Waking up to Marginalization: Public Value Failures in Artificial Intelligence and Data Science

Thema Monroe-White, Brandeis Marshall, Hugo Contreras-Palacios
Proceedings of 2nd Workshop on Diversity in Artificial Intelligence (AIDBEI), PMLR 142:7-21, 2021.

Abstract

Data science education is increasingly becoming an integral part of many educational structures, both informal and formal. Much of the attention has been on the application of AI principles and techniques, especially machine learning, natural language processing and predictive analytics. While AI is only one phase in the data science ecosystem, we must embrace a fuller range of job roles that help manage AI algorithms and systems — from the AI innovators and architects (in CS, Math and Statistics) to the AI technicians and specialists (in CS, IT and IS). Also, it’s important that we better understand the current state of the low participation and representation of minoritized groups that further stifles the accessibility and inclusion efforts. However, how we learn and what we learn is highly dependent on who we are as learners. In this paper, we examine demographic disparities by race/ethnicity and gender within the information systems educational infrastructure from an evaluative perspective. More specifically, we adopt intersectional methods and apply the theory of public value failure to identify learning gaps in the fast-growing field of data science. National datasets of Master’s and Doctoral graduate students in IS, CS, Math and Statistics are used to create an “institutional parity score” which calculates field-specific representation by race/ethnicity and gender in data science related fields. We conclude by showcasing bias creep including the situational exclusion of individuals from access to the broader information economy, be it access to technologies and data or access to participate in the data workforce or data enabled-economic activity. Policy recommendations are suggested to curb and reduce this marginalization within information systems and related disciplines.

Cite this Paper

BibTeX
@InProceedings{pmlr-v142-monroe-white21a, title = {Waking up to Marginalization: Public Value Failures in Artificial Intelligence and Data Science}, author = {Monroe-White, Thema and Marshall, Brandeis and Contreras-Palacios, Hugo}, booktitle = {Proceedings of 2nd Workshop on Diversity in Artificial Intelligence (AIDBEI)}, pages = {7--21}, year = {2021}, editor = {Lamba, Deepti and Hsu, William H.}, volume = {142}, series = {Proceedings of Machine Learning Research}, month = {09 Feb}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v142/monroe-white21a/monroe-white21a.pdf}, url = {https://proceedings.mlr.press/v142/monroe-white21a.html}, abstract = {Data science education is increasingly becoming an integral part of many educational structures, both informal and formal. Much of the attention has been on the application of AI principles and techniques, especially machine learning, natural language processing and predictive analytics. While AI is only one phase in the data science ecosystem, we must embrace a fuller range of job roles that help manage AI algorithms and systems — from the AI innovators and architects (in CS, Math and Statistics) to the AI technicians and specialists (in CS, IT and IS). Also, it’s important that we better understand the current state of the low participation and representation of minoritized groups that further stifles the accessibility and inclusion efforts. However, how we learn and what we learn is highly dependent on who we are as learners. In this paper, we examine demographic disparities by race/ethnicity and gender within the information systems educational infrastructure from an evaluative perspective. More specifically, we adopt intersectional methods and apply the theory of public value failure to identify learning gaps in the fast-growing field of data science. National datasets of Master’s and Doctoral graduate students in IS, CS, Math and Statistics are used to create an “institutional parity score” which calculates field-specific representation by race/ethnicity and gender in data science related fields. We conclude by showcasing bias creep including the situational exclusion of individuals from access to the broader information economy, be it access to technologies and data or access to participate in the data workforce or data enabled-economic activity. Policy recommendations are suggested to curb and reduce this marginalization within information systems and related disciplines.} }
Endnote
%0 Conference Paper %T Waking up to Marginalization: Public Value Failures in Artificial Intelligence and Data Science %A Thema Monroe-White %A Brandeis Marshall %A Hugo Contreras-Palacios %B Proceedings of 2nd Workshop on Diversity in Artificial Intelligence (AIDBEI) %C Proceedings of Machine Learning Research %D 2021 %E Deepti Lamba %E William H. Hsu %F pmlr-v142-monroe-white21a %I PMLR %P 7--21 %U https://proceedings.mlr.press/v142/monroe-white21a.html %V 142 %X Data science education is increasingly becoming an integral part of many educational structures, both informal and formal. Much of the attention has been on the application of AI principles and techniques, especially machine learning, natural language processing and predictive analytics. While AI is only one phase in the data science ecosystem, we must embrace a fuller range of job roles that help manage AI algorithms and systems — from the AI innovators and architects (in CS, Math and Statistics) to the AI technicians and specialists (in CS, IT and IS). Also, it’s important that we better understand the current state of the low participation and representation of minoritized groups that further stifles the accessibility and inclusion efforts. However, how we learn and what we learn is highly dependent on who we are as learners. In this paper, we examine demographic disparities by race/ethnicity and gender within the information systems educational infrastructure from an evaluative perspective. More specifically, we adopt intersectional methods and apply the theory of public value failure to identify learning gaps in the fast-growing field of data science. National datasets of Master’s and Doctoral graduate students in IS, CS, Math and Statistics are used to create an “institutional parity score” which calculates field-specific representation by race/ethnicity and gender in data science related fields. We conclude by showcasing bias creep including the situational exclusion of individuals from access to the broader information economy, be it access to technologies and data or access to participate in the data workforce or data enabled-economic activity. Policy recommendations are suggested to curb and reduce this marginalization within information systems and related disciplines.
APA
Monroe-White, T., Marshall, B. & Contreras-Palacios, H.. (2021). Waking up to Marginalization: Public Value Failures in Artificial Intelligence and Data Science. Proceedings of 2nd Workshop on Diversity in Artificial Intelligence (AIDBEI), in Proceedings of Machine Learning Research 142:7-21 Available from https://proceedings.mlr.press/v142/monroe-white21a.html.

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