This is despite the fact that "black professionals are more enthusiastic about their careers than white professionals and are more likely to aspire to the best job." As a marketer, it's important to ask yourself how the data we tag and output to your website will affect your situation. Maybe for chickens and eggs – should the representation follow real workplace stats, or are workplace stats affected by the representation even in "harmless" places like stock photo websites? Even anecdotally, the latter has proven to be true. When talking to friends in the affairs department, what they say is that job interview candidates are likely to take that position when they see people who look like them in the company, leadership positions, and interview teams.
I also checked the degree. advertisement Continue ghost mannequin effect reading below Expression is always an issue on stock photo websites, so recently specialized photo sites have emerged that feature only BIPOC, people with disabilities, and people with larger bodies. Even now (late June 2020), if you search for "working women" on a popular stock photo site, the search on the first page will show over 100 images. Out of over 100 photos ... 10 Features BIPOC-Individuals appearing in the background or group. 11 As the main character, I focus only on BIPOC. The four images show the elderly in a professional workplace. 0 images show people with large bodies. 0 images show people with disabilities. This data is based on quick searches and rough counts, but indicates a lack of representation. As a marketer, it is important to use and request diverse and comprehensive images. If all the photos on your website are white high five, it's time for change.
Google data In her book "Algorithms of Oppression", Safiya Noble, Ph.D. Let's talk about how the people we trust to create unbiased algorithms and machine learning models for Google and other search engines are unbiased. advertisement Continue reading below In fact, a few years ago, there was news that a Google employee was fired after voicing anti-diversity rants (which received hundreds of votes in favor of other employees) and then gaining media attention. .. You need to ask yourself, "How many other people working on the algorithm share similar feelings of prejudice and anti-diversity?" But how does this lack of awareness of these known and unknown biases actually occur in searches