Tagged with: “systemic discrimination”
The best bookmarks I saved in Week 12, 2019.
Written by Ciara Byrne on Fast Company.
“Personal data is used to deny low-income people access to resources or opportunities, but it’s also used to target them with predatory marketing for payday loans or even straight-up scams.”
“ Undocumented immigrants, day laborers, homeless people, and those with criminal convictions suffer from another data extreme: living beyond the reach of the data collection systems needed to thrive in society, they gain so much “privacy” that they become increasingly invisible. Living in this surveillance gap can be as damaging as living under constant surveillance, and is often a reaction to it.”
A new study finds a potential risk with self-driving cars: failure to detect dark-skinned pedestrians
Written by Sigal Samuel on Vox.
“The list of concerns about self-driving cars just got longer.
In addition to worrying about how safe they are, how they’d handle tricky moral trade-offs on the road, and how they might make traffic worse, we also need to worry about how they could harm people of color.
If you’re a person with dark skin, you may be more likely than your white friends to get hit by a self-driving car, according to a new study out of the Georgia Institute of Technology. That’s because automated vehicles may be better at detecting pedestrians with lighter skin tones.”
Written by Frank Pasquale on Real Life.
“The debate over the terms and goals of accountability must not stop at questions like “Is the data processing fairer if its error rate is the same for all races and genders?” We must consider broader questions, such as whether these tools should be developed and deployed at all.”
“The dispute over how to reform or restrict algorithms is rooted in a conflict over to whom algorithmic processes should be accountable. If it’s to a community of engineers and technocrats, then accountability will usually mean more comprehensive data collection to produce less biased algorithms. If it is accountability to the public at large, there are broader issues to consider, such as what limits should be placed on these tools’ use and commercialization, if they should even be developed at all.”
It’s all too quotable.
Frank Pasquale also recommends reading Safiya Umoja Noble and Virginia Eubanks:
“Scholars like Noble and Eubanks need to be at the center of future conversations about algorithmic accountability. They have exposed deep problems at the core of the political economy of information, in data-driven social control. They diversify the forms of expertise and authority that should be recognized in the development of better socio-technical systems. And they are not afraid to question the goals — and not simply the methods — of powerful firms and governments, foregrounding the question of to whom algorithmic systems are accountable.”