Digital redlining

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Digital redlining is the practice of creating and perpetuating inequities between already marginalized groups specifically through the use of digital technologies, digital content, and the internet. [1] The concept of digital redlining is an extension of the practice of redlining in housing discrimination, [2] [3] a historical legal practice in the United States and Canada dating back to the 1930s where red lines were drawn on maps to indicate poor and primarily black neighborhoods that were deemed unsuitable for loans or further development, which created great economic disparities between neighborhoods. [4] [5] The term was popularized by Dr. Chris Gilliard, a privacy scholar, who defines digital redlining as "the creation and maintenance of tech practices, policies, pedagogies, and investment decisions that enforce class boundaries and discriminate against specific groups". [6] [7]

Contents

Though digital redlining is related to the digital divide and techniques such as weblining and personalization, it is distinct from these concepts as part of larger complex systemic issues. [8] [9] It can refer to practices that create inequities of access to technology services in geographical areas, such as when internet service providers decide to not service specific geographic areas because they are perceived to be not as profitable and thus reduce access to crucial services and civic participation. [10] It can also be used to refer to inequities caused by the policies and practices of digital technologies. [2] For instance, with these methods inequities are accomplished through divisions that are created via algorithms which are hidden from the technology user; the use of big data and analytics allow for a much more nuanced form of discrimination that can target specific vulnerable populations. [11] These algorithmic means are enabled through the use of unregulated data technologies that apply a score to individuals that statistically categorize personality traits or tendencies which are similar to a credit score but are proprietary to the technology companies and not under outside oversight. [12] [2]

Digital redlining and geography

While the roots of redlining lie in excluding populations based on geography, digital redlining occurs in both geographical and non-geographical contexts. [2] An example of both contexts can be found in the charges brought against Facebook on March 28 of 2019, by the United States Department of Housing and Urban Development (HUD). HUD charged Facebook with violating the Fair Housing Act of 1968 by "encouraging, enabling, and causing housing discrimination through the company's advertising platform." [13] HUD stated that Facebook allowed advertisers to “exclude people who live in a specified area from seeing an ad by drawing a red line around that area.” The discrimination called out by HUD included those that were racist, homophobic, ableist, and classist. [13] Besides this example of geographically based digital redlining, HUD also charged that Facebook used profile information and designations to exclude classes of people. The charges stated: "Facebook enabled advertisers to exclude people whom Facebook classified as parents; non-American-born; non-Christian; interested in accessibility; interested in Hispanic culture; or a wide variety of other interests that closely align with the Fair Housing Act’s protected classes" [13] Several media outlets pointed out HUDs own history of housing discrimination through redlining, the establishment of the Fair Housing Act to combat redlining, and how the digital platform was recreating this discriminatory practice. [14] [15] [16] [17]

Digital redlining within a geographical context

Although digital redlining refers to a complex and varied set of practices, it has been most commonly applied to practices with a geographical dimension. Common examples include when an internet service providers decide to not service specific geographic areas because those areas are seen to be not as profitable, resulting in discrimination against low-income communities, with resulting impacts on access to crucial services and civic participation. [10] [18] AT&T has faced specific scrutiny for this form of digital redlining, it has been reported that AT&T has been classist in its offerings of broadband internet service in areas that are more impoverished. [19]

Geographically based digital redlining can also apply to digital content or the distribution of goods sold online. Geographically based games such as Pokémon Go have been shown to offer more virtual stops and rewards in geographic areas that are less ethnically and racially diverse. [20] In 2016, Amazon was rebuked for not offering their Prime same-day delivery service to many communities that were largely African American and had incomes that were beneath the national average. [21] [22] Even services such as email can be impacted, with many email administrators creating filters for flagging particular email messages as spam based on the geographical origin of the message. [23]

