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Can science be value-free? The “gap” argument

Posted on May 7, 2019 by Chris ChoGlueck

If scientists are in the business of facts, is there still space for human values?  Like many other scientists-in-training, I used to think of the sciences as ideally free from societal values, such as environmentalism and feminism.  Sure, our ethical or political biases might guide what scientists study or how others use that knowledge.  But, during the “nitty gritty” of science—when researchers collect data, analyze statistics, and test hypotheses—they should not rely on the values of society.  Many people agree with this value-free ideal of science: public trust decreases in scientists who mention their own biases & values.

Many scientists, however, prefer a greater role of values in science and acknowledge that their objectivity does not require complete detachment from bias.  The history of science also provides countless examples of “good science” based on cultural values, including reprehensible ones.  For instance, Nazi scientists were the first to discover that smoking tobacco causes lung cancer, in part because of their horrid beliefs about “racial hygiene.”

If values and biases don’t necessarily result in “bad science,” then why do people hold the value-free ideal?  More importantly, is this ideal of science even possible?  Many philosophers of science, myself included, do not think so.  I’ll spend the rest of this post explaining one of the main lines of reasoning against the value-free ideal called the “gap” argument (or the argument from underdetermination).

Particularly in the nuts & bolts of scientific practice, scientists face challenging decisions about the logic of evidence.  By evidence, I don’t mean any old data or observations; rather, it is the specific data used to support a hypothesis or model.  Evidence is not a thing itself but the relationship between a hypothesis and those data collected to support it (figure 1).

This graphic has the structure of two circles connected by a bridge. The right circle has a line graph and is labeled "data,", which is conntect by a bridge labeled "evidence" with background knoweldge, to the left circle labeled "hypothesis" with a lightbulb inside.
Figure 1: Data (left) alone are not evidence for a hypothesis (right). The data must be shown to be relevant to the hypothesis and to support it. This justification requires additional background knowledge, represented by the bridge (middle) over the gap between data and hypothesis. This “bridge” supports the connection of the two, allowing one either to infer the hypothesis from the data or to predict the data from the hypothesis. (Image credit: Chris ChoGlueck.)

As any scientist can attest, turning data into evidence for a hypothesis is tricky business.  There are always many ways to test a hypothesis. For instance, what sort of data should count as evidence in support of it?  And how should that evidence be collected and described?  Furthermore, researchers can only collect so much evidence: how much evidence is enough to infer a given hypothesis?  When do you know that you have a sample that is representative?  These questions illustrate different sorts of uncertainties about evidence.  Here, scientists must make judgments and supply background knowledge to “bridge the gap” between data and hypothesis.

For instance, suppose we are interested in explaining a little girl’s illness.  She has skin rashes and spots inside her mouth, so we might hypothesize that she is infected with the measles virus.  But, why take these data as evidence for the hypothesis that she is infected?  We must supply additional background knowledge that measles is a virus that causes skin rashes and mouth spots.  This background knowledge about disease symptoms provides the additional logic to “make a bridge” supporting the connection between the data and the hypothesis (figure 2).

This graphic has the same structure as figure one. The data circle has an image of a little girl with red spots and rashes, while the hypothesis circle has a picture of a measles virus. The bidge between them ("evidence") is the background knoweldge that "measles is a virus that causes rashes and spots."
Figure 2: Why take rashes & spots to be evidence of a child’s measles infection?  Neither the rashes, nor the spots, nor the hypothesis (that she is infected) justifies this inference.  To “bridge the gap” that turns the data into evidence, we need to supply more background knowledge.  This example comes from Helen Longino.  (Image credit: Chris ChoGlueck.)

Now, when do judgments about evidence become an ethical or political value judgment?

Well, if neither data nor hypothesis is enough to make claims about evidence, then more background knowledge is necessary.  As philosopher Helen Longino has long argued, this “gap” allows for the recourse to human values, particularly when the theme of the research involves human interests and has social stakes.

