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Data Basics for Lawyers

By Rebecca Pinsky

 

Data use is an increasingly scrutinized practice. Terms like “data use,” “data privacy,” “analytics,” and “machine learning” can be obtuse to people without experience working with data. Understanding data doesn’t have to be difficult, though. The short guide below is meant to help current and future attorneys gain foundational understanding of core data concepts so they will be better suited to analyze data-related issues.

What is Data?

Data vs. Information

Data simply means recorded values.[1] Data can be qualitative or quantitative.[2] Typically, data is stored as text or numerals. Data is granular and specific. Information is broader and purpose-driven. Think of information as the higher-level insights that can be derived from data. [3]

Types of Data

Recording data as either “text or numerals” is still not a particularly precise methodology. That is why types of data are usually classified further. Different languages store data differently, but the basics typically do not vary broadly:

  • A character is a single entry, whether text or numerical. “R” is one character, “law” is three characters, and “2022” is four characters.[4]
  • Boolean data can be one of two values, usually TRUE or FALSE, YES or NO, or 1 or 2.[5]
  • Date data represents dates or times, like a YYYY-MM-DD date, a hh:mm:ss timestamp, a “datetime” combination of a date and a timestamp, or a date part, such as a year, month, or day.[6]
  • Numeric data types store numbers. Languages vary here. Numeric data may be further classified as integers, decimals, floating point numbers, or complex numbers.[7]
  • Strings are series of characters. Strings may be fixed in length, like a product ID, or variable in length, like the names of colors.[8]

 

Storing Data

Data is stored in tables made up of columns and rows where the columns are categories, and the rows are individual entries. Collections of data are datasets. A simple example is the wedding registry dataset in Exhibit A. Datasets may stand alone, or make up larger databases, which are organized collections of data.[9] For example, the data in Exhibits A and B could be organized into one larger database. A well-constructed database follows a database schema, which is the empty framework that models out the tables in the database and the fields included in each table.[10]

 

Often, data interacts with other data or media. Database models define the logical structures of databases and how the data within is connected, processed, and stored.[11] Relational databases are the most common.[12] In a relational model, the columns in the tables describe the rows, and tables can refer to data from other tables. In the wedding data examples, couple_id is created in Exhibit B and referred to by Exhibit A.

 

Exhibit A: Wedding Registry Dataset

couple_id product product_type product_maker product_priority
1 waffle_iron cooking cuisinart mid
2 china_set dining wedgewood low
3 vaccuum cleaning dyson high
4 skillet cooking lodge high

 

Exhibit B: Engaged Couples Dataset

couple_id partner_one partner_two wedding_date wedding_city
1 mary matthew 2021-06-10 downton
2 jodie alexandra 2020-10-20 los_angeles
3 billy adam 2020-12-31 manhattan
4 cameron tom 2021-04-01 dallas

 

More Data Terms to Know

  • An Algorithm is a process or set of instructions used to solve a problem or answer a question from data.
  • Analytics is the process of identifying patterns in data to answer questions.[13] Analytics is less sophisticated than data science and typically answers less complex questions. Analytical findings describe, not predict.
  • Big Data, in essence, means lots and lots of data, often compiled from different sources. The sheer scale of the data makes it difficult to avoid uncertainty and inconsistency.[14]
  • Cookies are small pieces of data stored on a user’s computer when that user visits a website. The purpose of cookies is to allow websites to recognize users’ preferences.[15]
  • Cloud means driven by a remote, out-sourced server. When you store photos on Dropbox, Google Photos, or iCloud, you’re utilizing cloud storage.
  • Data Architecture is the overarching term for the governance documentation of an organization’s databases and data systems.[16]
  • A Data Dictionary is documentation associated with a database that catalogs and describes what is in the database. The data dictionary may also give information about the database’s structure and operation.[17]
  • A Data Lake is a repository for raw data from a range of sources. The data remains formatted the way the sources formatted it.[18] The primary purpose of a data lake is to collect and hold large amounts of data.[19]
  • Data Science is advanced statistical analysis and modelling used to make predictions or make data easier to understand.[20]
  • A Data Warehouse is a system of aggregated data used for querying and analyzing data.[21]
  • ETL stands for extract, transform, and load. It is a common process used to change varied data from multiple sources[22] to make it more usable for the context that it is needed for.
  • Machine Learning refers to a process of feeding data to a program or system that will use some of the data you gave it to find patterns and make predictions about the rest of the data.[23] Machine learning models are only as good as their source data—if the source data contains historical bias or is missing important populations, the predictions the model makes will be flawed.
  • Metadata is data about data that furthers how the data can be used and understood.[24] For example, metadata of users who registered for a website might include date and time of registration, whether the user was a referral, and whether the user used their phone to access the site.
  • Open means free to use and distribute without restriction.[25] A foundational idea behind open data is that the sharing and use of open data is subject to an honor-bound social contract.[26]
  • PII stands for personal identifiable information. Data that is directly linked to a person’s identity and data that can be used to ascertain a person’s identity when used with other data can both be considered PII.[27]
  • Visualization is a way to represent information and data. Data visualizations like charts, graphs, infographics, dashboards, and maps can make it easier to understand patterns in data.[28]
  • Web Scraping is the process of taking data from a website and converting it into a more convenient format.[29]

 

[1] See What is the difference between data and information?, Computer Hope, https://www.computerhope.com/issues/ch001629.htm (last updated Aug. 31, 2020).

[2] See What is Data?, School of Data, https://schoolofdata.org/handbook/courses/what-is-data/ (last updated Sept. 2, 2013).

[3] See Computer Hope, supra note 1.

[4]See Character, Computer Hope, https://www.computerhope.com/jargon/c/charact.htm (last updated Apr. 2, 2019).

[5]See Boolean, Computer Hope, https://www.computerhope.com/jargon/b/boolean.htm (last updated May, 16, 2020).

