Algorithmic injustice is representative of the programmers behind 21st-century technology. This op-ed is meant to dig into the relationship between Indian-Americans in the tech field and their potential involvement in this discriminatory technology.
Indian American men are foundational to the current global tech force. They hold thousands of software engineering roles at almost every major institution, yet their powerful influence on advancements in technology and potential complicity in creating injustice often go unnoticed and unchecked. Code is a reflection of the programmers who write it, and it is time we start taking a deeper look at the harms that high-caste, privileged Indian men perpetuate as the architects of our technology.
From the social media filters on our phones to the satellite systems that surround our planet, technology has become a ubiquitous part of human society. Contrived by some of the most complex algorithms and mathematical equations, technology is often perceived as a feat of innovation void of human error—when in reality, it is a reflection of all the flaws that are ingrained in human society. Humans design A.I. systems and algorithms, and as a result, it is saturated with their biases. The discriminatory impacts of A.I. and algorithms have been shown and proven countless number of times, through harmful beauty artificial intelligence, criminal justice surveillance systems, discriminatory hiring and recruitment processes, and bias in healthcare tools. This technology adversely affects marginalized communities around the world in all facets of their lives.
While these examples are unsettling, they should not be shocking. If someone were to look into the demographics of the major tech companies and institutions, they would see that 92 percent of all software positions are held by whites and Asians, and 80 percent are held by men. Researchers and activists have showcased that the disproportionate number of white men in the software industry is directly connected to bias in our algorithms (“Diversity in Tech by the Numbers”). The most common cause of algorithmic bias happens through deep learning. Deep learning algorithms make decisions based on trends found in large quantities of data, which reflect the racism, misogyny, and classism rampant in society.
Growing up as a woman interested in technology in a community of Indian-American software engineers, I was exposed to deeply ingrained forces of misogyny, casteism, and classism from my male peers. Experiencing and seeing these behaviors present within individuals from my community brings me to question the role of 3+ million Indian software engineers might play in perpetuating algorithmic injustice. It is time we start questioning how their values and ways of thinking lead into the code and technology frameworks they are responsible for.
Harvard professor Ajantha Subramanian, states in her book, “The Caste of Merit: Engineering Schools of India'' that current Indian American software engineers hold significant class and caste privileges in Indian society. She explains that those belonging to lower castes in India face structural barriers that prevent them from pursuing higher education in a similar way to how America’s long history of racism has created institutionalized barriers in education for low-income, black and brown communities. Factors such as fewer academic resources, discrimination within classrooms, a lack of proper physical and emotional support, and lack of meaningful familial engagement prevent students of lower caste backgrounds from receiving the same merit of education as higher-caste students. The educational inequality in India’s schooling systems triggers a domino effect that leads to higher ratios of upper caste individuals in elite engineering schools, further allowing them to pursue well-paying careers in technology and utilize the H1-B visa to immigrate to the United States.
Equality Labs, a South Asian American human rights startup, reported that “two-thirds of members of the lowest caste, called Dalits, said they have faced workplace discrimination due to their caste. Forty-one percent have experienced discrimination in education because of it. And a quarter of Dalits say they've faced physical assault — all in the United States” (NPR). Unfortunately, it does not stop there. Indian Americans are fervent Modi loyalists, and privileged Indian Americans applaud Trump’s and Modi’s actions the way privileged White-Americans uplift Trump.
What this essentially means is that when it comes to algorithmic injustice, we must hold the Indian American IT sector accountable alongside other professionals. Just because Indian Americans are not white or because they are immigrants does not mean they have not benefited from oppressive institutions. We must understand their individual relationship to various social forces such as caste, race, gender, and ethnicity and how their personal role within these systems of oppression negatively influences their point of view, ultimately leading to biased and discriminatory technology.
Our next steps are twofold: to increase awareness and understanding of the problem alongside actively pushing for true diversity in STEM. As aforementioned, many researchers have examined the effects of a disproportionate amount of white men in technology and have drawn links to how their population promotes algorithmic injustice. We need more researchers and digital rights activists assessing how Indian-Americans specifically contribute to algorithmic injustice with their unique set of biases and prejudices, and how each of us might as well. Furthermore, we need to create educational and professional pathways for underrepresented minorities to secure jobs in technology to make sure our technology serves all Americans and not just the privileged.
Allen Smith, J.D. “AI: Discriminatory Data In, Discrimination Out.” SHRM, SHRM, 28 Feb. 2020, www.shrm.org/resourcesandtools/legal-and-compliance/employment-law/pages/artificial-intelligence-discriminatory-data.aspx.
Booz Allen Hamilton. “Artificial Intelligence Bias in Healthcare.” Booz Allen Hamilton, Booz Allen Hamilton, www.boozallen.com/c/insight/blog/ai-bias-in-healthcare.html.
Contributors, Et. “H1B Visa: The H-1B Saga – Much Ado about Something.” The Economic Times, Economic Times, 7 July 2020, economictimes.indiatimes.com/nri/visa-and-immigration/view-the-h-1b-saga-much-ado-about-something/articleshow/76808871.cms?from=mdr.
“Diversity in Tech by the Numbers: Age, Race, & Gender.” Recruiting Innovation, 1 Dec. 2020, recruitinginnovation.com/blog/diversity-in-tech/.
Julia Angwin, Jeff Larson. “Machine Bias.” ProPublica, 23 May 2016, www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.
Paul, Sonia. “When Caste Discrimination Comes To The United States.” NPR, NPR, 25 Apr. 2018, www.npr.org/sections/codeswitch/2018/04/25/605030018/when-caste-discrimination-comes-to-the-united-states.
Radhakrishnan, Vignesh. “'The Caste of Merit' Review: A Disconnect between Engineering Studies and the Profession.” The Hindu, The Hindu, 20 June 2020, www.thehindu.com/books/books-reviews/the-caste-of-merit-review-a-disconnect-between-engineering-studies-and-the-profession/article31867391.ece.
Report • By Emma García and Elaine Weiss • September 27. “Education Inequalities at the School Starting Gate: Gaps, Trends, and Strategies to Address Them.” Economic Policy Institute, www.epi.org/publication/education-inequalities-at-the-school-starting-gate/.
Service, Tribune News. “Trump-Modi 'Friendship' Driving Indian-Americans towards US President: Survey.” Tribuneindia News Service, www.tribuneindia.com/news/diaspora/trump-modi-friendship-driving-indian-americans-towards-us-president-survey-145817.
“The Disturbing Truth about AI and Beauty.” Dazed, 1 Oct. 2018, www.dazeddigital.com/beauty/head/article/41605/1/ai-beauty-face-scanning.
As someone who has grown up in a community of Indian American software engineers, I have been exposed to the deep misogyny, casteism, and classism embedded within our community and its members, and recently, I’ve spent a lot of time questioning the implications of these behaviors and its effect on technology and society. Through this oped, I hope bring awareness to this unexplored intersection and further examine the relationship between this demographic and modern day technology. With Indian- American men being a large backbone of the current tech infrastructure, it is imperative that both Asian-American and tech communities acknowledge this issue and develop further research on this crucial topic.
Sreya is a 17 year old activist from Atlanta, GA and is passionate about South Asian and tech advocacy. She is the founder of South Asian Students For Progressive Action, an organization for Desi-identifying youth to prioritize social and political change within their communities and aspires to do more community organizing in her future. When she’s not busy with advocacy work, Sreya loves to play with her dog, watch baking shows, and try out different types of boba.
Cover Photo Source: The New York Times