Tag: Science and Technology Studies

Why Business Anthropologists Should Reconsider Machine Learning

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This article is a follow-up to my previous article – “Integrating Ethnography and Data Science” – written specifically for anthropologists and other ethnographers.

As an anthropologist and data scientist, I often feel caught in the middle two distinct warring factions. Anthropologists and data scientists inherited a historic debate between quantitative and qualitative methodologies in social research within modern Western societies. At its core, this debate has centered on the difference between objective, prescriptive, top-downtechniques and subjective, sitautional, flexible, descritpive bottom-up approaches.[i] In this ensuing conflict, quantative research has been demarcated into the top-down faction and qualitative research within the bottom-up faction to the detriment of understanding both properly.

In my experience on both “sides,” I have seen a tendency among anthropologists to lump all quantitative social research as proscriptive and top-down and thus miss the important subtleties within data science and other quantitative techniques. Machine learning techniques within the field are a partial shift towards bottom-up, situational and iterative quantitative analysis, and business anthropologists should explore what data scientists do as a chance to redevelop their relationship with quantitative analysis.

Shifts in Machine Learning

Text Box: Data science is in a uniquely formative and adolescent period.

Shifts within machine learning algorithm development give impetus for incorporating quantitative techniques that are local and interpretive. The debate between top-down vs. bottom-up knowledge production does not need – or at least may no longer need– to divide quantitative and qualitative techniques. Machine learning algorithms “leave open the possibility of situated knowledge production, entangled with narrative,” a clear parallel to qualitative ethnographic techniques.[ii]

At the same time, this shift towards iterative and flexible machine learning techniques is not total within data science: aspects of top-down frameworks remain, in terms of personnel, objectives, habits, strategies, and evaluation criteria. But, seeds of bottom-up thinking definitely exist prominently within data science, with the potential to significantly reshape data science and possibly quantitative analysis in general.

As a discipline, data science is in a uniquely formative and adolescent period, developing into its “standard” practices. This leads to significant fluctuations as the data scientist community defines its methodology. The set of standard practices that we now typically call “traditional” or “standard” statistics, generally taught in introductory statistics courses, developed over a several decade period in the late nineteenth and early twentieth century, especially in Britain.[iii] Connected with recent computer technology, data science is in a similarly formative period right now – developing its standard techniques and ways of thinking. This formative period is a strategic time for anthropologists to encourage bottom-up quantative techniques.

Conclusion

Business anthropologists could and should be instrumental in helping to develop and innovatively utilize these situational and iterative machine learning techniques. This is a strategic time for business anthropologists to do the following:

  1. Immerse themselves into data science and encourage and cultivate bottom-up quantative machine learning techniques within data science
  2. Cultivate and incorporate (when applicable) situational and iterative machine learning approaches in its ethnographies

For both, anthropologists should use the strengths of ethnographic and anthropological thinking to help develop bottom-up machine learning that is grounded in flexible to specific local contexts. Each requires business anthropologists to reexplore their relationship with data science and machine learning instead of treating it as part of an opposing “methodological clan.” [iv]

[i] Nafus, D., & Knox, H. (2018). Ethnography for a Data-Saturated World. Manchester: Manchester University Press, 11-12

[ii] Ibid, 15-17.

[iii] Mackenzie, D. (1981). Statistics in Britain 1865–1930: The Social Construction of Scientific Knowledge. Edinburgh: Edinburgh University Press.

[iv] Seaver, N. (2015). Bastard Algebra. In T. Boellstorff, & B. Maurer, Data, Now Bigger and Better (pp. 27-46). Chicago: Prickly Paradigm Press, 39.

Breaking into Tech: A Career Workshop

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Earlier this week, Matt Artz, Astrid Countee, and I ran a workshop at the American Anthropological Association’s 2020 annual conference entitled “Breaking into Tech.” We discussed strategies for anthropologists interested in working in the tech world.

Here is the presentation for anyone who might find it useful but could not attend:

Download [6.86 MB]

Thank you, Astrid and Matt, for your help in developing and running this workshop.  

