For this week’s blog post, I decided to write about Rebecca Hains’s article titled Dear fellow white people. In this online newspaper article, the author shares what white people should do when called racist. The author breaks down the concept into five steps. The first step talked about staying in the moment of the interaction. She talks about how many people reference their past to show how they are not racist instead of evaluating what they had done at the moment. The second step seemed to be mostly about racial biases. She states that we develop biases rooted in racism and that they are essential to acknowledge. The third step talked about asking for help and clarification. She spoke about how often finding someone who understands racism can help guide you on what you did wrong. Step four was to listen to the answers about racism you receive actively. The last step was to express gratitude and to grow from the experience.
An aspect of the article I wanted to build off of for this week was the concept of racial biases. I feel like racial biases are something more prevalent in our society than what people think. In the article, Rebecca cites how Trump has been seen to have racial biases when speaking about marginalized groups, while he claims to be an ally. To build off of this, I wanted to talk about how not only do people have implicit biases, but so does the technology used. More specifically, computer programs and AIs carry racial biases. In the article titled Rise of the racist robots – how AI is learning all our worst impulses, the author talks about how systems are programmed to be racist. The prominent example was how face recognition software labeled many black people as “gorillas” (Buranyi 2017). What is scary about these racist encounters is that it is the fault of programmers. There are plenty of examples where computer software has “learned from us” and developed racist patterns (Buranyi 2017). I think this is a crucial aspect of racial biases and is something society needs to come together to fix in our technology-focused world.
Buranyi, Stephen. “Rise of the Racist Robots – How AI Is Learning All Our Worst Impulses.” The Guardian, Guardian News and Media, 8 Aug. 2017, https://www.theguardian.com/inequality/2017/aug/08/rise-of-the-racist-robots-how-ai-is-learning-all-our-worst-impulses.
Hi Jacob! I think the idea of implicit biases are very interesting. I have had to take so many implicit biases tests for class and it's alway interesting to see the results. It's hard not to have a first reaction that is getting upset after these tests because they always show a bias, but I think that is something really important to understand. The narrative has recently shifted from being not racist to being anti-racist, and I think this is in part to take into account the implicit biases people have. Having the end goal of being anti-racist rather than simply not racist at all allows people to work with their implicit biases and actively try to be good people.
ReplyDeleteHey, Jacob! I enjoyed your summary of the Hains article, and especially how you connected racial biases with AI not working properly. I hadn't thought about it when reading the article, but it makes sense that artificial intelligences that learn how to function using some of the worst places do to so - the internet - would come out with some biases.
ReplyDeleteI'm not entirely sure how this kind of thing can be fixed, as I have the general opinion that the internet can be a horrible place for the most part, yet AI keeps coming out with these kinds of biases... I wonder if the technology is just not ready yet or smart enough to recognize these biases, or if the places the AI is learning from are just not healthy and should not be used as AI training. Fascinating insight!