Tag: Business Anthropology

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.

Data Scientist, Anthropologist, and Entrepreneur: Interview with Schaun Wheeler (Interview #2 in the Interview Series)

For my second interview in the Interview Series, I interviewed Schaun Wheeler. Schaun is co-founder of Aampe, a startup that embeds an active learning system into mobile apps to turn push notifications into part of the app’s user interface. Before he co-founded Aampe, Schaun was the data science lead for the award-winning Consumer Graph intelligence product at Valassis, a U.S. ad-tech firm. And before that he founded and directed the data science team at Success Academy Charter Schools in New York City. Then before that, Schaun was one of the first people to champion the use of statistical inference to understand massive unstructured data at the United States Department of the Army. Schaun has a Ph.D. in Cultural Anthropology from the University of Connecticut.

If the audio does not play on your computer, you can download it here:

Schaun-Interview-AudioDownload

Over our conversation, we discussed the following:

  • Schaun’s experiences as both a data scientist and anthropologist
  • His utilization of anthropology within data science to decipher the right problem before launching into data science solutions
  • Recommendations for how anthropologists can develop data science and programming skills
  • His experiences starting a new data science consumer and market-research based company

To learn more about Schaun Wheeler and Aampe, check these out:

LinkedIn (the best way to contact him): https://www.linkedin.com/in/schaunwheeler/

Medium: https://medium.com/@schaun.wheeler

Twitter: https://twitter.com/schaunw

Aampe website: https://www.aampe.com/

Aampe blog: https://www.aampe.com/blog

A User Story, The Data Science Children’s Book: https://www.aampe.com/blog/a-user-story

More Detailed Walkthrough: Clip #1: https://www.youtube.com/playlist?list=PL03WDMCL2PHjRd8Y8USzvVkcIyQM57FMU and Clip #2: https://youtu.be/kwk_Ot8orPY

Previous Interview in the Interview Series: https://ethno-data.com/astrid-interview-1/

Breaking into Tech: A Career Workshop

code projected over woman
Photo by ThisIsEngineering on Pexels.com

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.  

Four Innovative Projects that Integrated Data Science and Ethnography

In a previous article, I have discussed the value of integrating data science and ethnography. On LinkedIn, people commented that they were interested and wanted to hear more detail on potential ways to do this. I replied, “I have found explaining how to conduct studies that integrate the two practically is easier to demonstrate through example than abstractly since the details of how to do it vary based on the specific needs of each project.”

In this article, I intend to do exactly that: analyze four innovative projects that in some way integrated data science and ethnography. I hope these will spur your creative juices to help think through how to creatively combine them for whatever project you are working on.

Synopsis:

Project:How It Integrated Data Science and Ethnography:Link to Learn More:
No Show ModelUsed ethnography to design machine learning softwarehttps://ethno-data.com/show-rate-predictor/
Cybersensitivity StudyUsed machine learning to scale up the scope of an ethnographic inquiry to a larger populationhttps://ethno-data.com/masters-practicum-summary/
Facebook Newsfeed Folk TheoriesUsed ethnography to understand how users make sense of and behave towards a machine learning system they encounter and how this, in turn, shapes the development of the machine learning algorithm(s)https://dl.acm.org/doi/10.1145/2858036.2858494
Thing EthnographyUsed machine learning to incorporate objects’ interactions into ethnographic researchhttps://dl.acm.org/doi/10.1145/2901790.2901905 and https://www.semanticscholar.org/paper/Things-Making-Things%3A-An-Ethnography-of-the-Giaccardi-Speed/2db5feac9cc743767fd23aeded3aa555ec8683a4?p2df

Project 1: No Show Model

A medical clinic at a hospital system in New York City asked me to use machine learning to build a show rate predictor in order to inform an improve its scheduling practices. During the initial construction phase, I used ethnography to both understand in more depth understand the scheduling problem the clinic faced and determine an appropriate interface design.

Through an ethnographic inquiry, I discovered the most important question(s) schedulers ask when scheduling their appointments. This was, “Of the people scheduled for a given doctor on a particular day, how many of them are likely to actually show up?” I then built a machine learning model to answer this exact question. My ethnographic inquiry provided me the design requirements for the data science project.  

In addition, I used my ethnographic inquiries to design the interface. I observed how schedulers interacted with their current scheduling software, which gave me a sense for what kind of visualizations would work or not work for my app.

This project exemplifies how ethnography can be helpful both in the development stage of a machine learning project to determine machine learning algorithm(s) needs and on the frontend when communicating the algorithm(s) to and assessing its successfulness with its users.

As both an ethnographer and a data scientist, I was able to translate my ethnographic insights seamlessly into machine learning modeling and API specifications and also conducted follow-up ethnographic inquiries to ensure that what I was building would meet their needs.

Project 2: Cybersensitivity Study

I conducted this project with Indicia Consulting. Its goal was to explore potential connections between individuals’ energy consumption and their relationship with new technology. This is an example of using ethnography to explore and determine potential social and cultural patterns in-depth with a few people and then using data science to analyze those patterns across a large population.

We started the project by observing and interviewing about thirty participants, but as the study progressed, we needed to develop a scalable method to analyze the patterns across whole communities, counties, and even states.

Ethnography is a great tool for exploring a phenomenon in-depth and for developing initial patterns, but it is resource-intensive and thus difficult to conduct on a large group of people. It is not practical for saying analyzing thousands of people. Data science, on the other hand, can easily test the validity across an entire population of patterns noticed in smaller ethnographic studies, yet because it often lacks the granularity of ethnography, would often miss intricate patterns.

Ethnography is also great on the back end for determining whether the implemented machine learning models and their resulting insights make sense on the ground. This forms a type of iterative feedback loop, where data science scales up ethnographic insights and ethnography contextualizes data science models.

Thus, ethnography and data science cover each other’s weaknesses well, forming a great methodological duo for projects centered around trying to understand customers, users, colleagues, or other users in-depth.

Project 3: Facebook Newsfeed Folk Theories

In their study, Motahhare Eslami and her team of researchers conducted an ethnographic inquiry into how various Facebook users conceived of how the Facebook Newsfeed selects which posts/stories rise to the top of their feeds. They analyze several different “folk theories” or working theories by everyday people for the criteria this machine learning system uses to select top stories.

How users think the overall system works influences how they respond to the newsfeed. Users who believe, for example, that the algorithm will prioritize the posts of friends for whom they have liked in the past will often intentionally like the posts of their closest friends and family so that they can see more of their posts.

Users’ perspectives on how the Newsfeed algorithm works influences how they respond to it, which, in turn, affects the very data the algorithm learns from and thus how the algorithm develops. This creates a cyclic feedback loop that influences the development of the machine learning algorithmic systems over time.

Their research exemplifies the importance of understanding how people think about, respond to, and more broadly relate with machine learning-based software systems. Ethnographies into people’s interactions with such systems is a crucial way to develop this understanding.

In a way, many machine learning algorithms are very social in nature: they – or at least the overall software system in which they exist – often succeed or fail based on how humans interact with them. In such cases, no matter how technically robust a machine learning algorithm is, if potential users cannot positively and productively relate to it, then it will fail.

Ethnographies into the “social life” of machine learning software systems (by which I mean how they become a part of – or in some cases fail to become a part of – individuals’ lives) helps understand how the algorithm is developing or learning and determine whether they are successful in what we intended them to do. Such ethnographies require not only in-depth expertise in ethnographic methodology but also an in-depth understanding how machine learning algorithms work to in turn understand how social behavior might be influencing their internal development.

Project 4: Thing Ethnography

Elise Giaccardi and her research team have been pioneering the utilization of data science and machine learning to understand and incorporate the perspective of things into ethnographies. With the development of the internet of things (IOT), she suggests that the data from object sensors could provide fresh insights in ethnographies of how humans relate to their environment by helping to describe how these objects relate to each other. She calls this thing ethnography.

This experimental approach exemplifies one way to use machine learning algorithms within ethnographies as social processes/interactions in of themselves. This could be an innovative way to analyze the social role of these IOT objects in daily life within ethnographic studies. If Eslami’s work exemplifies a way to graft ethnographic analysis into the design cycle of machine learning algorithms, Giaccardi’s research illustrates one way to incorporate data science and machine learning analysis into ethnographies.

Conclusion

Here are four examples of innovative projects that involve integrating data science and ethnography to meet their respective goals. I do not intend these to be the complete or exhaustive account of how to integrate these methodologies but as food for thought to spur further creative thinking into how to connect them.

For those who, when they hear the idea of integrating data science and ethnography, ask the reasonable question, “Interesting but what would that look like practically?”, here are four examples of how it could look. Hopefully, they are helpful in developing your own ideas for how to combine them in whatever project you are working on, even if its details are completely different.

Photo credit #1: StartupStockPhotos at https://pixabay.com/photos/startup-meeting-brainstorming-594090/

Photo credit #2: DarkoStojanovicat at https://pixabay.com/photos/medical-appointment-doctor-563427/  

Photo credit #3: NASA at https://unsplash.com/photos/Q1p7bh3SHj8  

Photo credit #4: Kon Karampelas at https://unsplash.com/photos/HUBofEFQ6CA

Photo credit #5: Pixabay at https://www.pexels.com/photo/app-business-connection-device-221185/  

UX Research and Business Anthropology Are Central within Applied Anthropology

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This is a research paper I wrote for a master’s course on Applied Anthropology at the University of Memphis. The overall master’s program sought to train students in applied anthropology, and the goal of this course was to teach the foundations of what applied anthropology is, in contrast to other types of anthropology.

Even though I found the course interesting, its curriculum lacked the readings and perspectives of applied anthropologists in the business world. As I discuss in the paper, statistically speaking, a significant number of applied anthropologists (and a University of Memphis’s applied anthropology program alum) work in the business sector, so excluding them leaves out what might be the largest group of applied anthropologists from their own field. I wrote this essay as a subtle nudge to encourage the course designers to add the works of business anthropologists, particularly UX researchers, into their curriculum.

Due to the lack of resources by applied business anthropologists in the curriculum, I had to assemble my own resources entirely by myself. Other applied anthropologists have told me they have encountered this as well. So, hopefully, in addition to the essay potentially providing helpful analysis of applied business anthropology, its bibliography might also provide a starting collection of business anthropology resources for you to explore.

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You Know You’re a Business Anthropologist If… (Funny)

You know you’re a business anthropologist if…

  1. You ask at least 500 follow-up questions when your supervisor gives you a project to really understand the full context. 
  2. You have a prepared spiel about how what you studied was different than digging up Mayan artifacts (unless that happened to be what you did).
  3. You constantly ask people how they feel when completing a task or what they think of the process.
  4. You try to reimagine and redesign any object or process that your organization will let you get your hands on.  
  5. You have critiqued every organization that has hired you.
  6. You have the strangest knick-knacks on your desk from around the world.
  7. You take triple the notes anyone else does in a meeting, recording in detail what everyone’s statements and body postures.
  8. In regular conversation, you interrogate your colleagues like you’re leading an interview.
  9. You frequent your company’s “watercooler spots” – informal places to gather to hang out. This is where the real work happens.
  10. You rage against top-down procedures and formal hierarchy every time you encounter it.
  11. You have resolved to never use PowerPoint for your presentations.
  12. Any time you hear a French word, your mind immediately goes to the French theorist with the most similar sounding name.

I intend this as a fun little exercise thinking about the quirks and idiosyncrasies of working as an anthropologist in the business world. 

Photo Credit: Toa Heftiba at https://unsplash.com/photos/FV3GConVSss

Three Situations When Ethnography Is Useful in a Professional Setting

This is a follow-up to my previous article, “What Is Ethnography,” outlining ways ethnography is useful in professional settings.

To recap, I defined ethnography as a research approach that seeks “to understand the lived experiences of a particular culture, setting, group, or other context by some combination of being with those in that context (also called participant-observation), interviewing or talking with them, and analyzing what is produced in that context.”

