Six ways machine learning threatens social justice

Machine learning is a powerful and imperfect tool that should not go unmonitored.

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  • When you harness the power and potential of machine learning, there are also some drastic downsides that you've got to manage.
  • Deploying machine learning, you face the risk that it be discriminatory, biased, inequitable, exploitative, or opaque.
  • In this article, I cover six ways that machine learning threatens social justice and reach an incisive conclusion: The remedy is to take on machine learning standardization as a form of social activism.
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Why science research still focuses mostly on males

In spite of a government mandate, females are often treated as afterthoughts in scientific research.

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  • A new study finds that though more females are included in experiments, sex-specific data often goes un-analyzed.
  • Only about a third of studies analyzed published participant breakdown by sex.
  • Some researchers say considering females more fully as research subjects is logistically too challenging.

In 2016, the National Institutes of Health (NIH) issued a directive that scientists receiving NIH funding must consider sex as a biological variable in pre-clinical research on vertebrate animals and human cells and tissues. According to a new study published in eLife that looked at over 700 journal articles, the number of women included as participants in pre-clinical research has jumped from 28 percent in 2009 to 49 percent in 2019. However, it's also unfortunately still the case that few studies actually consider sex as a biological influence that may potentially affect outcomes, and that data from women participants continues to be simply combined with data from men.

Study co-author Nicole C. Woitowich of Northwestern University's Feinberg School of Medicine tells INSIDE Higher Ed, "In the last 10 years, there has been a major in increase in sex inclusion, but it's still not where it's needs to be."

What's missing in current research

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Woitowich and others see two particularly problematic aspects to the continuing disregard of sex as a meaningful biological research variable.

First, female-specific data is rarely considered in study conclusions, despite the fact that it may have implications for women's health. According to L. Syd M Johnson of SUNY Update Medical University, who was not involved with the study, "This becomes highly problematic both scientifically and ethically, because women, children, and the elderly also need medical care, and they shouldn't be treated as if they have adult, male bodies. When they are excluded from research, and from the reported results, treatment for them becomes, effectively, off-label.

Second, Woitowich tells INSIDE Higher Ed it's, "troublesome to me as a scientist [that] a little under one-third [of studies] did not even report the number of males and females used as subjects." This makes it impossible for scientists to replicate the results. "If I don't have all the information," Woitowich says, "I'm left guessing."

On top of that, Woitowich laments that too much of the female-focused research that is undertaken is what's been called "bikini science," research surrounding issues related to female reproductive organs.

Why is this happening?

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"Many scientists, I don't even know if this is on their radar," says Woitowich. She proposes, therefore, that in the short term it may be the research gatekeepers — the funding entities, journal editors, and peer reviewers — who will have to step up and demand more inclusive science. She expresses surprise that they aren't already doing more to enforce the NIH's mandate. In the longer term, training for medical students should include a fuller awareness of the role that can be played by sex differences in research.

In a 2014 letter to the journal Nature, Janine A. Clayton and Francis S. Collins of the NIH admitted the problem even extends to female researchers. Noting that roughly half of the scientists doing NIH-funded research are women: "There has not been a corresponding revolution in experimental design and analyses in cell and animal research — despite multiple calls to action."

Another possible explanation

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There are some researchers who feel that a greater inclusion of women and their data in studies would unnecessarily complicate the problems inherent in designing research and getting it funded.

In a 2015 letter to the journal Science, a group of researchers wrote that sex considerations added an additional investigational layer to research, one that was often irrelevant to the purpose of a research project. They asserted that, "nonhypothesis-driven documentation of sex differences in basic laboratory research is more likely to introduce conceptual and empirical problems in research on sex and gender than bring new clarity to differences in men's and women's health outcomes."

The writers also suggested that sex may be less of a biological variable than gender and weight. If, for example, women are more likely to be taking multiple pharmaceuticals than men and tend to be lighter in weight, these factors may be more influential on experiment outcomes than sex. Reluctant to commit to considering sex as a variable, they suggested instead two generalized studies to determine if it should be, writing, "we see a stronger empirical basis for directed funding initiatives in two areas: scientific validation of preclinical models for studying human sex differences, and human studies of the interaction of sex- and gender-related variables in producing health outcomes that vary by sex."