[6] See SQL Data Types for MySQL, SQL Server, and MS Access, W3Schools, https://www.w3schools.com/sql/sql_datatypes.asp.

[7] See id. See also Python Data Types, W3Schools, https://www.w3schools.com/python/python_datatypes.asp; Python Data Types, W3Schools, https://www.w3schools.com/js/js_datatypes.asp.

[8] See W3Schools, supra note 6.

[9] See Database defined, Oracle, https://www.oracle.com/database/what-is-database/.

[10] See What is a Database Schema, Lucidchart, https://www.lucidchart.com/pages/database-diagram/database-schema.

[11] See What is a Database Model, Lucidchart, https://www.lucidchart.com/pages/database-diagram/database-models.

[12] See Lucidchart, supra note 11.

[13] See generally Analytics defined, Oracle, https://www.oracle.com/business-analytics/what-is-analytics/.

[14] See The Four V’s of Big Data, IBM, https://www.ibmbigdatahub.com/sites/default/files/infographic_file/4-Vs-of-big-data.jpg.

[15] See Cookie, PC Mag: Encyclopedia, https://www.pcmag.com/encyclopedia/term/cookie.

[16] See Data Architecture, Snowflake: Data Warehousing Glossary, https://www.snowflake.com/data-warehousing-glossary/data-architecture/.

[17] See Data Dictionaries, USGS, https://www.usgs.gov/products/data-and-tools/data-management/data-dictionaries.

[18] See Data Lake vs Data Warehouse, Snowflake, https://www.snowflake.com/trending/data-lake-vs-data-warehouse.

[19] See Snowflake, supra note 18.

[20] Data Science Terms and Jargon: A Glossary, Dataquest (Feb. 20, 2018), https://www.dataquest.io/blog/data-science-glossary/.

[21] See Data Warehousing, Snowflake: Data Warehousing Glossary, https://www.snowflake.com/data-warehousing-glossary/data-warehousing/.

[22] See zoinerTejada et al., Extract, transform, and load (ETL), GitHub: MicrosoftDocs (Nov. 20, 2019), https://github.com/microsoftdocs/architecture-center/blob/master/docs/data-guide/relational-data/etl.md.

[23] See Machine Learning Glossary, Google Developers, https://developers.google.com/machine-learning/glossary/#m (last updated Aug. 11, 2020).

[24] See Metadata Creation, USGS, https://www.usgs.gov/products/data-and-tools/data-management/metadata-creation.

[25] See What is open?, Open Knowledge Foundation, https://okfn.org/opendata/.

[26] See generally Open Definition 2.1, Open Knowledge Foundation, https://opendefinition.org/od/2.1/en/.

[27] See Guidance on the Protection of Personal Identifiable Information, U.S. Department of Labor https://www.dol.gov/general/ppii.

[28] See Data visualization beginner’s guide: a definition, examples, and learning resources, Tableau, https://www.tableau.com/learn/articles/data-visualization.

[29] See What is Web Scraping and What is it Used For?,ParseHub: Blog (Aug. 6, 2019), https://www.parsehub.com/blog/what-is-web-scraping/.

Image Source: https://www.needpix.com/file_download.php?url=https://storage.needpix.com/rsynced_images/database-schema-1895779_1280.png

Understanding Free Speech in a Social-Media Driven World: the Significance of Section 230

By Ian McDowell

 

Even though modern social media companies such as Facebook, Twitter and others did not exist at the time of passage, the 1996 Communications Decency Act (and specifically, Section 230 of the Act) is properly considered a landmark law in the history of the internet because it allows social media companies to be shielded from liability for what their users post on their platforms, and allows them to remove user-uploaded content, largely at their own discretion. [1]

 

Section 230 has even been referred to as the “twenty-six words that created the Internet”, and was enacted in response to two notable court rulings regarding the internet- in one, a federal court ruled that a host was not liable because it had not moderated user-generated content at all; in the other, a state court had ruled that a tech company could be liable because it had regulated some user content/posts. [2]

 

Under 47 U.S.C. § 230(c)(1), “[n]o provider or user of an interactive computer device shall be treated as the publisher or speaker of any information provided by another information content provider.”  Under § 230(c)(2), the entity is free to “in good faith” restrict access to material that it deems inappropriate, even if that material is constitutionally protected.

 

Because Section 230 provides a total shield from liability (except in special circumstances involving intellectual property issues and involving criminal investigations, etc.) to technology companies such as Facebook or Twitter for the potentially harmful effects of third-party content posted on their respective domains, it could be argued that Section 230 provides greater levels of speech protection to technology companies than the 1st Amendment provides to traditional media outlets, considering that they may still be sued for defamation or libel in response to content that they publish. [3]

 

In the polarized environment of post-2016 American politics, Section 230 has cultivated renewed public scrutiny.  The safeguard provided by Section 230 has created a situation where tech companies such as Twitter or Facebook will inevitably be criticized for either inaction or taking action in response to specific controversial user-uploaded content.  For example, some prominent members of the Democratic Party feel that Section 230 has been a “gift” to the tech industry, or that it gives them an excuse to not get rid of “slime” that is propagated on their website. [4]  By contrast, conservative politicians accuse the same companies of using Section 230 as a means to stifle conservative voices or outlets if they decide to remove or otherwise censor certain content, depending on the uploader or account. [5]

 

Even though the criticism of companies such as Twitter and Facebook on the issue of perceived censorship (or, conversely, the lack of providing any real safeguards against misinformation disseminated on their respective platforms) is extensive, and has at times been done in formal settings such as Senate hearings, some observers feel that criticism that is ostensibly focused on Section 230, may not really about Section 230 at all in certain circumstances.  Rather, Section 230 may be seen just a verbal tool that politicians can use at times in hearings to criticize figures such as Jack Dorsey and try to achieve headlines. [6]

 

However, even if criticism of Section 230 may in turn be critiqued for its sincerity in certain circumstances, the fact remains that both President Trump and President-elect Biden supported its’ repeal (or at least extensive changes) during the 2020 campaign. [7]  While it is unclear what will be achieved by the incoming Biden Administration as it relates to Section 230, President-elect Biden has the overarching goal of requiring that companies such as Facebook or Twitter moderate more content than they are presently doing, which certainly implicates the Act. [8]  Further, changes to how Section 230 is applied may not solely come from an Act of Congress, Justice Thomas for one has urged the Supreme Court to take a case to determine the meaning of Section 230 and what specifically constitutes a distributor vs. a publisher.  [9]

 

[1] Anshu Siripurapu, Trump’s Executive Order: What to Know About Section 230, council on foreign relations (Jun. 4, 2020),  https://www.cfr.org/in-brief/trumps-executive-order-what-know-about-section-230.

