To what extent has a lack of diversity influenced the development of the internet? How does this lack of diversity harm societies and individuals?

To what extent has a lack of diversity influenced the development of the internet? How does this lack of diversity harm societies and individuals?

 

Poor diversity efforts such as gender inequality in the tech industry, racism in media, and algorithm bias, have had a significant influence on the development of the internet, as the internet presents itself as an inclusive community, yet the individuals who govern the internet are not always inclusive thus the products produced cannot be accessible to everyone, which is harmful as the internet is not fair or accessible to everyone.

 

Gender inequality in tech industries

There is a vast lack of diversity in technology due to gender inequality, as there has been a poor representation of women in tech companies. An article by Forbes reports how “most tech companies are undergoing pressure to diversify their workforce as it is predominantly white, Asian, and male, and the public scrutiny has forced larger tech companies to reveal their employee information, which suggests minor progress (Marcus, Forbes, 2015). There is an obvious gender gap in tech industries, and it has often been assumed that the reason there is such a significant gap is that there are merely not as many females studying math and science, yet recent information “indicates that an equal number of girls and boys are participating in STEM electives” (Marcus, Forbes, 2015). Furthermore, a USA Today report “discloses that top universities graduate students who are people of colour, and studying computer science and computer engineering at twice the rate that leading technology companies hire them” (Weise & Guynn, USA Today, 2014). This further emphasises just how poor the diversity efforts of technology companies are as there are opportunities to hire women yet they do not, which harms societies and individuals as it can make females feel reduced and may make them feel as though they do not fit into the technology industry. Furthermore, the Forbes article suggests that “the reality is that gender and racial bias is so ubiquitous in the technology industry that it forces female and minority employees to leave” (Marcus, Forbes, 2015). Hence, the significant gender inequality in tech industries has influenced the development of the internet as it has limited its ability to grow, which is harmful as it has restricted females having the ability to govern technology and make a positive impact, thereby the internet is not accessible to everyone.

 

 

 

 

 

 

 

 

 

 

 

Racism in media

There has often been a lack of diverse representation within media, and media has used language and imagery to convey some sense of white superiority and the inferiority of other minority groups. Racism is evident in media particularly through news broadcasting, due to bias and unequal representation, and currently, some media outlets are facing backlash due to how they have covered racial issues, an example of this is the media coverage of the murder of Georg Floyd. Assisting managing director of Diversity and Community at Star Tribune, Kydell Harkness said that “perpetuating narratives of black men and black women to be viewed as dangerous, and the images that have been created of what black men are supposed to be, feeds into policing in America” (Harkness, 2021). A vast majority of the media and social media’s coverage of Georg Floyd’s murder was negative as some media focused on the fact that Floyd had a violent drug and criminal history, rather than focusing on the wrong of the White police officers. To create unity within the African American community media and news broadcast programs aim to have an increased number of African American broadcasters, and by doing this they are giving their community the information that they need to make well-defined and better changes. Another example of racism within media’s coverage of the news is the coverage of Russia’s invasion, as a video by The World is One News

, examined how much Western news coverage was blatantly racist, for example, one Western reporter said they are sad to see “Europeans with blue eyes and blonde hair” treated poorly. Western media is often part of the issue as they reflect the public mentality of the sense of European superiority. Within media, there has also been a significant lack of diversity in the music industry as minorities are often not well represented, and an example of this is when David Bowie in a 1983 interview with VJ Mark Goodman, criticised MTV for not playing music by Black artists.

An article by the BBC reports how racism in the music industry is upfront and mentions that “Little Mix band member Leigh-Anne Pinnock, said she was made to feel like the band’s “token black girl”; and that she often did not feel seen during public appearances” (BBC, Savage, 2021). This lack of diversity has impacted the development of the internet as it has trumped the perception of the internet to be a unified and harmonised community, as not all communities are equal.