Digital redlining based on personal identity

Although often aligned with discrimination that falls into a geographically based context digital redlining also refers to when vulnerable populations are targeted for or excluded from specific content or access to the internet in a way that harms them based on some aspect of their identity. Trade schools and community colleges, which typically have a more working class student body, have been found to block public internet content from their students where elite research institutions do not. [24] The use of big data and analytics allow for a much more nuanced form of discrimination that can target specific vulnerable populations. [25] For example, Facebook has been criticized for providing tools that allow advertisers to target ads by ethnic affinity and gender, effectively blocking minorities from seeing specific ads for housing and employment. [26] [27] [28] In October 2019, a major class action lawsuit was filed against Facebook alleging gender and age discrimination in financial advertising. [29] [30] A broad array of consumers can be particularly vulnerable to digital redlining when it is used outside of a geographical context. Besides targeting vulnerable populations based on traditional and legally recognized classifications such as race, gender, age, etc., it has been shown that personal data mined and then resold by brokers can be used to target those who have been identified as suffering from Alzheimer's or dementia, or simply identified as impulse buyers or gullible. [31] [32]

Term distinctions

Distinctions between weblining and digital redlining

Earlier distinctions have been made between weblining—the process of charging customers different prices based on profile information --- and internet or digital redlining, with digital redlining being focused not on pricing but access. [8] As early as 2002 the Gale Encyclopedia of E-Commerce puts forth the distinction more in use today: weblining is the pervasive and generally accepted (or at least tolerated) practice of personalizing access to products and services in ways invisible to the user; digital redlining is when such personalized, data-driven schemes perpetuate traditional advantages of privileged demographics. [33] As weblining has become more ubiquitous, the term has fallen out of use in favor of the more general term personalization.

Distinctions between the digital divide and digital redlining

Scholars have often drawn connections between the digital divide and digital redlining. [34] In practice, the digital divide is seen as one of a number of impacts of digital redlining, and digital redlining is one of a number of ways in which the divide is maintained or extended. [24] [35]

Criticisms

A 2001 report looked to find if the reason for a gap in access to broadband internet by low-income and minority populations was due to a lack of availability or due to other factors. [34] The report found that there was "little evidence of digital redlining based on income or black or Hispanic concentrations" but that there was mixed evidence of redlining based on areas in which Native American or Asian populations were larger. [34]

Related Research Articles

<span class="mw-page-title-main">Redlining</span> Systemic denial of services to some areas

Redlining is a discriminatory practice in which financial services are withheld from neighborhoods that have significant numbers of racial and ethnic minorities. Redlining has been most prominent in the United States, and has mostly been directed against African-Americans. The most common examples involve denial of credit and insurance, denial of healthcare, and the development of food deserts in minority neighborhoods.

The digital divide is the unequal access to digital technology, including smartphones, tablets, laptops, and the internet. The digital divide worsens inequality around access to information and resources. In the Information Age, people without access to the Internet and other technology are at a disadvantage, for they are unable or less able to connect with others, find and apply for jobs, shop, and learn.

Personalized marketing, also known as one-to-one marketing or individual marketing, is a marketing strategy by which companies leverage data analysis and digital technology to deliver individualized messages and product offerings to current or prospective customers. Advancements in data collection methods, analytics, digital electronics, and digital economics, have enabled marketers to deploy more effective real-time and prolonged customer experience personalization tactics.

<span class="mw-page-title-main">Freedom of information</span> Freedom of a person or people to publish and consume information

Freedom of information is freedom of a person or people to publish and have access to information. Access to information is the ability for an individual to seek, receive and impart information effectively. As articulated by UNESCO, it encompasses

"scientific, indigenous, and traditional knowledge; freedom of information, building of open knowledge resources, including open Internet and open standards, and open access and availability of data; preservation of digital heritage; respect for cultural and linguistic diversity, such as fostering access to local content in accessible languages; quality education for all, including lifelong and e-learning; diffusion of new media and information literacy and skills, and social inclusion online, including addressing inequalities based on skills, education, gender, age, race, ethnicity, and accessibility by those with disabilities; and the development of connectivity and affordable ICTs, including mobile, the Internet, and broadband infrastructures".

In computing, Internet geolocation is software capable of deducing the geographic position of a device connected to the Internet. For example, the device's IP address can be used to determine the country, city, or ZIP code, determining its geographical location. Other methods include examination of Wi-Fi hotspots, a MAC address, image metadata, or credit card information.

Internet privacy involves the right or mandate of personal privacy concerning the storage, re-purposing, provision to third parties, and display of information pertaining to oneself via the Internet. Internet privacy is a subset of data privacy. Privacy concerns have been articulated from the beginnings of large-scale computer sharing and especially relate to mass surveillance.

