Unlimited free links between humans and information embedded in the digital conversion era actuate the internet dynamics toward more transparency and approachability. W3C, constructed by Tim Berners-Lee, is inheriting the aboriginal aim of #Foreveryone to configure network cognition activation and bolster mass intellection schema. The ostensibly flat and balanced collaborative cyber environment successively triggers flare-ups of antithesis that emerge in the overwhelming information asymmetry and the unending paradox of transposition. Structural equalities indoctrinated in the social status quo remain mixed and result in a knowledge gap in participatory culture aroused by the digital divide to conceivably fashion the interior or exterior transmutation on internet headway.
ICTs Access and Digital Inequalities
Digital barrier issue as the ramification of structural inequality, to a large extent, determinate the rights and opportunities for the public to access the Information Communication Technologies (ICTs), and unequally distributed pattern formulate the structural grid to disseminate information (Pan et al., 2011, p.1). Jerry Wiggins (Freethink, 2020) conducted a field trip to explore broadband internet access and revealed that the Wi-Fi connection diverges from central and suburb areas during the COVID-19, and the sharpest divided region-Detroit lacks the affordable accessibility of internet and device to support the education. Additionally, the Measuring the Information Society Report (ITU, 2017, p.20) indicates that only “one in seven women uses the Internet compared with one in five men” in Less Developed Countries (LDCs). The socio-economically factors augment the disadvantages of clusters with low-level pay and education in the proprietorship of Internet PCs, involving the old and the young (Hsieh et al., 2008, p. 20). It veritably enunciates that the diversiform structural inequalities compactly correlating with social constituents are the mainspring of the information gap.
“The Butterfly Effect” of Structural Inequality
Split by the digital divide, the chasm brings along the attribute alienation of internet culture and hinders the unprivileged groups in touch with gender, ethnicity, and sexuality. Based on the ideologies of the digital economy in the 1960s, the popularization-oriented ideological form is in the domination of male, heteronormative, white (Lusoli & Turner, 2021, p. 237). Therefore, the structural inequality infiltrates the communication structure in the big-data network, demonstrating that the individual web exposure reshapes the media diet in a higher degree of cohesiveness or “turtles up” beneath the social stress (Romero et al., 2019, p. 6:1). Information Cocoons and Echo Chamber become more manifest and propagating, is the representative phenomenon of attention polarization. In light of the actuality, there are four shifts assimilated in the internet development:
- Economic: Capitalization enlarges the gap between the haves and the have-nots.
- Politic: The muzzle of contradiction points to the discord of political accomplishment.
- Cultural: Deterioration of interchange separation intensifies the root of discrimination.
- Technical: Historical experience forces revolution but not eradicates the uncomfortable.
Dawn of Justice
Technology is arguably the most struggling tool for people to seek equal entrances and exits, which means meanwhile inequality externalized and envisioned, there are practical measures to remedy the complexion. For instance, dedicated to transforming the privatized and liberalized Australian telecommunications system of the late 1980s, the establishment of the National Broadband Network (NBN) contributes to the “high-speed fixed-line” connection to premises of the country (Madsen & Percy, 2020, p.218). The disabled, as one of the disadvantaged communities, is still keeping on track with the justness dilemma. The sue to SOCOG from Bruce Maguire accusing the devoid accessible braille and online resources on 2000 Olympic Games rising concern to learn from the digital inclusion (Goggin et al., 2017), and in succession, the 2006 United Nations Convention on the Rights of Persons with Disabilities (CRPD) set out to guarantee the rights of people with disabilities to access the ICTs and use the internetworked sources. Been informed of the adjustment details, the maturing of the Internet has expanded the space for cultivating a sense of fairness and create an inclusive net encompassment.