[2] Id. 

[3] See Eric Goldman, Why Section 230 is Better Than the First Amendment, 95 Notre Dame L. Rev. Reflection 33, 36-37 (2019).

[4] Siripurapu, supra.

[5] See id.

[6] See Gilad Edelman, Surprise!  The Section 230 Hearing Wasn’t About Section 230, wired (Oct. 28, 2020) https://www.wired.com/story/section-230-hearing-wasnt-about-section-230/.

[7] Matt Perault, Section 230: A Reform Agenda for the Next Administration, day one project , 1(Oct. 26, 2020), https://www.dayoneproject.org/post/section-230-reform

[8] See id. 

[9] See id., see also Mike Godwin, Clarence Thomas is Begging Someone to sue Over Conservatives’ Most Hated Internet Law, slate (Oct. 16, 2020), https://slate.com/technology/2020/10/clarence-thomas-section-230-cda-content-moderation.html.

Image Source: https://www.bloomberg.com/news/articles/2020-08-11/section-230-is-hated-by-both-democrats-and-republicans-for-different-reasons

An Epic Apple Fight

By Drew Apperson

 

At what point does a rent contract overburden retailers in a shopping mall? What if there are only two shopping malls for the entire world? A trial set for mid-2021 may answer this question in the mobile-app context. It will immediately effect over 100 million people in the United States and potentially anyone with a smartphone around the globe.[1] Thankfully, the suit may save consumers money and its anticipation has already triggered a selective discount to begin in January, 2021.[2] Who can be thanked for initiating the suit? The developer of a free gaming app.

 

When the once-popular video game, Snake, was first available on cellphones in 1997, it was only available on select Nokia phones on which it was predownloaded from the factory.[3] Today, Apple offers its users over 278 thousand gaming apps, available for download from its virtual, mobile-app storefront, the App Store.[4] Google’s equivalent, Google Play, offers over 385 thousand.[5] Together, the two account for nearly 100% of the global smartphone market.[6]

 

For an app to be made available on the App Store, a game developer must, among other things, agree to Apple’s rules and meet its standards.[7] The App Store then distributes the apps to Apple devices and continues to profit from the apps by taking a percentage of the sales made therein – 30% during the app’s first year and 15% during subsequent years.[8]Apple requires these purchases to be made via In-App Purchase, which is a tool that “allows [a developer] to offer users the opportunity to purchase in-app content and features. Customers can make the purchases within [the developer’s] app, or directly from the App Store.”[9] In 2019, the App Store reportedly paid out $35 billion to app developers after taking its share.[10] Because of Apple’s massive customer base and successful App Store, these transaction fees have grown Apple into one of the top-five largest gaming companies in the world despite not having made a single game of its own.[11]

 

The rent that Apple charges for a spot in its marketplace, however, has been a topic of discussion for a few popular app developers who initiated antitrust inquiries into Apple’s policy.[12] Fittingly, Epic Games, the developer of Fortnite, has taken charge in the fight.[13] Fortnite is a household name in the gaming world: it is a free-to-play, battle-royale game that offers multi-million-dollar prizes and, through its over 250 million users, brought in $1.8 billion in 2019.[14] On August 13, 2020, in Epic’s strategic approach to voice dismay with Apple, the developer: breached its App Store contract by including in its Fortnite update a workaround to the App Store’s transaction fees; launched an anti-Apple campaign, which included a parody of Apple’s ‘1984’ ad; and filed suit after Apple delisted Fortnite from the App Store.[15] The subsequent lawsuit has drawn widespread attention as Epic has gained support, not just from the gaming-app community, but from other app markets as well.[16] It even gained support from Microsoft as it too has fought with Apple over the introduction of Microsoft games to the App Store.[17]

 

Tim Sweeny, founder and CEO of Epic, argues that Epic’s victory in the lawsuit would allow for consumers to pay less and for developers to earn more.[18] In contrast, Douglas Vetter, Apple’s Vice President and Associate General Counsel, argues that result would undermine the principle of the App Store’s standards – “to provide a safe, secure and reliable experience for users and a great opportunity for all developers to be successful.”[19] The suit may serve as a precedent for the similar suit that Epic filed against Google when Google Play delisted Fortnite for the same workaround.[20] It follows that if it wins both cases, Epic will effectively change the entire smartphone industry.

 

[1] S. O’Dea, iPhone Users as Share of Smartphone Users in the United States 2014-2021, Statistica (Sept. 10, 2020), https://www.statista.com/statistics/236550/percentage-of-us-population-that-own-a-iphone-smartphone (providing statistics of active iPhone users); Hirun Cryer, Here’s Everything We Know About the Epic vs Apple Lawsuit, Games Radar (Nov. 3, 2020), https://www.gamesradar.com/epic-vs-apple-lawsuit (providing anticipated trial date).

[2] Josh Taylor, Apple to Reduce its Cut from In-App Purchases as it Faces New Lawsuit from Fortnite Maker, The Guardian (Nov. 18, 2020), https://www.theguardian.com/technology/2020/nov/18/apple-to-reduce-its-cut-from-in-app-purchases-as-it-faces-new-lawsuit-from-fortnite-maker.

