Tag: Machine Learning

Conversing with AI: Interview with Chelsea Wang about Communications with Artificial Intelligence Systems (Part 3 of 3)

In the final part of our conversation, Chelsea Wang explains how her background in psychology has influenced her work in artificial intelligence. In particular, she describes how her social science background helped her develop and deploy her own version of the Mutual Theory of Mind as a psychologist within the field of artificial intelligence. When socializing, humans employ a recursive feedback loop of conceptualization of each other, and she explores the application of similar concepts to conversational AI systems.

She concludes by discussing her journey as a PhD student: what led her to seek her dissertation and her plans afterwards to use what she is learning now to conduct innovative and impactful work in the business world.

Click here to learn more about the Interview Series.

More about Chelsea:

Qiaosi Wang (Chelsea) is a fifth-year PhD candidate in Human-Centered Computing at Georgia Institute of Technology. Chelsea is a human-centered AI researcher and her PhD dissertation work focuses on building the Mutual Theory of Mind framework, inspired by the basic human capability to surmise what is happening in others’ minds (also known as “Theory of Mind”), to enhance mutual understanding between humans and AIs during human-AI communication. Her work specifically focuses on the human-AI communication process during AI-mediated social interaction in online learning, where AI agents can connect socially isolated online learners by providing personalized social recommendations to online learners based on information extracted from students’ posts on the online class discussion forums.

Chelsea received her Bachelor of Science degrees in Psychology and Informatics from the University of Washington, Seattle. In her free time, Chelsea loves hiking, playing with her cat, Gouda, and spending time at bouldering gyms. 

To learn more about Chelsea and the sources we referenced in our conversation:

Conversing with AI: Interview with Chelsea Wang about Communications with Artificial Intelligence Systems (Part 2 of 3)

Chelsea Wang has spent many years trying to improve the cognitive process of artificial intelligence systems to better interact with humans. In this second part of our conversation, she explains her theories about metacognition, intelligence, and potential anthropomorphization of AI “thought” processes. Through this, she explicates her vision and approach to the potential social life of AI.

Click here to learn more about the Interview Series.

More about Chelsea:

Qiaosi Wang (Chelsea) is a fifth-year PhD candidate in Human-Centered Computing at Georgia Institute of Technology. Chelsea is a human-centered AI researcher and her PhD dissertation work focuses on building the Mutual Theory of Mind framework, inspired by the basic human capability to surmise what is happening in others’ minds (also known as “Theory of Mind”), to enhance mutual understanding between humans and AIs during human-AI communication. Her work specifically focuses on the human-AI communication process during AI-mediated social interaction in online learning, where AI agents can connect socially isolated online learners by providing personalized social recommendations to online learners based on information extracted from students’ posts on the online class discussion forums.

Chelsea received her Bachelor of Science degrees in Psychology and Informatics from the University of Washington, Seattle. In her free time, Chelsea loves hiking, playing with her cat, Gouda, and spending time at bouldering gyms. 

To learn more about Chelsea and the sources we referenced in our conversation:

Conversing with AI: Interview with Chelsea Wang about Communications with Artificial Intelligence Systems (Part 1 of 3)

Chelsea Wang describes her work developing and refining the communication processes between artificial intelligence and humans, particularly the Mutual Theory of Mind framework she has helped build. As a doctoral student in Human-Computer Interaction, she also discusses her journey from human psychology to the social interactions of AI.

Click here to learn more about the Interview Series.

More about Chelsea:

Qiaosi Wang (Chelsea) is a fifth-year PhD candidate in Human-Centered Computing at Georgia Institute of Technology. Chelsea is a human-centered AI researcher and her PhD dissertation work focuses on building the Mutual Theory of Mind framework, inspired by the basic human capability to surmise what is happening in others’ minds (also known as “Theory of Mind”), to enhance mutual understanding between humans and AIs during human-AI communication. Her work specifically focuses on the human-AI communication process during AI-mediated social interaction in online learning, where AI agents can connect socially isolated online learners by providing personalized social recommendations to online learners based on information extracted from students’ posts on the online class discussion forums.

Chelsea received her Bachelor of Science degrees in Psychology and Informatics from the University of Washington, Seattle. In her free time, Chelsea loves hiking, playing with her cat, Gouda, and spending time at bouldering gyms. 

To learn more about Chelsea and the sources we referenced in our conversation:

Designing Machine Learning Products Anthropologically: Building Relatable Machine Learning

Loader Loading…
EAD Logo Taking too long?

Reload Reload document
| Open Open in new tab

Download [2.38 MB]

How do we build relatable machine learning models that regular people can understand? This is a presentation about how design principles apply to the development of machine learning systems. Too often in data science, machine learning software is not built with regular people who will interact with it in mind.

I argue that in order to make machine learning software relatable, we need to use design thinking to intentionally build in mechanisms for users to form their own mental models of how the machine learning software works. Failing to include theses helps cultivate the common sense that machine learning is a black box for users.

I gave three different versions of this talk at Quant UX Con on June 8th, 2022, the Royal Institute of Anthropology’s annual conference on June 10th, 2022, and Google’s AI + Design Tooling Research Symposium on August 5th, 2022.

I hope you find it interesting and feel free to share any thoughts you might have.

Thank you for the conference and talk organizers for making this happen, and I appreciate all the insightful conversations I had about the role of design thinking in building relatable machine learning.

Data Science and Game Design: Conversation with Clayton Sisson (Part 3 of 3)

During the final part of our conversation, Clayton discusses his journey from game design to data science, including what inspired them to study data science and what it has been like learning and working in this new field. Clayton Sisson is a game designer and aspiring data scientist, passionate about how data science can shed light on human behavior.

This is the next installment in my Interview Series. During Over the course of the three parts of our conversation, we discuss how game design thinking can help develop usable and useful machine learning products within data science.

Here is Part 1 and Part 2 of our interview.

Resources:

Data Science and Game Design: Conversation with Clayton Sisson (Part 2 of 3)

In Part 2, we discuss how to apply the design concept shikake to machine learning systems. Clayton Sisson is a game designer and aspiring data scientist, passionate about how data science can shed light on human behavior.

This is the next installment in my Interview Series. During Over the course of the three parts of our conversation, we discuss how game design thinking can help develop usable and useful machine learning products within data science.

Here is Part 1 and Part 3 of our interview.

Resources:

Data Science and Game Design: Conversation with Clayton Sisson (Part 1 of 3)

Clayton Sisson is a game designer and aspiring data scientist, passionate about how data science can shed light on human behavior. For the next installment of my Interview Series, we discuss ways to use game design and UX design to develop usable and useful machine learning products and their experiences transitioning from design into data science. In this first part, we discuss the connections between data science and game design.

Here is Part 2 and Part 3 of our interview.

Resources:

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.