Indulge in Learning
Image is not available

Unconscious Bias: How It Affects Us More Than We Know

When you type CEO, CFO OR CTO in your textbox on your iPhone, notice what comes up. It is an emoji of a man in a suit. Shocking, isn’t it, that this sexism exists? Even seemingly innocuous things as an emoji can reinforce these gendered stereotypes and demonstrate the structural sexism inherent in our society. However, most of us wouldn’t even notice these subtle stereotypes in these emojis unless we are looking for one that represents us. It is a vicious cycle. The hidden biases and stereotypes feed into such designs, and these images, in turn, propagate such biases.

Implicit Bias as a novel concept was first introduced in a paper of 2006, where it was introduced as “the new science of unconscious mental processes that has a substantial bearing on discrimination law”, and refuted the longstanding belief that humans are guided solely by explicit beliefs and by their conscious intentions.

Unconscious biases are everywhere. From the neighborhood that we choose, the close friends that we have, to the people we date. Developments in neuroscience now demonstrate that many biases are formed throughout life and held at the subconscious level, mainly through societal and parental conditioning. We gather millions of bits of information and our brain processes that information in a certain way –  unconsciously categorizing and formatting it into familiar patterns. Though most of us have difficulty accepting or acknowledging it, we all do it. Gender, ethnicity, disability, sexuality, body size, profession etc., all influence the assessments that we make of people and form the basis of our relationship with others, and the world at large.

All You Want Is to Be Believed': The Impacts of Unconscious Bias in Health  Care | Kaiser Health News
Article originally posted here

A google image search ‘thinks’ that more than 90% of professors are white men. Yes, there is a huge gender disparity with only about 25% of women on a professorial level, but this is not only statistically inaccurate, but it also demonstrates the inherent bias in the algorithm itself. A study has shown that an AI algorithm learned to associate women with images of a kitchen, learning from and reviewing more than 100,000 images from around the internet. As it carried out its learning, it was noticed that its biased assumptions became even stronger than that shown by the dataset, so in the end, the results were not merely replicating the inherent bias in the images that had been presented to it, but also amplifying it.

A Yale University study found that male and female scientists, both trained to be objective, were more likely to hire men, and consider them more competent than women, and pay them $4,000 more per year than women. Other research has shown that a science faculty rated male applicants for a laboratory manager position as significantly more competent and hireable than female applicants. Faculty also selected a higher starting salary and offered more career mentoring to the male applicant. When this was explicitly pointed out to them, the faculty members were often shocked, as they hadn’t realized their own internalized biases.