Inference to the best diagnosis: inside the black box of HIV antibody-testing

This paper analyzes three eras of United Kingdom (UK) official
guidance that since 1985 has governed laboratory testing for
antibodies to the human immunodeficiency virus (HIV). Using
data from expert governance of U.K. laboratory practice and
users understandings thereof, I analyze how this "publicly
hidden" or "black-boxed" process has dealt with the
interpretative flexibility of antibody testing by optimizing user
expertise in testing processes for purposes of inferring medical
diagnosis. I show that variables such as test sensitivity,
specificity and risk of exposure categories, which all impact on
the interpretive flexibility of the antibody-tests were historically
factored into test algorithms by regulatory expertise. This
enabled regulators to develop test algorithms using accrued
expertise that aimed to increase throughput targets, diminish
interpretive flexibility and optimize diagnostic accuracy by a
form of inference to the best diagnosis critiqued through user
interactional expertise. These algorithms are found to diminish
interpretive flexibility by a corroborative congruence between
heterogeneous forms of laboratory and clinical data mediated by
the concept of seroepidemiological risk. For STS, these findings
advance our understanding about the role of expert governance in
regulating laboratory practice and the limits of users’
interactional expertise.