithmic unknowability and how data scientists respond to it in 35 public data science Jupyter notebooks containing algorithmic solutions to predict customer churn in a credit card dataset on a data science platform Kaggle.com. We offer a more thorough understanding of the unknowability in algorithmic development that can enable algorithmic bias: resource, problem, dataset, analytical, model, and performance unknowability. We find that in response, data scientists engage in bias-enabling interpretation, bias-enabling optionalizing, and bias-enabling experimentation. These findings contribute to literature on algorithmic bias and can help avert bias earlier in practice.
Recommended Citation
Stelmaszak, Marta, "How To Train Your Algo: Investigating the Enablers of Bias in Algorithmic Development" (2021). ICIS 2021 Proceedings. 7.
https://aisel.aisnet.org/icis2021/ai_business/ai_business/7
How To Train Your Algo: Investigating the Enablers of Bias in Algorithmic Development
Literature on algorithmic bias identifies its source in either biased data or statistical methods, more rarely in the development of algorithmic solutions as a potential factor. Because of the prior unknowability of algorithms, data scientists developing such solutions have to take various design decisions. Drawing from the flow-oriented approach, we study algorithmic unknowability and how data scientists respond to it in 35 public data science Jupyter notebooks containing algorithmic solutions to predict customer churn in a credit card dataset on a data science platform Kaggle.com. We offer a more thorough understanding of the unknowability in algorithmic development that can enable algorithmic bias: resource, problem, dataset, analytical, model, and performance unknowability. We find that in response, data scientists engage in bias-enabling interpretation, bias-enabling optionalizing, and bias-enabling experimentation. These findings contribute to literature on algorithmic bias and can help avert bias earlier in practice.
When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.
Comments
11-AI