[3] 10 Things You Didn’t Know About Mobile Gaming, Windows (Jan. 16, 2013), https://blogs.windows.com/devices/2013/01/16/10-things-you-didnt-know-about-mobile-gaming-2/#:~:text=One.,called%20the%20Hagenuk%20MT%2D2000.

[4] Christina Gough, Number of Gaming Apps in the Apple App Store from 1st Quarter 2015 to 3rd Quarter 2020, Statistica (Oct. 27, 2020), https://www.statista.com/statistics/780238/number-of-available-gaming-apps-in-the-apple-app-store-quarter/#:~:text=This%20statistic%20gives%20information%20on,compared%20to%20the%20previous%20quarter.

[5] Christina Gough, Number of Gaming Apps at Google Play from 1st Quarter 2015 to 3rd Quarter 2020, Statistica (Oct. 27, 2020), https://www.statista.com/statistics/780229/number-of-available-gaming-apps-in-the-google-play-store-quarter/#:~:text=Google%20Play%3A%20number%20of%20available%20gaming%20apps%20as%20of%20Q3%202020&text=As%20of%20the%20third%20quarter,compared%20to%20the%20previous%20quarter.

[6] Jason Cohen, iOS More Popular in Japan and US, Android Dominates in China and India, PCMag (Sept. 4, 2020), https://www.pcmag.com/news/ios-more-popular-in-japan-and-us-android-dominates-in-china-and-india.

[7] App Store Review Guidelines, Developer, Apple (Sept. 11, 2020), https://developer.apple.com/app-store/review/guidelines/#reader-apps.

[8] Julia Alexander, A Guide to Platform Fees, The Verge (Sept. 22, 2020, 8:05 AM), https://www.theverge.com/21445923/platform-fees-apps-games-business-marketplace-apple-google.

[9] In-App Purchase, Developer Documentation, Apple (last visited Nov. 28, 2020, 6:00 PM), https://developer.apple.com/documentation/storekit/in-app_purchase.

[10] Kif Leswing, Apple’s App Store Had Gross Sales Around $50 Billion Last Year, But Growth is Slowing, CNBC (Jan. 8, 2020, 12:30 PM), https://www.cnbc.com/2020/01/07/apple-app-store-had-estimated-gross-sales-of-50-billion-in-2019.html.

[11] Ross Krasner, Apple v. Epic Lawsuit Could Open Door to Third-Party Payments — Led by Esports, Venture Beat (Nov. 3, 2020, 7:17 AM), https://venturebeat.com/2020/11/03/apple-v-epic-lawsuit-could-open-door-to-third-party-payments-led-by-esports.

[12] Isobel Asher Hamilton, Apple Just Got Hit with 2 EU Antitrust Probes into the App Store and Apple Pay, Bus. Insider (June 16, 2020, 6:29 AM),  https://www.businessinsider.com/apple-two-eu-competition-investigations-2020-6?r=US&IR=T.

[13] Erin Griffith, Apple and Epic Games Spar Over Returning Fortnite to the App Store, NY Times (Nov. 18, 2020), https://www.nytimes.com/2020/09/28/technology/apple-epic-app-court.html.

[14] Akhilesh Ganti, How Does Fortnite Make Money, Investopedia (Sept. 10, 2020), https://www.investopedia.com/tech/how-does-fortnite-make-money/#:~:text=In%202019%2C%20Fortnite%20brought%20in%20revenues%20of%20%241.8%20billion%2C%20according,to%20250%20million%20Fortnite%20players.

[15] Epic Games v. Apple Inc., No. 4:20-cv-05640-YGR, 2020 U.S. Dist. LEXIS 188668, at *12-14 (N.D. Cal. Oct. 9, 2020).

[16] Griffith, supra note 13.

[17] Cryer, supra note 1.

[18] Epic Games, Inc. v. Apple Inc., at *10.

[19] Id. at *11.

[20] Epic Games, Inc. v. Google LLC et al, No. 3:20-cv-05671-JD (N.D. Cal. filed Aug. 13, 2020).

Image Source: https://search.creativecommons.org/photos/5dcefd42-cce2-4c84-b71a-e0ce4f315a5b

Predictive Policing

By Jeffrey A. Phaup

 

In September of 2020 a Tampa Bay Times investigative report brought to light a policing operation in Pasco County Florida.[1] The county’s Sheriff’s Department had deployed mass monitoring, targeted intimidation, and harassment tactics against selected families and individuals for years based on the implementation of a questionably designed algorithm that relied on dubious data and arbitrary decisions.[2]

 

There are two broad types of predictive policing tools.[3] The first are location-based algorithms that draw on links between places, events, and historical crime rates to predict where and when crimes are more likely to happen.[4] The second type are tools that draw on data about individuals, such as age, gender, marital status, substance abuse history, and criminal records.[5] This second type of tool is used both by police in attempts to intervene before crimes take place, and by the court system  to make determinations about pretrial release and sentencing; where a person’s score is used to quantify how likely they are to be rearrested if released.[6]

 

In Pasco County, the algorithm assigned targets utilizing scores based on individuals’ criminal records, including charges that were later dropped.[7] Once the algorithm identified individuals it considered at risk of committing more crimes, Deputies were then instructed to visit these individuals’ homes and make arrests for any reason they could.[8]These arrests in turn would then be fed back into the algorithm, creating a feedback loop and leading to increased targeting of select individuals by the algorithm.[9]

 

The Tampa Bay Times report highlights the dangers of algorithm-based policing, also known as predictive policing.[10]While proponents of the practice argue that algorithm-based policing can help predict crimes more accurately and effectively than traditional police methods do, critics have raised concerns about transparency and accountability.[11]There is also an underlying danger that lies with the data the algorithms feed upon.[12] Bias that may be baked into the algorithms themselves, as they rely on historical data produced by biased decisions and consequences.[13]