Personalization consists of tailoring a service or product to accommodate specific individuals. It is sometimes tied to groups or segments of individuals. Personalization involves collecting data on individuals, including web browsing history, web cookies, and location. Various organizations use personalization to improve customer satisfaction, digital sales conversion, marketing results, branding, and improved website metrics as well as for advertising. Personalization acts as a key element in social media and recommender systems. Personalization influences every sector of society — be it work, leisure, or citizenship.

In marketing, promotion refers to any type of marketing communication used to inform target audiences of the relative merits of a product, service, brand or issue, persuasively. It helps marketers to create a distinctive place in customers' mind, it can be either a cognitive or emotional route. The aim of promotion is to increase brand awareness, create interest, generate sales or create brand loyalty. It is one of the basic elements of the market mix, which includes the four Ps, i.e., product, price, place, and promotion.

Mortgage discrimination or mortgage lending discrimination is the practice of banks, governments or other lending institutions denying loans to one or more groups of people primarily on the basis of race, ethnic origin, sex or religion.

Rural Internet describes the characteristics of Internet service in rural areas, which are settled places outside towns and cities. Inhabitants live in villages, hamlets, on farms and in other isolated houses. Mountains and other terrain can impede rural Internet access.

<span class="mw-page-title-main">Targeted advertising</span> Form of advertising

Targeted advertising is a form of advertising, including online advertising, that is directed towards an audience with certain traits, based on the product or person the advertiser is promoting.

Housing discrimination refers to patterns of discrimination that affect a person's ability to rent or buy housing. This disparate treatment of a person on the housing market can be based on group characteristics or on the place where a person lives.

In the United States, housing segregation is the practice of denying African Americans and other minority groups equal access to housing through the process of misinformation, denial of realty and financing services, and racial steering. Housing policy in the United States has influenced housing segregation trends throughout history. Key legislation include the National Housing Act of 1934, the G.I. Bill, and the Fair Housing Act. Factors such as socioeconomic status, spatial assimilation, and immigration contribute to perpetuating housing segregation. The effects of housing segregation include relocation, unequal living standards, and poverty. However, there have been initiatives to combat housing segregation, such as the Section 8 housing program.

Housing discrimination in the United States refers to the historical and current barriers, policies, and biases that prevent equitable access to housing. Housing discrimination became more pronounced after the abolition of slavery in 1865, typically as part of Jim Crow laws that enforced racial segregation. The federal government didn't begin to take action against these laws until 1917, when the Supreme Court struck down ordinances prohibiting blacks from occupying or owning buildings in majority-white neighborhoods in Buchanan v. Warley. However, the federal government as well as local governments continued to be directly responsible for housing discrimination through redlining and race-restricted covenants until the Civil Rights Act of 1968.

<span class="mw-page-title-main">Digital privacy</span>

Digital privacy is often used in contexts that promote advocacy on behalf of individual and consumer privacy rights in e-services and is typically used in opposition to the business practices of many e-marketers, businesses, and companies to collect and use such information and data. Digital privacy, a crucial aspect of modern online interactions and services, can be defined under three sub-related categories: information privacy, communication privacy, and individual privacy.

Net bias is the counter-principle to net neutrality, which indicates differentiation or discrimination of price and the quality of content or applications on the Internet by ISPs. Similar terms include data discrimination, digital redlining, and network management.

<span class="mw-page-title-main">Cloud marketing</span>

Cloud marketing is the process of an organization's efforts to market their goods and services online through integrated digital experiences, by which they are specialized for every end-user. It aims to use advertising methods to give tailor-made adverts to customers based on their browsing history or interests via online applications through social media websites such as Facebook, Twitter and various online portals. Cloud marketing platforms could be supported by third-party providers that maintain the platform.

The commercialization of the Internet encompasses the creation and management of online services principally for financial gain. It typically involves the increasing monetization of network services and consumer products mediated through the varied use of Internet technologies. Common forms of Internet commercialization include e-commerce, electronic money, and advanced marketing techniques including personalized and targeted advertising. The effects of the commercialization of the Internet are controversial, with benefits that simplify daily life and repercussions that challenge personal freedoms, including surveillance capitalism and data tracking. This began with the National Science Foundation funding supercomputing center and then universities being able to develop supercomputer sites for research and academic purposes.