Tech Leap Springs up Chained with Value Distortion
In effect, the bridge built for the digital gap is not even complete, and the negligence of social issues deeply implants in the technical transformation. Under the weight of economic benefits, political oppression, and cultural division of the Internet, vulnerable groups are subjected to prejudice and suffer from unfair treatment either by accident or on purpose. Plaformization, algorithmization, and artificial intelligence nowadays become synonymous with the technology revolution. Nonetheless, the “techlash” and capital market incur the anti-competitive modes of tech companies and severe privacy disclosure due to the commodification of media content and user data and the allure of advertising and traffic (Flew et al., 2019). In a sense, the structural inequalities potentially amplify ideological flaws followed by the admission of ideas and values 180-degrees away from the original (Flew, 2019, p. 97).
Discriminatory Commercial AI System of Amazon to Identify Faces
The deficiency of self-regulation and government control of artificial intelligence with strong commercialized potential is the bane of a more lopsided market dictatorship in the common interest. Joy Buolamwini, as the institutor of the Algorithmic Justice League, digs out the jaundiced technical issue of recognizing performance on facial analysis. As shown in the picture, the accuracy of AI in face recognition is significantly different in gender and skin color, and the precision of white and male facial analysis is approximately 100% in 2018. Furthermore, the error rate on the image prediction of darker-skinned women is much higher than that of light-skinned men (Buolamwini, 2019). The Facial Recognition Technology (FRT) is facing plenty of critics for oppressing the ethical group and inciting the racism issues from “the computer vision and technical classification” (Stevens & Keyes, 2021, p. 844). This case thoroughly projects mainstream consciousness and data bias extracted from the real world in technology profoundly affects human decision preference and machine learning outcomes.
Reddit: The Anti-feminism Imprinted in the Hack Attack
Info-age entrapped in the “Platform Capitalism” mentioned by Srnicek (2017), and even open-source platforms perpetuate and exasperate the immoral use behavior and non-action. Sub-communities (subreddit) possessing a high degree of autonomy embraced by the Reddit platform turn into the nucleus of Information Cocoons to agitate the circulation of the toxic technocultures. Numerous female selfies storing in Apple’s iCloud become the target of the hack attack, especially the photos of Jennifer Lawrence propagated in /r/thefappening attracting 100,000 new subscribers to sign up within 24 hours in 2014 (Massanari, 2017, p. 335). Once again, women descend to being victims of the platform algorithm and technique violation. Due to being rooted in the unequal structure of gender rights and skills, females defenselessly situate in an inferior position in un-neutralism and animus of platform operation. Hence, the collision from popular cognition inserted in digital memories is the originator to compel justice to be jeopardized.
Perceiving Sexuality Using Vision Algorithms Tagged by “Partiality”
Nicknamed as AI “Gaydar” using the VGG-Face in prediction model to detect the sexual orientation of people (Quach, 2019) caused a stir in the public outcry. Michal Kosinski reports that the accuracy of a classifier telling apart gay/lesbian and heterosexual from cases is overtly higher than human according to facial expression and dressing style, and profiles leakage from dating websites and the application for companies and government (Wang & Kosinski, 2018) both makes people ponder how much privacy is at stake outside the threshold of freedom. The visual precision algorithm analysis associating appearance with sexuality puts in motion for people to reappraise, which is a threat behavior referring to the minority. If this technology is exploited in countries where LGBT is illegal, it will reach more importunate human rights infringement. The abuse of AI and the misappropriation of the database are the introspective challenges for the gradual marginalization of disadvantaged communities.
There is Still a Long Haul Ahead
As stated, even if the door to the internet is eternally open for everyone, information inequalities have never gone away. It is worth mentioning that the faith in achieving absolute social fairness in terms of geography, age, gender, race, and sexuality is a utopian fantasy. The innovation monopoly dragged the society into differentiation, and the communes confined to the old ways of thinking are the self-examining to be confronted. The extreme variation between users in acquisition and participation induces the cost of network infrastructure and invasion of ideologies bringing new problems. Therefore, derived from the experience and lessons, the internet transformation also needs to put proactive correction of solidified spirit and the construction of a tech ecosystem emphasizing diversity into practice.