 

According to the US Department of Justice a person is more then twice as likely to be arrested if they are Black then if they are White.[14] A Black person is also five times more likely to be stopped without just cause then a White person is.[15] These arrests in turn would then be fed back into the algorithm, creating a feedback loop and leading to increased targeting of select individuals by the algorithm.[16]

 

The Constitution protects the right of individuals to be “secure in their persons, houses, papers, and effects, against unreasonable searches and seizures.”[17] Under the Fourth Amendment individuals are afforded a subjectively reasonable expectation of privacy that society is prepared to recognize as objectively reasonable.[18] Thus, in the United States, the standard generally requires probable cause or a warrant for any search or seizure.[19] However, none of the determinations produced by predictive policing programs rise to the legal standard of a Fourth Amendment search or seizure, so their use by the police does not require probable cause or a warrant.[20]

 

Data analytics is increasingly a part of the way society operates. However, predictive policing algorithms come with considerable risks to individuals’ privacy and rights. Substituting inherently biased or otherwise flawed algorithmic predictions for prior investigative techniques risks skewing the judgment of law enforcement officials; resulting in arbitrary and discriminatory stops, searches, and arrests. While police departments may view algorithms as a way to replace possible prejudicial human judgment, doing so with an algorithm that conceals and embodies those same prejudices is not a viable solution to the problem at hand.

 

[1] Kathleen McGrory, Neil Bedi & Douglas R. Clifford, A futuristic data policing program is harassing Pasco County families Pasco’s sheriff created a futuristic program to stop crime before it happens. It monitors and harasses families. | Investigations | Tampa Bay Times(2020), https://projects.tampabay.com/projects/2020/investigations/police-pasco-sheriff-targeted/intelligence-led-policing/ (last visited Nov 11, 2020).

[2] Id.

[3] Will Douglas Heaven, Predictive policing algorithms are racist. They need to be dismantled. MIT Technology Review (2020), https://www.technologyreview.com/2020/07/17/1005396/predictive-policing-algorithms-racist-dismantled-machine-learning-bias-criminal-justice/ (last visited Nov 15, 2020).

[4] Id.

[5] Id.

[6] Id.

[7]  McGrory, Bedi & Clifford, supra note 1.

[8]  Id.

[9]  Id.

[10] Tim Lau, Predictive Policing Explained Brennan Center for Justice (2020), https://www.brennancenter.org/our-work/research-reports/predictive-policing-explained (last visited Nov 15, 2020).

[11]  Id.

[12] McGrory, Bedi & Clifford, supra note 1.

[13]  Lau, supra note 9.

[14] Heaven, supra note 2.

[15] Id.

[16]  McGrory, Bedi & Clifford, supra note 1.

[17] U.S. CONST. amend. IV.

[18] See Katz v. United States, 389 U.S. 347, 361 (1967) (Harlan, J., concurring) (establishing the reasonable expectation of privacy test).

[19] See, e.g., United States v. Ross, 456 U.S. 798, 824–25 (1982) (quoting Mincey v. Arizona, 437 U.S. 385, 390 (1978)).

[20] Lindsey Barrett, REASONABLY SUSPICIOUS ALGORITHMS: PREDICTIVE POLICING AT THE UNITED STATES BORDER, 41 N.Y.U. Review of Law & Social Change 327, 329 (2018).

Image Source: https://croga.org/how-to-reduce-crime-rates-without-gun-control/

Big Tech Under Fire

By Anna Hargett

 

Initial Congressional investigations of the Big Tech companies (Google, Facebook, Apple, and Amazon) started off as a bipartisan effort, with Democrats focusing on the antitrust issues and Republicans targeting bias concerns. [1] However, recent Congressional hearings evidence the partisan divisions that have delayed solutions to the issues.[2]

 

As Democrats work to overhaul antitrust laws that have not materially changed in 50 years, Republicans on the House Judiciary Committee withdrew support from a report that did not include recognition of the conservative anti-bias claims. [3] Republicans did not favor the increase in regulations for the antitrust overhaul amidst fear that further regulations could hamper innovation.[4]

 

Although the report did not receive support from the Republican House Judiciary Committee members, the Democratic staffers published the findings in a 449-page report. Upon release, the stock market reacted neutrally if not positively, with three out of the four companies’ mentioned stock prices rising the day after the release.[5] Overall market sentiment points to Big Tech’s power remaining unchecked.[6]

 

Aside from antitrust issues, Congress recently sparred with Big Tech executives over legal liability protection from their content moderation decisions, located in Section 230.[7] Republicans claim that the shield covers the firms from claims of bias by over-policing conservative posts, while Democrats state that the executives are not doing enough to “remove harmful content.”[8] Both parties hope to rework Section 230 to also include a national digital privacy law.[9]

 

Despite the bipartisan efforts that persisted throughout the summer, this hearing ultimately let executives off the hook without any solution or reprimand. The executives requested a “clearer roadmap” from the Congressional leaders.[10]Instead of focusing on policy efforts, the lawmakers attacked each other through questions posed to the Big Tech executives.[11] Ultimately, the hearing allowed the public to observe the executives squirm under pressure and offer meager promises of increasing transparency in the future.[12]

 

In addition to issues of antitrust and censorship, Big Tech has also faced scrutiny during this election season in reaction to its political advertising rules.[13] Both campaigns spent hundreds of millions of dollars on Facebook and Google ads, even though Facebook stated in September that it would not run political advertising during the week of the election.[14]While attempting to regulate the political ads according to its policies, Facebook inadvertently blocked advertising created by both of the candidates’ campaigns.[15] Biden’s campaign claimed that the glitch interrupted about $500,000 worth of ads, while Trump’s campaign claimed that the platform was specifically blocking his ads to target the President.[16] In response to this malfunction, Facebook maintained that it encountered “unanticipated issues” while enforcing its new policy. [17]

 

While Congress attempts to implement change in Big Tech, whether in antitrust, content policies, or privacy, these changes will slowly evolve before lasting solutions are found.[18] These hearings remind us that Congressional hearings are not the endgame, but simply get the ball rolling in terms of awareness.[19] Further, glitches in the Facebook political advertising policy demonstrate that even the tech companies themselves will struggle to control their own platforms.[20]  Ultimately, analysts believe that lasting change will be seen via regulatory action and through the judicial system.[21]

 

[1] David McCabe and Cecilia Kang, Republicans Focused on Bias Concerns About Platforms, The New York Times (July 29, 2020), https://www.nytimes.com/live/2020/07/29/technology/tech-ceos-hearing-testimony/republicans-focused-on-bias-concerns-about-platforms.

