Published On: Fri, Oct 21st, 2022

Go read this special Nature issue on racism in science

[ad_1]

The journal Nature published a special issue on racism in science this week. In it, Black and Indigenous scientists, doctors, and researchers share how they’ve experienced racism in their work. Many of them experienced discrimination as they entered their chosen fields, then faced backlash as they called out wrongdoing in those same places. It takes bravery to share this kind of pain so publicly. Their stories are raw and revelatory.

This special issue of Nature came about as part of the journal’s own efforts to grapple with racism. After the killing of George Floyd at the hands of police in 2020, there was an upswell of voices calling out systemic racism in all areas of society, including in academia and science. Ahead of a “Strike for Black Lives” focused on STEM industries in June 2020, Nature wrote in an editorial that it “recognize[s] that Nature is one of the white institutions that is responsible for bias in research and scholarship,” the journal said at the time. It committed to creating a special issue of the journal “exploring systemic racism in research, research policy and publishing.”

Nature’s special issue this week includes five features from Black and Indigenous folks in science who have pushed for more inclusion and accountability in their fields of work — and have faced appalling racism in the process. Nadine Caron, Canada’s first Indigenous female general surgeon, describes her horrifying experience while applying for funding to advance genetic treatments for Indigenous children, when she was told on a conference call, “I don’t understand why you’re spending so much money and so much time applying for this grant when your people are killing themselves.”

It’s jarring to hear people’s lives dismissed so casually and cruelly

It’s jarring to hear people’s lives dismissed so casually and cruelly, even though it quickly brings to mind other discriminating remarks that have come to light recently — like the leaked audio that exposed racist statements Los Angeles City Council members made about Indigenous and Black people.

It all shows racism’s pollution is everywhere, even within environments that are supposed to be sterile — it festers in the research lab and in the data that’s used to create new technologies. Another article in the issue looks back on the impact of groundbreaking research that revealed racial and gender bias in facial recognition software. The 2018 study by Joy Buolamwini and Timnit Gebru found that facial recognition systems built by IBM, Microsoft, and Face++ had error rates of up to 34.7 percent for females with darker skin. That’s compared to an error rate of less than 1 percent for males with lighter skin.

The Verge reported on that study when it published and followed the waves it’s made since then. The researchers’ work pushed the companies to develop more accurate systems, which can be done by training the AI with data that includes a more diverse range of faces. A year after the paper was published, a follow-up audit found that Microsoft, IBM, and Face++ had reduced their error rates. But beyond that, the researchers also sparked deeper questions about how that facial recognition software would be used. “What good is it to develop facial analysis technology that is then weaponized?” Buolamwini told The Verge in 2018 as the field grappled with the technology’s potential to exacerbate police surveillance and racial profiling. By 2020, IBM announced that it would no longer develop facial recognition products.

That’s the power of telling stories like these — whether through research or narrative. Change comes with the ebb and flow of actions large and small. There are mass protests. And there are individuals navigating ivory towers and writing down what they find. Go read Nature’s special issue here.

[ad_2]

Source link

Leave a comment

XHTML: You can use these html tags: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>