Recently Published Article: “Anthropology by Data Science”

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I am pleased to announce that the Annals of Anthropological Practice has accepted my article “Anthropology by Data Science.” https://anthrosource.onlinelibrary.wiley.com/doi/10.1111/napa.12169. In it, I reflect on the relationship anthropologist have cultivated with data science as a discipline and the importance of integrating machine learning techniques into ethnographic practice.

Annals of Anthropological Practice is overseen by the National Association for the Practice of Anthropology (NAPA) within the American Anthropological Association. Thank you, NAPA, for publishing my article and thank you to all the unnamed editors and reviewers in the process.

Interdisciplinary Anthropology and Data Science Master’s Thesis: A Quick and Dirty Project Summary

This is a quick and dirty summary of my master’s practicum research project with Indicia Consulting over the summer of 2018. For anyone interested in more detail, here is a more detailed report, and here is the final report with Indicia. 

Background

My practicum was the sixth stage of a several year-long research project. The California Energy Commission commissioned this larger project to understand the potential relationship between individual energy consumption and technology usage. In stages one through five, we isolated certain clusters of behavior and attitudes around new technology adoption – which Indicia called cybersensitivity – and demonstrated that cybersensitivity tended to associate with a willingness to adopt energy-saving technology like smart meters.

This led to a key question: How can one identify cybersensivity among a broader population such as a community, county, or state? Answering this question was the main goal of my practicum project.

In the past stages of the research project, the team used ethnographic research to establish criteria for whether someone was a cybersensitive based on several hours of interviews and observations about their technology usage. These interviews and observations certainly helped the research team analyze behavioral and attitudinal patterns, determine what patterns were significant, and develop those into the concept of cybersensitivity, but they are too time- and resource-intensive to perform with an entire population. One generally does not have the ability to interview everyone in a community, county, or state. I sought to address this directly in my project.

TaskTimelineTask NameResearch TechniqueDescription
Task 1June 2015-Sept 2018General Project TasksAdministrative (N/A)Developed project scope and timeline, adjusting as the project unfolds
Task 2July 2015 – July 2016Documenting and analyzing emerging attitudes, emotions, experiences, habits, and practices around technology adoptionSurveyConducted survey research to observe patterns of attitudes and behaviors among cybersensitives/awares.
Task 3Sept 2016 – Dec 2016Identifying the attributes and characteristics and psychological drivers of cybersensitivesInterviews and Participant-ObservationConducted in-depth interviews and observations coding for psych factor, energy consumption attitudes and behaviors, and technological device purchasing/usage.
Task 4*Sept 2016 – July 2017Assessing cybersensitives’ valence with technologyStatistical AnalysisTested for statistically significant differences in demographics, behaviors, and beliefs/attitudes between cyber status groups
Task 5Aug 2017 – Dec 2018  Developing critical insights for supporting residential engagement in energy efficient behaviorsStatistical AnalysisAnalyzed utility data patterns of study participants, comparing it with the general population.
Task 6March 2018 – Aug 2018Recommending an alternative energy efficiency potential modelDecision Tree ModelingConstructed decision tree models to classify an individual’s cyber status

Project Goal

The overall goal for the project was to produce a scalable method to assess whether someone exhibits cybersensitivity based on data measurable across an entire population. In doing this, the project also helped address the following research needs:

  1. Created a method to further to scale across a larger population, assessing whether cybersensitives were more willing to adopt energy saving technologies across a community, county, or state
  2. Provided the infrastructure to determine how much promoting energy-saving campaigns targeting cybersensitives specifically would reduce energy consumption in California
  3. Helped the California Energy Commission determine the best means to reach cybersensitives for specific energy-saving campaigns

The Project

I used machine learning modeling to create a decision-making flow to isolate cybersensitives in a population. Random forests and decision trees produced the best models for Indicia’s needs: random forests in accuracy and robustness and decision trees in human decipherability. Through them, I created a programmable yet human-comprehensible framework to determine whether an individual is cybersensitive based on behaviors and other characteristics that an organization could be easily assess within a whole population. Thus, any energy organization could easily understand, replicate, and further develop the model since it was both easy for humans to read and encodable computationally. This way organizations could both use and refine it for their purposes.