[2] Cecilia Kang and David McCabe, Big Tech Was Their Enemy, Until Partisanship Fractured Battle Plans, The New York Times (Oct. 6, 2020), https://nyti.ms/3d3NJpn.

[3] Id.

[4] Id.

[5] Ari Levy, et al., How Big Tech Became Such a Target on Capitol Hill, CNBC (Oct. 10, 2020), https://www.cnbc.com/2020/10/10/how-big-tech-became-such-a-big-target-on-capitol-hill.html.

[6] Id.

[7] Brian Fung, Analysis: Two Words Describe the Senate’s Latest Big Tech hearing: Worthless and Petty, CNN Business (Oct. 28, 2020), https://www.cnn.com/2020/10/28/tech/big-tech-section-230-hearing-analysis/index.html; 47 U.S.C. § 230 (2019).

[8] Lauren Feiner, Big Tech CEO Senate Hearing Ends with Little Discussion on How to Fix Companies’ Liability Shield, CNBC (Oct. 28, 2020),https://www.cnbc.com/2020/10/28/facebook-google-and-twitter-ceos-testify-in-congress-over-section-230-live-updates.html.

[9] Id.

[10] Id.

[11] Fung, supra, note 7.

[12] Id.

[13] Michael Cogley, Big Tech’s ‘Uneven’ and Confusing Political Advertising Rules Under Fire, The Telegraph, (Nov. 3, 2020), https://www.telegraph.co.uk/technology/2020/11/03/big-techs-uneven-confusing-political-advertising-rules-fire/.

[14] Id.

[15] Id.

[16] Id.

[17] Id.

[18] See Cecilia Kang and David McCabe, supra, note 2.

[19] Fung, supra, note 7.

[20] See Michael Cogley, supra, note 13.

[21] Id.

Image Source: https://www.nytimes.com/live/2020/07/29/technology/tech-ceos-hearing-testimony#there-are-many-investigations-into-the-tech-companies-heres-where-they-all-stand

Voting Machines: Bought with Flaws, Used with Flaws

By Drew Apperson

 

As of the president-elect’s November 7, 2020 victory speech, an estimated 159 million ballots had been counted, and the percentage of voter turnout among those eligible was predicted to be the highest in over a century.[1] But how does this align with the significant percentage of American voters who lack confidence in the security of our most advanced voting systems to date?[2] After all, “the probability of voter confidence is significantly affected by the voting-system’s technology.”[3] Was there a vast security overhaul since the last presidential election – the one where U.S. intelligence agencies found that Russian hackers gained access to voting systems throughout the country?[4] Implementing the latest technology is not necessarily the best solution when it is not even clear if the technology is safe.

 

Following the 2000 presidential election’s “hanging chad” controversy,[5] Congress enacted the Help America Vote Act of 2002 (“HAVA”) to replace punch-out ballots with computerized voting systems.[6] HAVA also created the Election Assistance Commission (“EAC”) to administer the federal incentives to participating states and hold them to a federal standard.[7] “HAVA does not require any particular voting system, but it sets requirements that influence what systems election officials choose.”[8]Most voting systems use optical-scan machines (“OSMs”) that scan and record paper ballots that are either filled out by hand or created via ballot-marking devices (“BMDs”).[9] On a basic BMD, voters make selections on an electronic interface, e.g. a touch screen, then the BMD prints out a paper ballot with the corresponding selections.[10]  The physical ballots create a paper trail for auditing and allow voters to verify their ballot’s accuracy prior to submitting it, even though it has been found that most voters never notice errors, if they verify the ballot at all.[11] The other option is direct-recording electronic machines (“DREs”), which are essentially BMDs, but instead of printing a ballot, they digitally record selections via their software.[12] Some DREs offer a voter-verified paper audit trail (“VVPAT”) option – a running paper scroll that is used for auditing the electronic record.[13]

 

Leading up to HAVA’s enactment, experts warned of potential problems of an exclusively electronic voting software (like that found in DREs) in the hands of private companies and insisted on corresponding physical, non-computer-generated copies of ballots as a paper trail for auditing.[14] DREs were known to be hackable to correctly display the voter’s selection on the screen and printed on the VVPAT, yet record a different selection in the system’s software where the ballot is officially recorded.[15]Furthermore, BMDs and OSMs, besides the occasional improper (if not malicious) calibration to record inaccurate selections, could also be hacked to record extra votes or none at all.[16] However, legislatures chose not to incorporate the paper trail requirement or higher software standards to prevent hacking.[17] Because the few manufacturers keep their software closely guarded and HAVA requires only minimally intrusive machine testing, it was not until four years after HAVA’s enactment that a DRE was made accessible to an outside research group for a security analysis – the machine was donated by an anonymous source.[18]

 

Upon its creation, the EAC subsequently reported “an unprecedented surge in the acquisition of voting systems across this country” from 2002 to 2005.[19] Georgia was one of the first major participants in HAVA and contracted some $54-million to implement an entirely paperless network of DRE’s without VVPAT.[20] The state then “scrambled to get machines into place” for its governor race that resulted in the first political party change in 130 years.[21] A former contractor for Georgia’s sole supplier later shared accounts of continuous software issues and implementation of software patches that were never examined by the state officials or testing lab.[22] Furthermore, an election-integrity activist found unsecured online access to the company’s server through which it distributed software patches throughout the state.[23] The server held around 40,000 files including its voting machines’ source code and the company-wide password to voting records and audit logs – “the most critical data on a voting system.”[24]

