Scientiﬁc Argument with Supervised Learning
Abstract
The use of machine learning (ML) for scientiﬁc discovery has enabled data-driven approaches to new and old questions alike.
We argue that scientiﬁc arguments based on algorithms for discovery hold the potential to reinforce existing assumptions about phenomena, under the guise of testing them.
Using examples from imagebased biological classiﬁcation, we show how scientiﬁc arguments using supervised learning can contribute to unintended, unrealistic, or under-evidenced claims.
ML for scientiﬁc discovery Life sciences often advance through creating and testing taxonomies or classiﬁcations of entities.
Statistical methods to evaluate the theorized differences between groups offer the opportunity to validate such classiﬁcations.
From machine learning, the introduction of large-scale, data-driven efforts to test and redeﬁne biological classiﬁcations has led, for instance, to innovations in population genetics [1, 2], microbiology [3, 4], and pharmacology [5].
Scientists have long looked to the brain and physical body as the source of differences between people, focusing for example on how brain imaging could be used to differentiate men and women, trans and cisgender people, autistic and non-autistic people, criminals and non-criminals, and other social divisions [6, 7, 8].
Similarly, a raft of recent work from evolutionary psychology, criminology, and computer vision has looked to establish how photographs of faces can be used to measure the biological basis of “honesty, personality, intelligence, sexual orientation, political orientation, and violent tendencies” [9, 10, 11, 12, 13, , resurrecting questions from a branch of eugenics known as physiognomy [15, ; some of this work even explicitly endorses the discredited 19 th century phrenologist Cesare Lombroso [17].
The recent availability of large, high-dimensional data sets such as face photographs and neuroimaging has enticed ML research to these questions [15]
For scientiﬁc ﬁelds focused on differences, supervised classiﬁcation offers an appealing set of tools.
When applied scientists believe that groups are “truly” categorically different, classiﬁcation mirrors this assumption [18, 19].
For instance, to establish group differences using MRI data, many scientists have turned to classiﬁcation.
As one author explains, “If a binary classiﬁer has good performance, then clearly the groups [men’s and women’s brains] have restricted overlap... a classiﬁer can only achieve perfect classiﬁcation if the data points are well separated (note... the data may be well separated, even if a particular classiﬁer is no better than random guessing)”
In other words, this author—and those of 48 other papers [6]—argue that classiﬁers give evidence to his claim that men and women have categorically different brains by showing that it is possible to assign the correct sex label to a person using brain MRI data.
But as the quote indicates, supervised learning strategies cannot challenge or suggest alternatives to scientists’ theories of group difference.
Moreover, this approach to ML for discovery risks “machinic neoplatonism” [21], i.e., an overconﬁdence that the model represents real, fundamental truths of the world simply because the algorithmic approach to the problem is appealing— potentially despite poor empirical evidence, high error rates, and limited validity [6, 16, 21, 22].
Measurement in ML for scientiﬁc discovery An emphasis on prediction without an emphasis on measurement can seem to legitimize concepts that would not otherwise hold up to scrutiny [23, 24, 25].
Gelman and colleagues illustrate this in their critique of an algorithm predicting sexual orientation from face photographs: “stripping a phenomenon of its social context...
give[s] the feel of scientiﬁc objectivity while creating serious problems for generalizing ﬁndings to the world outside the lab or algorithm”, where sexual orientation is neither a boolean category nor a ﬁxed, socially-unmediated biological phenomenon [26].
Beyond scientiﬁc insight, supervised learning for discovery carries larger societal risks and ethical challenges researchers should be aware of.
For example, the neuroscientiﬁc question “do men and women have categorically different brains?”
is still hotly contested among researchers [27, 28, 29, 30] and highly politicized in society as a means of excluding women from STEM jobs, denying trans people rights, and more [27, 28].
The argument about brain differences by sex was itself a central premise of the eugenics movement that compared women’s brains to “gorillas... children and savages” in the 1870s [29, 31].
ML and even brain researchers are often unaware of the eugenic history and implications of claims like this, or related claims such as that we can infer personality, sexuality, or criminality from facial structure [15, 26, 32].
Nevertheless, building technology that inaccurately supposes an essential, categorical difference between human groups risks contributing to discriminatory, polarizing, and eugenic beliefs [33].
Using classiﬁcation to identify biases Even researchers trying to oppose racism often inadvertently reinforce racist, eugenic beliefs with supervised classiﬁcation.
Benjamin [34] offers numerous examples; more recently, a preprint [35] received widespread attention for showing that neural networks could classify race in medical imaging data such as hand X-rays.
The authors publicized the paper to warn against racial bias in algorithms, and yet within a day the discussion of their ﬁndings was “swamped by racists” using it as proof of biological race differences [36].
This is why, we argue, it is not enough to involve ML and medical experts in such a project.
Experts in the scientiﬁc and technological construction of race and other human identities are indispensable for designing ML to advance knowledge without falling into centuries-old traps.
Scientiﬁc claims with unsupervised learning In cases where the fundamental nature of groups is in question, or where historical harms have come from essentializing groups, unsupervised learning offers a variety of promising techniques to advance research while avoiding the risks we outlined.
Unsupervised clustering approaches can offer a data-driven alternative to the biases and social baggage of human categories.
They can also fulﬁll a promise of ML for discovery: to uncover patterns in data that we would not have expected, labeled, or sought out [37].
Clustering can also offer a better test of group difference hypotheses in neuroscience: Joel et al.
[38] test the hypothesis that human brains come in two fundamental types, male and female, by ﬁrst clustering brain imaging data and then comparing the clusters to participant sex.
Unlike the classiﬁcation approach [20], this approach could either support the hypothesis by ﬁnding that brains are best described by sex-segregated clusters, or reject it by ﬁnding otherwise.
The authors ﬁnd evidence to reject the hypothesis [38].
Other promising approaches turn to anomaly detection to evaluate categorical differences.
Joel et al.
[38] argue that if men and women’s brains are as categorically different as some claim, then a model trained on women’s brains would ﬂag men’s brains as anomalous and vice versa.
This setup could produce evidence both for and against the hypothesis of categorical difference, and again the authors ﬁnd evidence against it.
These and related ﬁndings [19, 27, 29, 30] showing that sex is not a binary aspect of brains—and that furthering these constructions contributes to fairness-related harms—are of critical importance for ML researchers working with neuroimaging data, which at present uses sex classiﬁcation as a ‘gold standard’ benchmark for evaluating new algorithms [6, 39, 40, 41].
Scientiﬁc claims with interpretative supervised learning The same interpretive approach that makes unsupervised learning useful for scientiﬁc discovery can be adapted for supervised learning.
One such approach is illustrated by Sanchis-Segura et al.
[30]: rather than uncritically reproducing group difference by asking if they can ﬁnd differences, the authors show how researcher assumptions and data preparation choices inﬂuence ﬁndings about brains and sex.
This work falls within a larger conversation about the implications of assumptions and data preparation and the emphasis on prediction in lieu of validation in algorithms for discovery [6, 22, 24, 42, 43].
(A related conversation looks to the harms made during the construction and labeling of data sets [44, 45, 46].)
Other work compares performance across different model speciﬁcations, examining both what number of classes best models sex in brains and which (possibly inconsistent) features are important across models [19].
Broadly, instead of assuming a model is ‘correct’ and risking machinic neoplatonism—and its concomitant harms [6, 16, 21]— interpretative approaches learn from a diverse set of perspectives on a problem embodied in a diverse set of models [25, 37].