One particularly salient example has been documented by IU Professor Elisabeth Lloyd from the Department of History & Philosophy of Science.  She has documented how evolutionary biologists relied on androcentric (male-focused) values in their research on female orgasm in humans & other primates.  Despite relevant data to the contrary, many biologists have simply assumed that female orgasm happens reliably during intercourse just like male orgasm.  In this way, they expect that the trait of orgasm would increase females’ evolutionary fitness in the same manner as with males (figure 3).

This graphic also has the same structure as figure one. The data circle has a 2 dimensional line graph. It is plotting the positive relationship between male fitness and male orgasm frequency during sex. The hypothesis circle has the same graph but for female fitness and orgasm. The bidge between them ("evidence") is the background knoweldge that "females are just like males."
Figure 3: The data (left) from human males show a positive relationship between evolutionary fitness and the frequency of orgasm during intercourse. This suggests that male orgasm has been selected by evolutionary forces. Many biologists have applied this data (left) about males to the case of female orgasm: they hypothesize that female orgasm likewise has been selected and predict that it would show the same positive relationship (right).  They justify this inference based on the androcentric assumption that male sexuality is just like female sexuality (middle). This example is simplified from Elisabeth Lloyd.  (Image credit: Chris ChoGlueck).

As a feminist and an empiricist, Lloyd criticized this explanation as based more on dominant androcentric values than on empirical data about females.  Although the female orgasm is in fact empirically different than the male orgasm (e.g., likelihood of orgasm with intercourse), biologists have relied on data about male sexuality to evidence this hypothesis.  In addition, their inference depends on the background assumption that male and female sexualities are essentially the same.  This sexist assumption went unquestioned until Lloyd criticized it. Following her critique, scientists later disconfirmed this hypothesis: frequency of female orgasm is not correlated with fitness.

Returning to our topic, doesn’t Lloyd’s critique support the value-free ideal?  Didn’t she show how ethical values like androcentrism & sexism ruin scientific reasoning?

On the contrary, her critique is rooted in her critical feminism and its ability to call out misuse of values & biases.  The problem here was an uncritical reliance on androcentrism.  Ethical and political value judgments inevitably enter much of the background knowledge on which scientists rely.  As historian Londa Schiebinger has shown, feminism has changed science for the better by helping us recognize and criticize the oversights that come from dominant perspectives in our culture.

Along these lines, Longino (who first formulated this “gap” argument) has suggested that a diversity of values improves the objectivity of a community by increasing its critical rigor.  The problem in our example was not the presence of androcentric bias.  Instead, it was that their ability to supplant relevant data when unnoticed and unquestioned by those with similar values.

While some philosophers think the value-free ideal should be defended or refined, others argue that it is time to look for more socially responsible ideals and guidelines.  I, for one, think it is important to find a replacement that better guides scientists toward ethical choices.  It is better for scientists to become more mindful of their value judgments and the political stakes of their science.  Accordingly, they are better suited to discern their responsibilities as scientists and to improve society with science.

For those of you still unconvinced that science cannot be value-free, I’ll make a suggestion: there is a more concrete line of argumentation (called the “error” argument) related to this more abstract one (the “gap”).  If you check out that link to my published article, you can read more about the methodology of risk assessment and the inescapable value judgments therein.

Edited by Lana Ruck, Abby Kimmitt, and Jennifer Sieben.

Further reading:

ChoGlueck, Christopher. “The Error Is in the Gap: Synthesizing Accounts for Societal Values in Science.” Philosophy of Science 85, no. 4 (2018). https://doi.org/10.1086/699191.

Lloyd, Elisabeth A. The Case of the Female Orgasm: Bias in the Science of Evolution. Cambridge, MA: Harvard University Press, 2005.

Longino, Helen E. Science as Social Knowledge. Princeton: Princeton University Press, 1990.

Schiebinger, Londa. Has Feminism Changed Science? Harvard University Press, 2001.

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Filed under: General Science, Scientific Methods and TechniquesTagged ethics in science, evolution, feminism, gender studies, history and philosophy of science, logic of evidence, philosophy of science, values in science

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