A lookalike audience is a group of social network members who are determined as sharing characteristics with another group of members. In digital advertising, it refers to a targeting tool for digital marketing, first initiated by Facebook, which helps to reach potential customers online who are likely to share similar interests and behaviors with existing customers. Since Facebook debuted this feature in 2013, additional advertising platforms have followed suit, including Google Ads, Outbrain, Taboola, LinkedIn Ads and others.

Digital self-determination is a multidisciplinary concept derived from the legal concept of self-determination and applied to the digital sphere, to address the unique challenges to individual and collective agency and autonomy arising with increasing digitalization of many aspects of society and daily life.

References

  1. ↑ Gilliard, Christopher (2019-11-21). Prepared testimony and statement for the record of Christopher Gilliard PhD, Hearing on Banking on your Data the Role of Big Data in Your Financial Services, Before the House Financial Services Committee Task Force on Financial Technology . Retrieved 2019-11-21
  2. 1 2 3 4 Podesta, John; Penny, Pritzker; Moniz, Earnest, J.; Holdren, John; Zients, Jeffery (May 2014). "Big Data: Seizing Opportunities, Preserving Values" Whitehouse.gov. Retrieved December 18, 2018
  3. ↑ Gilliard, Chris (2017-07-03). "Pedagogy and the Logic of Platforms". er.educause.edu. EDUCAUSE Review. Retrieved 2019-04-04.
  4. ↑ Madrigal, Alexis C. (2014-05-22). "The Racist Housing Policy That Made Your Neighborhood". The Atlantic. Retrieved 2018-11-11.
  5. ↑ Harris, Richard; Forrester, Doris (2003-12-01). "The Suburban Origins of Redlining: A Canadian Case Study, 1935-54". Urban Studies. 40 (13): 2661–2686. Bibcode:2003UrbSt..40.2661H. doi:10.1080/0042098032000146830. ISSN   0042-0980. S2CID   154651681.
  6. ↑ Oremus, Will (September 17, 2021). "A Detroit community college professor is fighting Silicon Valley's surveillance machine. People are listening". Washington Post. ISSN   0190-8286 . Retrieved 2021-09-17.
  7. ↑ Eveleth, R. (2020). Flash Forward [Audio Podcast]. Boss Bot. https://www.flashforwardpod.com/2020/09/29/boss-bot/
  8. 1 2 Bidgoli, Hossein (2004-04-26). The Internet Encyclopedia, Volume 3 (P - Z). John Wiley & Sons. ISBN   9780471689973.
  9. ↑ Stepanek, Marcia (2 April 2000). "Weblining". Bloomberg BusinessWeek.
  10. 1 2 Neidig, Harper (2017-09-25). "AT&T hit with second complaint of discrimination against low-income neighborhoods". TheHill. Retrieved 2018-11-11.
  11. ↑ O'Neil, Cathy (2016-09-06). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown/Archetype. ISBN   9780553418828.
  12. ↑ Taylor, Astra; Sadowski, Jathan. "How Companies Turn Your Facebook Activity Into a Credit Score | The Nation". The Nation. ISSN   0027-8378. Archived from the original on 2019-03-31. Retrieved 2018-11-11.
  13. 1 2 3 "HUD Charges Facebook With Housing Discrimination Over Company's Targeted Advertising Practices". www.hud.gov (in internet archive). 2019-03-28. Archived from the original on 2020-04-10. Retrieved 2020-04-26.
  14. ↑ Board, The Editorial (2019-03-29). "Opinion | Facebook Faces a Reckoning for Redlining". The New York Times. ISSN   0362-4331 . Retrieved 2019-03-31.
  15. ↑ Brandom, Russell (2019-03-28). "Facebook has been charged with housing discrimination by the US government". The Verge. Retrieved 2019-03-31.
  16. ↑ Jones, Sarah (2019-03-28). "Facebook Charged With Discrimination, Despite Ben Carson's Fair Housing Rollback". Intelligencer. Retrieved 2019-03-31.
  17. ↑ Higgins, Eoin. "'Commendable, If Not Overdue': HUD Files Charges Against Facebook for Virtual Redlining". Common Dreams. Retrieved 2019-03-31.