 

 

Algorithm Bias

Algorithm Bias as defined by Florida State University Libraries is “systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others” (Florida State University Libraries, 2021). Algorithm bias is an example of an issue that can be extremely harmful to societies and individuals. Algorithms are everywhere, from powering internet search results to taking out the guesswork in online shopping experiences, and individuals use and trust them every day to make countless decisions for us. Although, despite its seemingly neutral mathematical nature, algorithms are not necessary anymore objective than humans as they are also written by people, and this is when algorithm bias occurs, as an article says “much of the discussion has centered on exclusion, algorithms and machine learning can also be used to target certain groups, for example, algorithms might identify certain ethnic groups as susceptible to abusive mortgage offers pitched in a certain way” (Silva & Kenney, pp. 13, 2018). There are numerous examples of this, including when Nikon cameras which were equipped with the blink detection feature would not take photos of many of its Asian users because the software thought their eyes were closed, seen in the image. Another example is Amazon’s Alexa which struggles to recognise different accents, and how google translates insistently associates certain jobs with certain genders as it translates sentences with pronouns such as “he is a surgeon”. This algorithm bias has had a significant influence on the development of the internet, as it shows the significant lack of diversity the internet poses today, which is harmful because it excludes certain groups and individuals. Algorithm bias is rooted in the way artificial intelligence algorithms work, by using ‘machine learning and ‘deep learning, which are both ways in which the internet makes decisions, and is dependent on data. MIT grad student Joy Buolamwini

presented an interesting TED talk in which she discussed the inequalities and bias of algorithm, as she experienced that the software did not detect her face, and this was because the people who coded the algorithm had not taught it to identify a broad range of skin tones and facial structures, she is now on a mission to fight bias in machines, which helps to ensure all groups and individuals are included, and a study mentions “algorithms can learn negative associations for certain social labels, especially when the data reflect a broad array of inputs” (Williams, Brooks, & Shmargad, pp. 89, 2018). Therefore, algorithm bias is a prevalent issue on the internet which sheds light on the lack of diversity in the community. 

 

Therefore, this lack of diversity has had a substantial influence on the development of the internet, as the internet is not what it presents itself to be, as it is not always an inclusive community, which is harmful as the internet is accessible to everyone.

 

References

 

Marcus, B. (2015). The Lack Of Diversity In Tech Is A Cultural Issue. https://www.forbes.com/sites/bonniemarcus/2015/08/12/the-lack-of-diversity-in-tech-is-a-cultural-issue/?sh=5dd408e179a2

 

Weise, E & Guynn, J. (2014). Black and Hispanic computer scientists have degrees from top universities, but don’t get hired in tech. https://www.usatoday.com/story/tech/2014/10/12/silicon-valley-diversity-tech-hiring-computer-science-graduates-african-american-hispanic/14684211/

 

WION. (2022, March 1). Gravitas: Western media’s racist reportage on Ukrainian refugees. [Video]. YouTube. https://www.youtube.com/watch?v=KBRwmTVVKQk.

 

MTV News. (2016, January 12). David Bowie Criticizes MTV for Not Playing Videos by Black Artists MTV News. [Video]. YouTube. https://www.youtube.com/watch?v=XZGiVzIr8Qg.

 

Savage, M. (2021). Racism in the music industry ‘is upfront and personal’. BBC News.

https://www.bbc.com/news/entertainment-arts-58884705

 

(2020). Rage and anguish: how the US papers have covered the George Floyd protests. The Guardian. https://www.theguardian.com/us-news/2020/jun/01/rage-and-anguish-how-the-us-papers-have-covered-the-george-floyd-protests

 

Hupfer, S. (2020). Closing the tech conference gender gap, Increasing women’s representation on the technology stage. Deloitte. https://www2.deloitte.com/us/en/insights/industry/technology/gender-gap-in-tech-industry.html

 

(2021). Algorithm Bias. Florida State University Libraries. https://guides.lib.fsu.edu/algorithm

 

TED. (2017, March 30^th). How I’m fighting bias in algorithms Joy Buolamwini. [Video]. YouTube. https://www.youtube.com/watch?v=UG_X_7g63rY

 

Williams, B. A., Brooks, C. F., & Shmargad, Y. (2018). How Algorithms Discriminate Based on Data They Lack: Challenges, Solutions, and Policy Implications. Journal of Information Policy, 8, 78–115. https://doi.org/10.5325/jinfopoli.8.2018.0078

 

Silva, S., & Kenney, M. (2018). Algorithms, Platforms, and Ethnic Bias: An Integrative Essay. Phylon (1960-), 55(1 & 2), 9–37. https://www.jstor.org/stable/26545017