 

One might think that the technology was implemented too fast and the kinks have been worked out over time. Yet, there was the 2016 presidential election for which the Senate Select Committee on Intelligence released a report that reverberated the same concerns originally voiced before HAVA and advertised what widespread vulnerability still existed in our voting technology’s security.[25] Midterm elections in Georgia just two years ago still suffered. The same machines they bought in 2002 were not only still in use, but were still causing issues including reports of voter’s selections being altered. [26]

 

In response, Georgia, again, dropped a heavy investment – over $100 million – on a state-wide replacement using the latest-technology voting machines; again, rushed them in before an election; and again, experienced software issues across the state.[27]However, Georgia’s audit of the recent 2020 election seems to affirm the state’s trust in their investment, despite thousands of ballots initially missed.[28] If the same percentage of missed Georgian votes occurred across all 50 states, there would be over 200,000 voters who would not be heard.[29]

 

Nineteen years ago, experts were “concerned about the secretive and proprietary treatment of tabulation software of all[-]electronic voting. The fraud that occurs one ballot at a time . . . accumulates slowly like grains of sand on a scale.”[30] Today, states updating their voting technology must understand that those concerns still exist.

 

[1] Camila Domonoske & Barbar Sprunt, ‘A Victory for We The People’: Biden Addresses Nation as President-Elect, NPR (Nov. 7, 2020, 9:25 PM), https://www.npr.org/sections/live-updates-2020-election-results/2020/11/07/932104693/biden-to-make-victory-speech-as-president-elect-at-8-p-m-et; Olivia Waxman, The 2020 Election Set a Record for Voter Turnout. But Why is it Normal for so Many Americans to Sit Out Elections?, Time (Nov. 5, 2020, 9:24 AM), https://time.com/5907062/record-turnout-history.

[2] R. Michael Alvarez et al., Are Americans Confident Their Ballots are Counted?, 70 J. Politics 754, 754 (2008).

[3] Id. at 764.

[4] Staff of S. Select Comm. on Intelligence, 116th Cong., Rep. on Russian Active Measures Campaigns and Interference in the 2016 U.S. Election, Volume 1: Russian Efforts Against Election Infrastructure with Additional Views (2018) [hereinafter Intelligence Report].

[5] Lesley Kenney, How the 2000 Election Came Down to a Supreme Court Decision, History (Nov. 4, 2020), https://www.history.com/news/2000-election-bush-gore-votes-supreme-court.

[6] Help America Vote Act of 2002 (HAVA), Pub. L. No. 107-252, 116 Stat. 1666 (codified as amended at 52 U.S.C. § 20901–21145).

[7] Help America Vote Act, U.S. Election Assistance Commission, https://www.eac.gov/about_the_eac/help_america_vote_act.aspx#:~:text=HAVA%20was%20passed%20by%20the,identified%20following%20the%202000%20election (last visited Nov. 20, 2020) [hereinafter EAC Website].

[8] Arthur L. Burris & Eric A. Fischer, Cong. Rsch. Serv., RS20898, The Help America Vote Act and Election Administration: Overview and Selected Issues for the 2016 Election 5 (2016).

[9] Id.

[10] J. Alex Halderman & Mathew Bernhard, Not Enough Voters Detecting Ballot Errors and Potential Hacks, Study Finds, The Michigan Engineer (Jan. 8, 2020).

[11] Id.

[12] Kim Zetter, The Crisis of Election Security, N.Y. Times (Sep. 26, 2018), https://nyti.ms/2N3hoAh; Burris, supra note 8.

[13] Zetter, supra note 12.

[14] Improving Voting Technologies: The Role of Standards: Hearing Before H. Comm. Science, 107th Cong. 29–34 (2001) [hereinafter Hearing] (statements of: Dr. Steve Ansolabehere, Professor, MIT and Director, Caltech-MIT Voting Project; & Dr. Rebecca Mercuri, Assistant Professor of Computer Sciences, Bryn Mawr College and President, Notable Software, Inc.); Voting Tech. Project, Voting – What is, What Could Be, The Caltech/MIT Voting Tech. Project 45–47 (July 1, 2001),  https://vote.caltech.edu/reports/1.

[15] Hearing, supra note 14, at 31.

[16] Zetter, supra note 12.

[17] Help America Vote Act of 2002 (HAVA), Pub. L. No. 107-252, 116 Stat. 1666 (codified as amended at 52 U.S.C. § 20901–21145).

[18] Jen Schwartz, The Vulnerabilities of Our Voting Machines, Sci. Am.: Electronics (Nov. 1, 2018), https://www.scientificamerican.com/article/the-vulnerabilities-of-our-voting-machines/?print=true.

[19] EAC Website, supra note 7.

[20] Zetter, supra note 12.

[21] Id.

[22] Id.

[23] Id.

[24] Id.

[25] Intelligence Report, supra note 4.

[26] Christina A Cassidy & Michael Liedtke, Midterm Voting Exposes Growing Problem of Aging Machines, Associated Press (Nov. 7, 2018), https://apnews.com/article/e1de7029cf2246ab97697ddf8f52d018.

[27] Id.

[28] Historic First Statewide Audit of Paper Ballots Upholds Result of Presidential Race, Georgia Secretary of State, https://sos.ga.gov/index.php/elections/historic_first_statewide_audit_of_paper_ballots_upholds_result_of_presidential_race (last visited Nov. 20, 2020) (statement of Georgia Secretary of State Brad Raffensperger) (“Georgia’s historic first statewide audit reaffirmed that the state’s new secure paper ballot voting system accurately counted and reported results.”); County Summary Data, Georgia Secretary of State, https://sos.ga.gov/admin/uploads/county-summary-data.pdf (last visited Nov. 20, 2020).