Buolamwini, J. (2019). Response: Racial and gender bias in Amazon rekognition - commercial AI system for analyzing faces. Retrieved October 09, 2021, from https://medium.com/@Joy.Buolamwini/response-racial-and-gender-bias-in-amazon-rekognition-commercial-ai-system-for-analyzing-faces-a289222eeced
Curiious. (2009). NBN – National Broadband Network Explained [Video]. https://www.youtube.com/watch?v=SgzAmKHSqFI
Flew, T. (2019). Guarding the Gatekeepers: Trust, Truth and Digital Platforms. Griffith Review, 64, 94-103. doi: https://eprints.qut.edu.au/128266/
Flew, T., Martin, F., & Suzor, N. (2019). Internet regulation as media policy: Rethinking the question of digital communication platform governance. Journal of Digital Media & Policy, 10(1), 33–50. https://doi.org/10.1386/jdmp.10.1.33_1
Freethink. (2020). The Digital Divide, Explained [Video]. https://www.youtube.com/watch?v=aMi3ky04XqY.
Goggin, G., Hollier, S., & Hawkins, W. (2017). Internet accessibility and disability policy: lessons for digital inclusion and equality from Australia. Internet Policy Review, 6(1). https://doi.org/10.14763/2017.1.452
Hsieh, J. J. P.-A., Rai, A., & Keil, M. (2008). Understanding Digital Inequality: Comparing Continued Use Behavioral Models of the Socio-Economically Advantaged and Disadvantaged. MIS Quarterly, 32(1), 97–126. https://doi.org/10.2307/25148830
ITU. (2017). Measuring the Information Society Report 2017 Volume 1. Geneva Switzerland: ITU. Retrieved from https://www.itu.int/en/ITU-D/Statistics/Documents/publications/misr2017/MISR2017_Volume1.pdf
Lusoli, A., & Turner, F. (2021). “It’s an Ongoing Bromance”: Counterculture and Cyberculture in Silicon Valley—An Interview with Fred Turner. Journal of Management Inquiry, 30(2), 235–242. https://doi.org/10.1177/1056492620941075
Madsen, A., & Percy, M. (2020). Telecommunications infrastructure in Australia. The Australian Journal of Social Issues, 55(2), 218–238. https://doi.org/10.1002/ajs4.121
Massanari, A. (2017). Gamergate and The Fappening: How Reddit’s algorithm, governance, and culture support toxic technocultures. New Media & Society, 19(3), 329–346. https://doi.org/10.1177/1461444815608807
Pan, Z., Yan, W., Jing, G., & Zheng, J. (2011). Exploring structured inequality in Internet use behavior. Asian Journal of Communication, 21(2), 116–132. https://doi.org/10.1080/01292986.2010.543555
Quach, K. (2019). The infamous AI Gaydar study was repeated – and, no, code can’t tell if you’re straight or not just from your face. Retrieved October 12, 2021, from https://www.theregister.com/2019/03/05/ai_gaydar/
Romero, D., Uzzi, B., & Kleinberg, J. (2019). Social Networks under Stress: Specialized Team Roles and Their Communication Structure. ACM Transactions on the Web, 13(1), 1–24. https://doi.org/10.1145/3295460
Srnicek, N., & De Sutter, L. (2017). Platform capitalism. Cambridge, UK;: Polity.
Stevens, N., & Keyes, O. (2021). Seeing infrastructure: race, facial recognition and the politics of data. Cultural Studies (London, England), 35(4-5), 833–853. https://doi.org/10.1080/09502386.2021.1895252
Wang, Y., & Kosinski, M. (2018). Deep Neural Networks Are More Accurate Than Humans at Detecting Sexual Orientation From Facial Images. Journal of Personality and Social Psychology, 114(2), 246–257. https://doi.org/10.1037/pspa0000098
The Vehicle of Digital Equalities is on the Way of Counterpoise or Polarization? by Yiqing Chen is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.