Conclusion

This is a quick overview of my master’s practicum project. For more details on what modeling I did, how I did it, what results it produced, and how it fit within the wider needs of the multi-year research project, please see my full report.

I really appreciated the opportunity it posed to get my hands dirty integrating ethnography and data science to help address a real-world problem. This summary only scratches the surface of what Indicia did with the Californian Energy Commission to encourage sustainable energy usage societally. Hopefully, though, it will inspire you to integrate ethnography and data science to address whatever complex questions you face. It certainly did for me.

Thank you to Susan Mazur-Stommen and Haley Gilbert for your help in organizing and completing the project. I would like to thank my professorial committee at the University of Memphis – Dr. Keri Brondo, Dr. Ted Maclin, Dr. Deepak Venugopal, and Dr. Katherine Hicks – for their academic support as well.

The Anthropology of Machine Learning

In the spring of 2018, I researched how anthropologists and related social scholars have analyzed data science and machine learning for my Master’s in Anthropology at the University of Memphis. For the project, I assessed the anthropological literature on data science and machine learning to date and explore potential connections between anthropology and data science, based on my perspective as a data scientist and anthropologist. Here is my final report.

Thank you, Dr. Ted Maclin, for your help overseeing and assisting this project.

Download [120.57 KB]

Anthropology by Data Science: The EPIC Project with Indicia Consulting as an Exploratory Case Study

This is my practicum report with Indicia Consulting. In lieu of a master’s thesis, the University of Memphis Department of Anthropology required that we master’s students conduct a practicum project. For this, we had to partner with an organization and complete a 300+ hour anthropological research project based on the organization’s needs and our skills and interests. My practicum project was Indicia’s EPIC Project with the California Energy Commission (see this link and this link for more details on the EPIC Project). In this report, I outline potential ways to integrate ethnographic/anthropological and data science research in professional settings.

In November 2019, the American Anthropological Association’s Committee for the Anthropology of Science, Technology, and Computing (CASTAC) awarded me the David Hakken Graduate Student Prize for innovative science and technology scholarship.

Full Report:

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Download [1.56 MB]

The Anthropology Department also required that you publicly present your practicum research to the University of Memphis campus. This PowerPoint summarizes my practicum project. If you are not keen to read the 99 page full report, this is a much shorter alternative:

Download [1.05 MB]

If you are interested in learning more about the project, please check out the following:

  1. Indicia Consulting’s Final Research Report with the California Energy Commission
  2. My Presentation at the 2019 Memphis Data Conference for Data Scientists Specifically

Computerized Knowledge Production: Machine Learning Models as Social Actors

The following is a presentation I gave at the Society for Applied Anthropology’s 2018 annual conference in Philadelphia, PA. In it, I describe how I think anthropologists should understand, analyze, and relate to machine learning and data science.

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Memphis Data Conference: Anthropology by Data Science: The EPIC Project with Indicia Consulting as an Exploratory Case Study

Below is a talk I gave at the 2019 Memphis Data conference, organized by the University of Memphis to discuss data science research in the Memphian community. In this presentation, I summarize a project I did with Indicia Consulting that integrated data science and ethnography.

Check out these articles for a more detailed description of the projects: a short project summary, my master’s thesis about the project, and Indicia’s full report.

Download [2.85 MB]

Applied Anthropology Conference Presentation: Integrating Anthropology and Data Science

On July 8th, 2021, I presented virtually at the Congress of Anthropologists and Ethnologists of Russia in Tomsk, Siberia, organized by Association of Anthropologists and Ethnologists of Russia. My talk was titled “Integrating Anthropology and Data Science,” which I presented as part of its subcommittee for applied and business anthropology. I discussed the unique opportunities integrating data science could provide anthropologists and potential strategies for how to integrate the two disciplines.

Here was my original abstract for the conference:

Download [12.62 KB]

Here is my full presentation:

Download [119.66 KB]

I had a great time, and I hope you enjoy it as well.