A Note on AI-Inflected Ethnography

The idea of an AI-Inflected ethnography emerged at this time. It came together for us as a way to bring qualitative analysis to silences and to the downstream impact of algorithm development at the moment of conception and problem formation.

We believe an AI-Inflected ethnography has a potential to focus on the silences, intentionalities, gaps, aspirations and conceptions that may be in one moment accepted and in another moment forgotten or rendered invisible. An AI-Inflected ethnography we suggest, is not about what has been built but what has not yet been built and the reasoning or emotions that go into and are fought over to arrive at the product or algorithmic problem worthy of engineering time and worthy of gaining sometimes expensive data resources.

When we think about AI-Inflected ethnography we should not conjure pictures of methodological reasoning. Perhaps such reasoning will come over time and over further case studies but here we are marking active algorithm development today and its implications on people in a start-up that struggled and often failed to inhabit different points of view. An “inflected” ethnography focused on AI development does not offer up suggestions for research tools, recording procedures or discussion on synchronous (real-time) or asynchronous (non-real-time) of research data capture and technique in the field. Instead we have chosen to examine dimensions of problem formation in data science and epistemic splits in certain forms of expertise that have had practical implications and anxieties within an early stage machine learning start-up.

This type of analysis we believe is best located in moments when we can tease out the collaborative opportunities and imagination in algorithm development in an everyday when diversity of thought may be up for grabs and when algorithm problem formation may be held open. This form of analysis is unstable and in some ways an outcome of role murkiness between data scientist and ethnographer. But such analytical instability comes as a benefit. It is at these formative moments when certain epistemic events can be made visible through gaps in idea generation.

One of the strengths of AI-Inflected ethnography is paying close attention not only to the content of knowledge, but also to the processes of knowledge development focused on shifting edges of algorithm R&D. These edges of organizational knowledge and practice are not simply learned, known and then applied ideas but are locally contested and puzzled through. Deciphering epistemic borders suggests that everyone in an early stage start-up is engaged on one level or another with processes of the formation, co-creation and development of algorithmic knowledge and outcome even when these outcomes are very uncertain and distant. The process of building algorithms paradoxically engages and silences organizational members. The epistemological glue that represents machine learning knowledge does not draw together all forms of knowledge as domain knowledge or as a binary of usefulness/non-usefulness. Instead this glue provides a kind of organizational coherence as much as it limits the flow of forms of knowledge and fashions types of algorithms and types of organizations. These inflection points in AI development are tensions and realities not to fend off and avoid but to identify and transform.

It is important to keep in mind that what characterizes an AI-Inflected ethnography is not consultative attendings but an organizational embeddedness of an ethnographer/researcher that helps capture gaps and slippages within an organizational environment operating as a kind of disjointed body pulling together its internal fortitude to take on and build algorithms that can be integrated into software and into people’s lives. The odds of early stage start-up success are daunting, the odds of their failure are well over 90%.

Could these odds be improved upon with a true T-shaped organization and a true diversity of perspective?

Where Do We Go From Here? – What we gained access to was a deeper image of an organizational group of people trying to genuinely integrate a patient’s ‘thank you’ letter into their thinking but unable to seize upon its message of gratitude and transform this message into actionable algorithm development. We gained access to the potential consequences of radiologist’s insight as consultative input and domain knowledge. We gained a sense that the generalizing and particularizing aspects of machine learning were open and unsettled. There were forms of knowledge and emotional life that algorithm development resisted. We believe there may have been other forms of knowledge and diversity of knowledge that has not yet been organizationally imagined to produce very different algorithms for very different outcomes. We do not yet know how these forms will appear in the future but we can as researchers prepare for their emergence by laying the groundwork for the conceptualizing of more malleable and inclusive algorithms.

One of the most disembodied technologies in development today requires embodied and embedded research and engagement. AI calls for us as researchers to not only step back into the social but to step into the daily grind of AI’s silences and epistemic threads that are constantly being shredded and mended to transform organizational coherence.

If we use algorithm development to screen out the pain and suffering of vulnerable people like patients, instead of finding ways to integrate their journeys, then we may also find ever-narrowing algorithm development populating or even taking over our everyday lives.

It is an extensive kind of labor to evaluate, question, chart, chronicle how a start-up and AI development together forge forms of thinking and acting across algorithm, people, product and clients. It seems to be a worthy endeavor. However, if this kind of research needs to get done who is going to do it? Who will listen and make use of it? What kind of organization will it produce? Most importantly, what kind of commitment will it take to produce it?

Rodney Sappington Ph.D. is a data scientist and ethnographer who leads machine learning strategy and product development. His research focuses on the intersections of knowledge, human-machine relations and behavior, and societal outcomes from algorithm development. Rodney is CEO and Founder of Acesio Inc. rodney@acesio.org

Laima Serksnyte Ph.D. has 15+ years of experience in advanced research, consulting and coaching in the areas of executive leadership and organizational psychodynamics. Her research focuses on psycho-social, behavioral and societal levels of analysis in areas of AI development, healthcare, education and organizational member advancement. Laima is Head of Behavioral and Organizational Research at Acesio Inc. laima@acesio.org