Practicality

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A 2019 analysis by Harvard University's GenderSci Lab found that basic science researchers, "repeated again and again that their experiments were in large part constrained by practicalities of various sorts. These practicalities were often used to explain why they don't or can't account for sex in their research," says the lab's Annika Gompers. Among the practicalities noted were the acquisition of study materials such as cells from deceased patients, test animals, fat from cosmetic surgery patients, and so on. Gompers said researchers often simply work with what they can get.

She adds, "While my participants recognize that considering sex can be important for the generalizability of results, in practice it is often impractical if not impossible to incorporate sex as a variable into biomedical research. Such a finding is consistent with scholars who have long looked at science as practice and observed how practicalities — as mundane as the availability of materials — are often central to the reduction of complexity into 'doable problems.'"

As far as sample composition goes, the choice of subjects may have to do with researchers wanting to avoid the constraints and costs of the safety regulations that accompany studies of pregnant women, women of child-bearing age whom may become pregnant, children, and the elderly.

Finally, though it may be that having enough females in a sample to draw valid conclusions would likely require larger participant cohorts. Woitowich's co-author, Smith College's Anneliese Beery, says that fears of doubled sample sizes are overblown, asserting that such increases in participant numbers would be "not actually necessary."

Avoiding wasted research opportunities

One of the authors of that Science letter was Harvard's Sarah S. Richardson, who suggests a sort of middle path, though it does give researchers license to ignore the NIH requirement as they see fit. Richardson proposes something she calls "sex contextualism," which is the "simple view that the definition of sex and sex-related variables, and whether they are relevant in biological research, depends on the research context."

Science journalist Angela Saini agrees , saying, "While it's valuable to include a broad spectrum of people in studies, it doesn't necessarily follow that the sex differences will be significant or important. So disaggregating for sex, while useful sometimes, doesn't always matter."

The above points, however, don't seem to acknowledge the potential for findings important specifically to female health, and seem more concerned with protecting the efficacy of studies that benefit males.

In any event, Woitowich finds that things are progressing more slowly than the NIH and others may have hoped. While Beery says it's "exciting to see increased inclusion of female subjects across so many different fields of biology," there are potentially meaningful scientific insights being lost. The disinclination toward fully collecting and analyzing female data for research experiments "means we are still missing out on the opportunity to understand when there are sex differences and losing statistical power when sex differences go unnoticed."

Exposing our hidden biases curbs their influence, new research suggests

Do you know the implicit biases you have? Here are some ways to find them out.

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  • A study finds that even becoming aware of your own implicit bias can help you overcome it.
  • We all have biases. Some of them are helpful — others not so much.
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Diversity, stereotyping, success: Why being different at work is risky business

Stereotyping isn't about "bad people doing bad things." It's about our subconscious biases, and how they sneak into organizational structures.

Psychologist Valerie Purdie Greenaway is the first African American to be tenured in the sciences at Columbia University, in its entire 263 year history. Despite her celebrated position—and, in fact, perhaps because of it—she still struggles with perception, subtle stereotyping, and the enormous stakes of being one of few women of color in a leadership role. Here, Valerie Purdie Greenaway speaks with diversity and inclusion expert Jennifer Brown about being "the only" in a workplace, whether that is along lines of gender, race, culture, or sexual orientation, and how organizations and individuals can do more to recognize and address their biases. That also means letting go of the idea that stereotyping is a malevolent case of "bad people doing bad things." What does discrimination really look like day to day? Most of it is subconscious, subtle, and is deeply embedded into the structure of organizations, which can have an impact on performance, mentorship, and staff turnover. Do you recognize any of your own behavior in this discussion? This live conversation was part of a recent New York panel on diversity, inclusion, and collaboration at work.

A.I. will serve humans—but only about 1% of them

AI is leaving human needs and democracy behind in its race to accomplish its current profit-generating goals.

It doesn't have to be this way, but for now it is: AI's primary purpose is to maximize profits. For all of the predictions of its benefits to society, right now, that's just window-dressing—a pie-in-the-sky vision of a world we don't actually inhabit. While some like Elon Musk issue dire warnings against finding ourselves beneath the silicon thumbs of robot overlords, the fact is we're already under threat. As long as AI is dedicated to economic goals and not societal concerns, its tunnel vision is a problem. And as so often seems to be the case these days, the benefits will go to the already wealthy and powerful.

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