[29] 6,320/5,000,585 = 0.1264% error. 158,895,790 *0.1264% = 200,821. County Summary Data, supra note 28 (providing audit tabulation that voting machines recorded 1,058 extra votes and missed 6,320 votes out of the audited total of 5,000,585 votes); Michael P. McDonald, 2020 November General Election Turnout Rates, U.S. Elections Projects (Nov. 16, 2020), http://www.electproject.org/2020g (providing estimate of 158,895,790 total ballots counted, not including Washington, D.C.);

[30] Voting Tech. Project, Voting – What is, What Could Be, The Caltech/MIT Voting Tech. Project 10 (July 1, 2001),  https://vote.caltech.edu/reports/1.

Image Source: https://search.creativecommons.org/photos/9aabd091-9aea-42c2-aa45-422122eb8332

Wayfair, You’ve Got Just What I Need? Wayfair Accusation of Involvement in Human Trafficking Ring

By Amanda Short

 

Wayfair is an e-commerce store that specializes in the sale of furniture, home goods, and human trafficking victims?[1] In July of 2020, a conspiracy theory created by QAnon began to spread like rapid-fire over social media.[2] For those of you unfamiliar with QAnon, this group spreads “conspiracy theories that allege, falsely, that the world is run by a cabal of Satan-worshiping pedophiles who are plotting against Mr. Trump while operating a global child sex-trafficking ring.”[3]QAnon generated a conspiracy theory that Wayfair was engaged in a human trafficking scheme by advertising items such as industrial storage cabinets and throw pillows as a ruse for selling children.[4]

 

The Wayfair human trafficking scheme centered around higher-priced items named after missing children.[5] Wayfair responded to claims of human trafficking by stating that the industrial cabinets were priced accordingly, but the pricing of pillows at $9,999 was an error made by the company.[6] There was no evidence found to substantiate this conspiracy of a human trafficking ring by Wayfair, and alleged victims of the scheme came forward to notify the public they were indeed safe.[7]

 

Unfortunately, conspiracy themes of this nature skew the public opinion as to what human trafficking is. Human trafficking is the exploitation of persons for labor, services, or commercial sex.[8] Human trafficking reports have occurred in every state in the United States [9] with a disproportionate effect on children and women.[10] According to the Polaris Project, many misconceptions surround the crime of human trafficking.[11] These misconceptions of human trafficking lead to a diversion in resources and a hindrance to ending the crime.[12]

 

The fantasized images of human trafficking portrayed through conspiracy theories and blockbuster movies lead to a misunderstanding of what human trafficking is.[13] Many may recall the movie Taken when thinking about human trafficking.[14] In Taken, Liam Nielson portrayed a former FBI agent forced to spring action when his daughter was kidnapped by a human trafficking ring.[15] Movies of this nature perpetuate ideas that human trafficking victims are always taken by force, and victims are restrained against their will.[16]

 

The Wayfair human trafficking conspiracy spread misinformation and directly impacted resources for victims.[17]According to the Polaris Project, the National Human Trafficking Hotline became overrun with callers claiming Wayfair was selling children.[18] The hotline received hundreds of phone calls about the Wayfair conspiracy theory from the internet, which diverted support and attention to real reports of human trafficking.[19] None of the incoming phone calls from worried persons included information beyond what was shared online.[20]

 

Some websites have been accused and found to facilitate human trafficking, but Wayfair is not one of them. Backpage.com was a classified ad website that included an adult section for personal ads.[21] In 2018, Backpage.com was seized by federal authorities following CEO Carl Ferrer’s plea deal on charges of facilitating prostitution and money laundering.[22] Backpage.com facilitated the sale of children for sexual acts by overlooking code words used to indicate the sale of children, such as ads containing “Amber Alert.”[23] Backpage.com profited immensely from the sale of persons, as indicated by the 129.7 million dollar increase in revenues from 2008 to 2014 with 90% of the increase of earnings from the adult ads.[24]

 

The internet has brought on an era of immediate access to information, but with this instant information comes misinformation. To learn more about human trafficking, check out the resources from the Polaris Project at https://polarisproject.org.

 

[1] See On Wayfair and the Problem with Sex Trafficking Conspiracy Theories, End Sexual Exploitation (July 16, 2020), https://endsexualexploitation.org/articles/on-wayfair-and-viral-conspiracy-theories-about-sex-trafficking/.

[2] See id.

[3] Kevin Roose, What is QAnon, the Viral Pro-Trump Conspiracy Theory, N.Y. Post (Oct. 19, 2020), https://www.nytimes.com/article/what-is-qanon.html.

[4] See On Wayfair and the Problem with Sex Trafficking Conspiracy Theories, supra note 1.

[5] See Amanda Seitz & Ali Swenson, Baseless Wayfair Child-Trafficking Theory Spreads Online, Wash. Post (July 16, 2020, 5:26 PM), https://www.washingtonpost.com/business/technology/baseless-wayfair-child-trafficking-theory-spreads-online/2020/07/16/f5206c10-c7aa-11ea-a825-8722004e4150_story.html.

[6] See id.

[7] See id.

[8] See Human Trafficking, U.S. Dep’t of Just., https://www.justice.gov/humantrafficking (last visited Nov. 13, 2020).

[9] See 2019 U.S. National Human Trafficking Hotline Statistics, Polaris, https://polarisproject.org/2019-us-national-human-trafficking-hotline-statistics/ (last visited Nov. 13, 2020).

[10] See id.

[11] Common Myths and Misconceptions About Human Trafficking in the U.S., Polaris, https://humantraffickinghotline.org/sites/default/files/Common%20Myths%20and%20Misconceptions.pdf (last visited Nov. 13, 2020).

[12] