Tag: Research

Applying Computational Ethnography and Statistics to Vapor Wave: Interview with Tanner Greene (Part 1 of 2)

For my next installment in my Interview Series, I interviewed Tanner Greene. He recently received his doctorate from the University of Virginia for his research on the digital music genre, vapor wave. He primarily used qualitative means but has also taught himself Python to be able to employ quantitative textual analysis into his project. It is a good example of how to integrate qualitative digital ethnographic techniques with quantitative natural language processing.

In this first part, he discusses why he decided to study the vapor wave community and his experiences learning Python to conduct statistical analysis with.

Here is Part 2 of our interview.

Tanner’s interests straddle math and the humanities, spanning digital cultures, user metadata, and a long-dormant statistics ability he wants to revive. In his spare time, Tanner enjoys writing about music, playing video games, and dreaming about learning SQL.

Resources We Referenced:

For more context on my interview series in general, click here.

Ethno-Data: Introduction to My Blog

            Hello, my name is Stephen Paff. I am a data scientist and an ethnographer. The goal of this blog is to explore the integration of data science and ethnography as an exciting and innovative way to understand people, whether consumers, users, fellow employees, or anyone else.

            I want to think publicly. Ideas worth having develop in conversation, and through this blog, I hope to present my integrative vision so that others can potentially use it to develop their own visions and in turn help shape mine.

Please Note: Because my blog straddles two technical areas, I will split my posts based on how in-depth they go into each technical expertise. Many posts I will write for a general audience. I will write some posts, though, for data scientists discussing technical matters within that field, and other posts will focus on technical topics withn ethnography for anthropologists and other ethnographers. At the top of each post, I will provide the following disclaimers:

Data Science Technical Level: None, Moderate, or Advanced
Ethnography Technical Level: None, Moderate, or Advanced

Integrating Ethnography and Data Science

As a data scientist and ethnographer, I have worked on many types of research projects. In professional and business settings, I am excited by the enormous growth in both data science and ethnography but have been frustrated by how, despite recent developments that make them more similar, their respective teams seem to be growing apart and competitively against each other.

Within academia, quantitative and qualitative research methods have developed historically as distinct and competing approaches as if one has to choose which direction to take when doing research: departments or individual researchers specialize in one or the other and fight over scarce research funding. One major justification for this division has been the perception that quantitative approaches tend to be prescriptive and top-down compared with qualitative approaches which tend to be to descriptive and bottom-up. That many professional research contexts have inherited this division is unfortunate.

Recent developments in data science draw parallels with qualitative research and if anything, could be a starting point for collaborative intermingling. What has developed as “traditional” statistics taught in introductory statistics courses is generally top-down, assuming that data follows a prescribed, ideal model and asking regimented questions based on that ideal model. Within the development of machine learning been a shift towards models uniquely tailored to the data and context in question, developed and refined iteratively.[i] These trends may show signs of breaking down the top-down nature of traditional statistics work.

If there was ever a time to integrate quantitative data science and qualitative ethnographic research, it is now. In the increasingly important “data economy,” understanding users/consumers is vital to developing strategic business practices. In the business world, both socially-oriented data scientists and ethnographers are experts in understanding users/consumers, but separating them into competing groups only prevents true synthesis of their insights. Integrating the two should not just include combining the respective research teams and their projects but also encouraging researchers to develop expertise in both instead of simply specializing in one or the other. New creative energy could burst forth when we no longer treat these as distinct methodologies or specialties.

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

Photo credit #1: Frank V at  https://unsplash.com/photos/IFLgWYlT2fI

Photo credit #2: Arif Wahid at https://unsplash.com/photos/y3FkHW1cyBE

Why Business Anthropologists Should Reconsider Machine Learning

high angle photo of robot
Photo by Alex Knight on Pexels.com

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.

Writing Ethnographic Findings as Software Specs

Photo Credit: Cuncon

When working as an ethnographer with software engineers, I have found formatting my write-up for any ethnographic inquiry I conducted as software specs incredibly valuable. In general, I prefer to incarnate any ethnographic report I make into the cultural context I am conducting the research for, and this is one example of how to do that.

Many find the academic essay prose style stifling and unintelligible, so why limit yourself to that format like most ethnographic write-ups tend to be when conducting work for and with other parties? Like Schaun Wheeler said in this interview, in the professional world, pdf reports are often where thoughts go to die.

Photo Credit: Mimi Thian

Most often when I am conducting ethnographic research with software engineers, I am doing some kind of user research on a potential or actual software product: trying to understand how users engage with a software or set of softwares to help engineers improve the design to better meet users’ needs. When doing this, I most often bullet my findings by topic and suggested change, ordering them based on importance and complexity. This allows software engineers to easily transfer the insights into actionable ideas for how to improve the software design.

For example, a software company asked me to conduct ethnography to understand how users engaged with a beta version of an app. For this project, I broke down ethnographic insights into advantages of the app and common pitfalls encountered. I illustrated each item on the list with stories and quotes from users. I ordered the points based on importance and difficulty addressing (aka as either important and easy to fix, not important but easy to fix, important but not easy to fix, and not important but not easy to fix). On each list, I focused on the item itself, but sometimes I might also mention potential solutions, particularly when users proposed specific ideas for how to resolve something they encountered. Only occasionally did I give my own suggestions. This allowed software engineers to think through the ethnographic findings and translate them into software specs. They liked the report formatting so much the CTO of the company came to me personally to tell me I had the most profound and useful documentation he had seen. 

I have found describing ethnographic findings as design specs has been incredibly helpful in the tech world. It allows the immersion of ethnographic insights into engineering contexts and facilitates the development of actional insights and designs. Instead of defaulting to a long essay or manuscript, ethnographers should think carefully about the best way to format their findings to make sure it is approachable, relatable, and useful for the audience(s) that will look at and use it.

Tech Anthropologist Working and Product Manager: Interview with Matt Artz (Part 1 of 3)

As part of my Season 2, I interviewed Matt Artz, a design anthropologist who has been recently working as a product manager in the tech space. In Part 1, he discussed his experiences making innovative software products as an anthropologist and product manager.

Matt Artz is a business and design anthropologist, consultant, author, speaker, and creator. He writes, speaks, and consults in user experience, product management, and business strategy. He creates products, podcasts, music, and visual art.

For more context on my interview series in general, click here.

Resources we mentioned or other additional resources:

  • My website – https://www.mattartz.me/
  • LinkedIn – https://www.linkedin.com/in/matt-artz-anthropology/
  • Anthropology in Business podcast – https://www.mattartz.me/podcasts/anthropology-in-business-with-matt-artz/
  • Anthro to UX podcast – http://anthropologytoux.com/
  • Venn Diagram – https://www.ideou.com/pages/design-thinking
  • Book – https://www.ideo.com/post/design-kit

Tech Anthropologist Working and Product Manager: Interview with Matt Artz (Part 2 of 3)

This is the second part of three in our conversation. In it, he described his work developing data science-based recommendation systems using the concepts of design anthropology, participatory research, and design thinking, and then how he uses his skills as an anthropologist to visualize and communicate results and then plan what to do going forward with stakeholders.

Matt Artz is a business and design anthropologist, consultant, author, speaker, and creator. He writes, speaks, and consults in user experience, product management, and business strategy. He creates products, podcasts, music, and visual art.

Please also see Part 1 of the interview.

For more context on my interview series in general, click here.

Resources we mentioned or other additional resources:

  • My website – https://www.mattartz.me/
  • LinkedIn – https://www.linkedin.com/in/matt-artz-anthropology/
  • Anthropology in Business podcast – https://www.mattartz.me/podcasts/anthropology-in-business-with-matt-artz/
  • Anthro to UX podcast – http://anthropologytoux.com/
  • Venn Diagram – https://www.ideou.com/pages/design-thinking
  • Book – https://www.ideo.com/post/design-kit

Tech Anthropologist Working and Product Manager: Interview with Matt Artz (Part 3 of 3)

This is the third and final part of three in our conversation. In Part 3, he discussed why he decided to study anthropology for his business work and how that helped give him the skills for the work he does today.

Matt Artz is a business and design anthropologist, consultant, author, speaker, and creator. He writes, speaks, and consults in user experience, product management, and business strategy. He creates products, podcasts, music, and visual art.

Previous Parts:

  1. Part 1
  2. Part 2

For more context on my interview series in general, click here.

Resources we mentioned or other additional resources:

  • My website – https://www.mattartz.me/
  • LinkedIn – https://www.linkedin.com/in/matt-artz-anthropology/
  • Anthropology in Business podcast – https://www.mattartz.me/podcasts/anthropology-in-business-with-matt-artz/
  • Anthro to UX podcast – http://anthropologytoux.com/
  • Venn Diagram – https://www.ideou.com/pages/design-thinking
  • Book – https://www.ideo.com/post/design-kit

The Promises and Failures of Current Artificial Intelligence Technology: An Interview with Gemma Clavell at Eticas (Part 1 of 3)

I spoke with Gemma Galdon-Clavell, founder of Eticas Foundation and Eticas Consulting about the social implications of artificial intelligence technologies. In this first part, we discussed the policy strategies for ensuring that our data and artificial intelligence systems built on our data are good quality, safe, and accountable.

Here are Part 2 and Part 3 of the interview.

Dr. Gemma Galdon-Clavell is a leading voice on technology ethics and algorithmic accountability. She is the founder and CEO of Eticas, where her multidisciplinary background in the social, ethical, and legal impact of data-intensive technology allows her and her team to design and implement practical solutions to data protection, ethics, explainability, and bias challenges in AI. She has conceived and architected the Algorithmic Audit Framework which now serves as the foundation for Eticas’s flagship product, the Algorithmic Audit.

To learn more about Gemma’s and Eticas’s work:

For more context on my interview series in general, click here.

The Promises and Failures of Current Artificial Intelligence Technology: An Interview with Gemma Clavell at Eticas (Part 2 of 3)

Here is the second part of three in my conversation with Gemma Clavell. We compared various corporate models – good and bad – for artificial intelligence and how to foster responsible corporate practices in this field.

Dr. Gemma Galdon-Clavell is a leading voice on technology ethics and algorithmic accountability. She is the founder and CEO of Eticas, where her multidisciplinary background in the social, ethical, and legal impact of data-intensive technology allows her and her team to design and implement practical solutions to data protection, ethics, explainability, and bias challenges in AI. She has conceived and architected the Algorithmic Audit Framework which now serves as the foundation for Eticas’s flagship product, the Algorithmic Audit.

Here is Part 1 and Part 3 of our interview.

To learn more about Gemma’s and Eticas’s work:

For more context on my interview series in general, click here.