  18. ↑ Neidig, Harper (2017-08-24). "Civil rights lawyer accuses AT&T of discriminating against low-income communities". TheHill. Retrieved 2018-11-11.
  19. ↑ Callahan, Bill (2019-08-06). "AT&T's Digital Redlining of Dallas: New Research by Dr. Brian Whitacre". National Digital Inclusion Alliance. Retrieved 2019-10-29.
  20. ↑ Akhtar, Allana (2016-08-09). "Is Pokémon Go racist? How the app may be redlining communities of color". USA TODAY. Retrieved 2018-11-11.
  21. ↑ Ingold, David; Soper, Spencer (2016-04-16). "Amazon Doesn't Consider the Race of Its Customers. Should It?". Bloomberg. Retrieved 2018-11-11.
  22. ↑ Talbot, David. "Amazon's same-day delivery service reinforces inequality". MIT Technology Review. Retrieved 2018-11-14.
  23. ↑ Lueg, Christopher; Jeff, Huang; Twidale, Michael (2007-03-01). "Mystery Meat revisited: Spam, Anti-Spam Measures and Digital Redlining". Webology. 4.
  24. 1 2 Gilliard, Chris; Culik, Hugh (2016-05-24). "Digital Redlining, Access, and Privacy". Common Sense Education. Archived from the original on 2019-03-31. Retrieved 2018-11-11.
  25. ↑ Podesta, John (2014-05-01). "Findings of the Big Data and Privacy Working Group Review". whitehouse.gov. Retrieved 2018-11-14.
  26. ↑ Angwin, Julia; Parris Jr., Terry (2016-10-28). "Facebook Lets Advertisers Exclude Users by Race". ProPublica. Retrieved 2018-11-11.
  27. ↑ Cole, Samantha; Koebler, Jason (2019-04-04). "Researchers Find Facebook's Ad Targeting Algorithm Is Inherently Biased". Motherboard. Retrieved 2019-04-04.
  28. ↑ Muhammad, Ali; Sapiezynski, Piotr; Bogen, Miranda; Korolova, Aleksandra; Mislove, Alan; Aaron, Rieke (2019-04-03). "Discrimination through optimization: How Facebook's ad delivery can lead to skewed outcomes". Proceedings of the ACM on Human-Computer Interaction. 3 (CSCW). arXiv: 1904.02095 . Bibcode:2019arXiv190402095A. doi:10.1145/3359301. S2CID   102353579.
  29. ↑ Merrill, John Detrixhe, Jeremy B. (November 2019). "The fight against financial advertisers using Facebook for digital redlining". Quartz. Retrieved 2019-11-02.{{cite web}}: CS1 maint: multiple names: authors list (link)
  30. ↑ "Facebook Hit with Massive National Civil Rights Class Action by Women and Older People Denied Financial Services Opportunities on Facebook for Years | Outten & Golden LLP". Outten & Golden. Archived from the original on 2020-04-28. Retrieved 2019-11-02.
  31. ↑ Pasquale, Frank (2017-11-17). "Algorithms: How Companies’ Decisions About Data and Content Impact Consumers” Written Testimony of Frank Pasquale Professor of Law University of Maryland Before the United States House of Representatives Committee on Energy and Commerce Subcommittee on Digital Commerce and Consumer Protection. Retrieved 2018-11-14
  32. ↑ "Digital Market Manipulation" (PDF). The George Washington Law Review. 2014-10-03. Retrieved 2019-11-02.
  33. ↑ Malonis, Jane A. (2002). Gale Encyclopedia of E-Commerce. Gale Group. pp. 758–760.
  34. 1 2 3 Prieger, James E. (December 2001). The Supply Side of the Digital Divide: Is There Redlining in the Broadband Internet Access Market? AEI-Brookings Joint Center Working Paper No. 01-16. Available at SSRN: https://ssrn.com/abstract=297499 or https://dx.doi.org/10.2139/ssrn.297499
  35. ↑ Ferro, E., Cantamessa, M., & Paolucci, E. (2005). Urban versus regional divide: Comparing and classifying digital divide. In E-government: Towards electronic democracy (pp. 81-90). Springer, Berlin, Heidelberg. Accessed December 13, 2008 from https://www.researchgate.net/publication/221460528_Urban_Versus_Regional_Divide_Comparing_and_Classifying_Digital_Divide
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