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Paper Type
Complete
Paper Number
1588
Description
With the explosion of data, analytics and artificial intelligence, information systems research focuses on the use, management and consequences of algorithms. This far, only a handful of papers offer insights into how algorithmic solutions work. To address this gap, we studied the code making up 45 public data science Jupyter notebooks containing algorithmic solutions developed to predict customer churn in a credit card dataset on a data science platform Kaggle.com. We synthesized a process model of an algorithmic solution: preparing the environment, reading in data, cleaning data, exploratory data analysis, pre-processing the dataset, building and training the model, and testing and validating model. Unboxing the algorithm and investigating the process offers a more fine-tuned understanding and language to better conceptualize the use, management and consequences of algorithmic solutions. It also provides a scaffolding for research into the development of algorithmic solutions, highlighting their variability, experimentation and data scientist decisions.
Recommended Citation
Stelmaszak, Marta, "Unboxing the Algorithm: A Process Model of an Algorithmic Solution" (2021). AMCIS 2021 Proceedings. 19.
https://aisel.aisnet.org/amcis2021/art_intel_sem_tech_intelligent_systems/art_intel_sem_tech_intelligent_systems/19
Unboxing the Algorithm: A Process Model of an Algorithmic Solution
With the explosion of data, analytics and artificial intelligence, information systems research focuses on the use, management and consequences of algorithms. This far, only a handful of papers offer insights into how algorithmic solutions work. To address this gap, we studied the code making up 45 public data science Jupyter notebooks containing algorithmic solutions developed to predict customer churn in a credit card dataset on a data science platform Kaggle.com. We synthesized a process model of an algorithmic solution: preparing the environment, reading in data, cleaning data, exploratory data analysis, pre-processing the dataset, building and training the model, and testing and validating model. Unboxing the algorithm and investigating the process offers a more fine-tuned understanding and language to better conceptualize the use, management and consequences of algorithmic solutions. It also provides a scaffolding for research into the development of algorithmic solutions, highlighting their variability, experimentation and data scientist decisions.
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