Ethnography is a powerful tool, developed by anthropologists and other social scientists over the course of several decades. Here are three types of situations in professional settings when I have found to use ethnography to be especially powerful:

1. To see the given product and/or people in action
2. When brainstorming about a design
3. To understand how people navigate complex, patchwork processes

Situation #1: To See the Given Product and/or People in Action

Ethnography allows you to witness people in action: using your product or service, engaging in the type of activity you are interested, or in whatever other situation you are interested in studying.

Many other social science research methods involve creating an artificial environment in which to observe how participants act or think in. Focus groups, for example, involve assembling potential customers or users into a room: forming a synthetic space to discuss the product or service in question, and in many experimental settings, researchers create a simulated environment to control for and analyze the variables or factors they are interested in.

Ethnography, on the other hand, centers around observing and understanding how people navigate real-world settings. Through it, you can get a sense for how people conduct the activity for which you are designing a product or service and/or how people actually use your product or service.

For example, if you want to understand how people use GPS apps to get around, one can see how people use the app “in the wild:” when rushing through heavy traffic to get to a meeting or while lost in the middle of who knows where. Instead of hearing their processed thoughts in a focus group setting or trying to simulate the environment, you can witness what the tumultuousness yourself and develop a sense for how to build a product that helps people in those exact situations.

Situation #2: When Brainstorming about a New Product Design

Ethnography is especially useful during the early stages of designing a product or service, or during a major redesign. Ethnography helps you scope out the needs of your potential customers and how they approach meeting said needs. Thus, it helps you determine how to build a product or service that addresses those needs in a way that would make sense for your users.

During such initial stages of product design, ethnography helps determine the questions you should be asking. Many have a tendency during these initial stages to construct designs based on their own perception of people’s needs and desires and miss what the customers’ or users’ do in fact need and desire. Through ethnography, you ground your strategy in the customers’ mindsets and experiences themselves.

The brainstorming stages of product development also require a lot of flexibility and adaptability: As one determines what the product or service should become, one must be open to multiple potential avenues. Ethnography is a powerful tool for navigating such ambiguity. It centers you on the users, their experiences and mindsets, and the context which they might use the product or service, providing tools to ask open-ended questions and to generate new and helpful ideas for what to build.

Situation #3: To Understand How People Navigate Complex, Patchwork Processes

At a past company, I analyzed how customer service representatives regularly used the various software systems when talking with customers. Over the years, the company had designed and bought various software programs, each to perform a set of functions and with unique abilities, limitations, and quirks. Overtime, this created a complex web of interlocking apps, databases, and interfaces, which customer service representatives had to navigate when performing their job of monitoring customer’s accounts. Other employees described the whole scene as the “Wild West:” each customer service representative had to create their own way to use these software systems while on the phone with a (in many cases disgruntled) customer.

Many companies end up building such patchwork systems – whether of software, of departments or teams, of physical infrastructure, or something else entirely – built by stacking several iterations of development overtime until, they become a hydra of complexity that employees must figure out how to navigate to get their work done.

Ethnography is a powerful tool for making sense of such processes. Instead of relying on official policies for how to conduct various actions and procedures, ethnography helps you understand and make sense of the unofficial and informal strategies people use to do what they need. Through this, you can get a sense for how the patchwork system really works. This is necessary for developing ways to improve or build open such patchwork processes.

In the customer service research project, my task was to develop strategies to improve the technology customer service representatives used as they talked with customers. Seeing how representatives used the software through ethnographic research helped me understand and focus the analysis on their day-to-day needs and struggles.

Conclusion

Ethnography is a powerful tool, and the business world and other professional settings have been increasingly realizing this (c.f. this and this ). I have provided three circumstances where I have personally found ethnography to be invaluable. Ethnography allows you to experience what is happening on the ground and through that to shape and inform the research questions we ask and recommendations or products we build for people in those contexts.

Photo credit #1: DariusSankowski at https://pixabay.com/photos/navigation-car-drive-road-gps-1048294/

Photo credit #2: AbsolutVision at https://unsplash.com/photos/82TpEld0_e4

Photo credit #3: Tony Wan at https://unsplash.com/photos/NSXmh14ccRU

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:

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Here is my full presentation:

Download [119.66 KB]

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