NetMission Academy 2020: Key Takeaway of Training VI – Martha Mai Hatch

As the working group for Training VI, it is our pleasure to be presenting our research on Diversity and Multi-Stakeholder Participation to our guest speakers, Maarten Botterman (ICANN Board, Chairman), Elisabeth Schaermann (Youth IGF Summit 2019, EuroDIG), Noelle Francesca De Guzman (Regional Policy Manager, Asia-Pacific) and to our fellow organizers and participants. The goal of this particular blog is to recapture the dialogue on diversity during the session and further deepening the discussion.

Diversity is an essential quality to any policy-making process in ensuring a balanced, fair and multifaceted policy is developed in minimizing its cons. “ignorance of each other’s ways and lives has been a common cause, throughout the history of mankind”, of suspicion and mistrust through which their differences have “all too often broken into war”(UNESCO cited in Köchler). With a diverse and multi-stakeholder participation process, the policy is designed to be as inclusive as possible and it is negotiated to balance the interests between the stakeholders.

Diversity is an important quality to any policymaking process as diversity assists in creating a success-oriented, cooperative, compassionate community that draws intellectual strength and produces innovative solutions from the synergy of its people (QBCC cited in Netmission). This is because diversity is a concept that encompasses acceptance and respect by recognizing and understanding each individuals’ uniqueness and differences. These can be along the dimensions of race, ethnicity, gender, sexual orientation, socio-economic, etc (QBCC cited in Netmission, n.d).

The diversity principle model is an important model in assisting us in understanding how diversity plays out and influences the outcome of policymaking. This model is highly recommended by Philip Napoli and Kari Karppinen in their research paper on translating diversity in Internet Governance as “the theme of diversity perhaps has the deepest roots in other areas of communications policymaking.”(Napoli & Karppinen, n.d).

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The principle of diversity can be broken down into three interrelated components which are: (1) Source Diversity; (2) Content Diversity and (3) Exposure Diversity. These three diversities are self-explanatory, as source diversity refers to the extent to which the media system is populated by a diverse array of content providers. This focus on content providers can emphasize the ownership of either the media outlets or the underlying content (Napoli & Karppinen, n.d). Diversity criteria taking a variety of forms, ranging from ownership race/ethnicity or gender, organizational or economic structure, etc. (Napoli & Karppinen, n.d). Content diversity refers to the diversity of program types or genres available, the diversity of ideas or viewpoints expressed, or in terms of the demographic diversity of those depicted in the content (Napoli & Karppinen, n.d).. Exposure diversity refers to the diverse array of content available for the audiences to consumes. It is often presumed that these three elements have a linear relationship and are interlinked (Napoli & Karppinen, n.d). However, it is to be noted that the interlinked relationship is often questioned and does not provide definitive evidence (Napoli & Karppinen, n.d).

Exposure diversity if often believed to be the most important element of diversity within the model. This is because the element is directly exposed to the public and has a significant influence on society. The influence would thus be recycled in the system back into the source diversity. To understand how changes in source and content diversity impact exposure diversity is fundamental to policymakers’ understanding of the production and consumption dynamics of any communications system (Napoli & Karppinen, n.d). To illustrate the above discussion, we will be analysis Dr. Safiya Noble’s book on Google’s PageRank algorithm with the diversity principal model.

The book draws on Dr. Safiya Umoja Noble’s research into algorithms and bias to show how online search results are far from neutral, but instead replicates and reinforces racist and sexist beliefs that reverberate in the societies in which search engines operate (Kara, 2019).  In the book, Dr. Noble has noticed that when she searched terms such as Asian girls, black girls, Hispanic girls, etc. of ethnic minorities in the United States, the research results were flooded with pornographic sites. Vice versa, she noticed that when such a keyword was searched in regard to the male gender, the search results were mostly just regular news or sometimes with a few occasions of stereotypies. With the non-transparent attributes and values set to the algorithm, it is hard to assume what has led to such occurrence. However, this is an example of a lack of exposure and content diversity, where the audience was only given a single narrative of results when exposed to the content.

One of the few known attributes of the PageRank algorithm is the number of searches. Dr. Noble argues that even if that pornographic sites are the most searched results for the keyword, does it justify that the search of a keyword is filled with pornographic. As the father of the internet Vint Cerf had said: “The Internet is a mirror of the population that uses it” (Cerf, cited in CIO, 2007). However, Dr. Noble believes that the internet is more than just the mirror of reality, but it also shapes the reality. Imagine if you’re a child that searches such keywords and the 10 pages of results of Asian girls are porn-sites. How would that affect the way they perceive women of color? Therefore, this presents us that the lack of exposure diversity could lead to significant social problems and worsen it. 

Dr. Noble then further explores the potential reason behind the sexist and racist results. She argues that the lack of source diversity is what has led to such consequences. According to research, Ten large technology companies in Silicon Valley did not employ a single black

woman in 2016; Three had no black employees at all; Six did not have a single female executive, etc. (Rangarajan, 2018). With such a lack of diversity in the source, there is a potential that the white-male dominated industry has embedded its homogenic world view into the algorithm. Even if the world view wasn’t deployed, the allowance of such homogenic exposure has presented the diversity isn’t a concern to the homogenic source. Therefore, this goes to show us how the lack of source diversity could have directly led to the content and exposure diversity, which is problematic and controversial. Hence, with the case study of Dr.Noble’s theory, it informs us why the application of diversity throughout the policymaking process is essential to create an inclusive and non-discriminatory policy that would have a significant impact on the world.

About the writer

Martha Mai Hatch (NetMission Ambassador of class 2019/20, Hong Kong)
Joint-Bachelor’s Degree in Creative Media & Digital Media, City University of Hong Kong & Leuphana University of Lüneburg

Definition of Diversity. (n.d.). Retrieved from

Kara, H. (2019, June 10). Book Review: Algorithms of Oppression: How Search Engines Reinforce Racism by Safiya Umoja Noble. Retrieved from

Köchler, H. (n.d.). Unity in Diversity:The Integrative Approach to Intercultural Relations. Retrieved from

Napoli , P. M., & Karppinen , K. (n.d.). Translating Diversity into Internet Governance . First Monday. Retrieved from

Noble, S. U. (2018). Algorithms of oppression: how search engines reinforce racism. New York: New York University Press.

Rangarajan, S. (2018, June 25). Bay Area tech diversity: White men dominate Silicon Valley. Retrieved from

Vint Cerf: Internet Is a Reflection of Society. (2007, February 21). Retrieved from–internet-is-a-reflection-of-society.html