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How To Train Your Algo: Investigating The Enablers Of Bias In Algorithmic Development, Marta Stelmaszak Rosa
How To Train Your Algo: Investigating The Enablers Of Bias In Algorithmic Development, Marta Stelmaszak Rosa
Business Faculty Publications and Presentations
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 …
Unboxing The Algorithm: A Process Model Of An Algorithmic Solution, Marta Stelmaszak Rosa
Unboxing The Algorithm: A Process Model Of An Algorithmic Solution, Marta Stelmaszak Rosa
Business Faculty Publications and Presentations
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 …
Toward A Bidirectional View Of Causality In Big Data Analytics: The Case Of Learning Analytics, Marta Stelmaszak Rosa, Alexi Aaltonen
Toward A Bidirectional View Of Causality In Big Data Analytics: The Case Of Learning Analytics, Marta Stelmaszak Rosa, Alexi Aaltonen
Business Faculty Publications and Presentations
Currently most of the managerial literature on big data analytics assumes a straightforward, unidirectional relationship between data and phenomena they describe. Drawing from critical perspectives on big data, this paper posits that a bidirectional view of causality in big data analytics is needed. Relying on the theory of reactivity by Espeland and Sauder, the authors designed a mixed-methods case study involving both interviewing and a computational analysis of a big data set to lay bare the mechanisms at play behind the intended and unintended consequences in a learning analytics system deployed at a major UK business school. The authors argue …