Dawn: … While both areas have a core set of expectations, they both have to extend beyond their core in order to deal with data about social life—data which has very real social consequences.
TYE: This is all the more true in industry contexts, where we often have to make social decisions, or design decisions, regardless of expertise.
DAWN: One difference is that in many data science scenarios, the available data has already been collected, whereas most ethnographic projects include field research time to gather new data.
TYE: Although this tendency doesn’t hold true all the time, it is a common expectation, and that expectation results in a divergent initial perspective on projects: data scientists often think about working within the available datasets while ethnographers tend to begin their projects by thinking expansively about what dataset could be created, or should be created given the state of the art of the relevant discipline (anthropology, sociology and so forth). This difference in perspectives leads to different attribution models for the results. Data scientists will often describe their results as derived from the data (even if the derivation is complex and practically impossible to trace). Data scientists will readily recognize that they made decisions throughout the project that impacted the results, but will often characterize these decisions as being determined by the data (or by common and proven analyses of the data). You have a totally different way of dealing with that.
DAWN: Yes, for sure. It’s all coming from “the data†but ethnographers themselves are a part of the data. A crucial part. If you were an active part of its creation—if you were there, having conversations with people, looking them in the eye as they try to make sense of your presence—you just can’t see it any other way. It’s unavoidable. You’re also aware of all of the other contingent factors involved in the data you collected in that moment. So we have to be explicitly reflective and critical of how our social position influenced the results.
TYE: This is all the more true in industry contexts, where we often have to make social decisions, or design decisions, regardless of expertise.
DAWN: One difference is that in many data science scenarios, the available data has already been collected, whereas most ethnographic projects include field research time to gather new data.
TYE: Although this tendency doesn’t hold true all the time, it is a common expectation, and that expectation results in a divergent initial perspective on projects: data scientists often think about working within the available datasets while ethnographers tend to begin their projects by thinking expansively about what dataset could be created, or should be created given the state of the art of the relevant discipline (anthropology, sociology and so forth). This difference in perspectives leads to different attribution models for the results. Data scientists will often describe their results as derived from the data (even if the derivation is complex and practically impossible to trace). Data scientists will readily recognize that they made decisions throughout the project that impacted the results, but will often characterize these decisions as being determined by the data (or by common and proven analyses of the data). You have a totally different way of dealing with that.
DAWN: Yes, for sure. It’s all coming from “the data†but ethnographers themselves are a part of the data. A crucial part. If you were an active part of its creation—if you were there, having conversations with people, looking them in the eye as they try to make sense of your presence—you just can’t see it any other way. It’s unavoidable. You’re also aware of all of the other contingent factors involved in the data you collected in that moment. So we have to be explicitly reflective and critical of how our social position influenced the results.