TO WHAT EXTENT HAS A LACK OF DIVERSITY INFLUENCED THE DEVELOPMENT OF THE INTERNET? HOW DOES THIS LACK OF DIVERSITY HARM SOCIETIES AND INDIVIDUALS?
The development of the internet has indeed been influenced by a lack of diversity. This lack of diversity can be found within the technology industry itself, and through the introduction of coding and algorithms. While this is seen as advancement, these codes and algorithms have begun to negatively impact individuals and societies. The internet was a revolutionary development intended to bring cultures and societies together. Ever advancing, developing and growing; the internet, through a lack of diversity, has developed a glitch, where Artificial Intelligence, while largely useful, has revealed a number a flaws and issues. The process of developing codes and algorithms will need to be examined and the flaws inspected and repaired in order to protect its users and provide a more balanced, inclusive future of the internet. This essay will discuss this lack of diversity has influenced the development of the internet and the extent of its damage to individuals and societies.
The Internet was a revolutionary development in the communications world. It transcended distance, created opportunities for collaboration, sharing, and access to information (Leiner et al., 2009). This technical idea of mass collaboration was a key part of the Internet’s development, however, in its beginning stages, the only people who had their hands on the making were a few scientific communities, which at the time, were predominantly white men. This has inevitably led to very deeply embedded issues and in turn become the source of the damage, separation and exclusion of some individuals and societies. Moreover, as Leiner describes; “starting in the early 1980’s and continuing to this day, the Internet grew beyond its primarily researched roots to include both a broad user community and increased commercial activity.” (Leiner et al., 2009). Because the internet consisted of thousands of hyperlinks, developers needed to find a way to filter this information. However, algorithms take a long time, and a large amount of data to become accurate, and its inaccuracy have caused algorithmic biases, which have created societal, gender and racial issues; For example, biases in online ads were found by Harvard researcher, Latanya Sweeny. She discovered that searches for African-American names were likely to be followed by ads from services that render arrest records, compared to white male names.
According to Adrian Johansen (2022) the issue lies within the lack of diversity found in technology based workforces. Johansen states that “The United States Equal Employment More than 83% of tech executives and managers are white and just over 20% are women. In addition, 92% are heterosexual. In fact, white and male employees fill the majority of all tech positions.” (Johansen, 2022). This predominantly homogenous workforce, which lacks racial, cultural and gender diversity, is susceptible to creating coding biases, some of which are done subconsciously; and while there might not be bad intentions, it is evident that underrepresented individuals and societies can be negatively impacted.
Adrian Johansen’s (2022) view recognises that the internet should draw from a multicultural, diverse and fully representative network which combines information, beliefs and cultures rather than accepting the ideas and developments from a largely homogeneous and underexposed industry. An example of this bias is found in US Healthcare. In October 2019, it was found that an algorithm used in US hospitals to predict patients that would require extra medical care favoured white patients over black patients. Even though race was not included in the algorithm, the cost of healthcare was. This translated into a correlation between black patients and the amount of money spent on care for the same health condition (Shin, 2020). An additional example, ironically is Amazon, one of the biggest global AI users. They became aware that their employment algorithm was biased against women. This was due to the majority of applicant being male, and therefore the algorithm became trained to favour men. Where the internet was developed as a networking tool, these unbalanced, biased algorithms have worked to disconnect and disassociate societies, cultures and race groups.
Ironically Artificial Intelligence (AI) is the product of this currently homogenous workforce and was developed to be an impartial, unbiased, unskewed solution; capable of making faster and better decisions than humans. A concept with massive potential, which unfortunately does show evidence of bias purely based on its creators. The AI itself becomes biased based on the non-diversity of the information it is built on (Johansen, 2022). An example of biased algorithms and the ease and speed with which they can develop can been seen on Twitter, where misogynistic tweets were sent to Tay, a Twitter-based chatbot built in 2016, it too less that 24 hours for an algorithm to be learnt and the chatbot was able to produce similarly unfavourable posts (Johansen, 2022). It appears that the solution to this problem lies in the hands of the leaders of the technology industry and their willingness and intention to diversify the workforce, particularly in the development sector which is white male dominated. These changes will take time, since the tech leaders fall into the same homogenous group. Another aspect to consider in the solution is that the technology sector continues the monomer that the tech workforce requires a degree and coding background. Upskilling in the technology arena would open up opportunities and tap into a much more diverse group of employees (Hurst, 2021).
Through the information and examples contained in this essay, it can be concluded that the internet has indeed been influenced by a lack of diversity. It can be seen that this lack of diversity is found within the technology industry itself, and through the introduction of coding and algorithms. These codes and algorithms have begun to negatively impact individuals and societies. Solutions to this issue and suggestions for risk mitigation have been highlighted in this essay. It can further be concluded that the process of developing codes and algorithms will need to be examined and the flaws inspected and repaired in order to protect its users and provide a more balanced, inclusive, diverse future of the internet.
Hurst, A. (2021, October 27). The top reasons why a lack of diversity in tech remains a problem. Information Age. Retrieved October 9, 2022, from https://www.information-age.com/why-lack-of-diversity-in-tech-remains-problem-123497406/
Johansen, A. (2022, July 14). How lack of diversity in tech leads to coding and algorithm biases – global comment. Global Comment – Where the world thinks out loud. Retrieved October 14, 2022, from https://globalcomment.com/how-lack-of-diversity-in-tech-leads-to-coding-and-algorithm-biases/
Leiner, B. M., Cerf, V. G., Clark, D. D., Kahn, R. E., Kleinrock, L., Lynch, D. C., Postel, J., Roberts, L. G., & Wolff, S. (2009). A brief history of the internet. ACM SIGCOMM Computer Communication Review, 39(5), 22–31. https://doi.org/10.1145/1629607.1629613
Shin, T. (2020, June 4). Real-life examples of discriminating artificial intelligence. Medium. Retrieved October 11, 2022, from https://towardsdatascience.com/real-life-examples-of-discriminating-artificial-intelligence-cae395a90070
YouTube. (n.d.). Gender Bias in Ai and Machine Learning Systems. YouTube. Retrieved October 10, 2022, from https://www.youtube.com/watch?v=Z7-DjqgO2w.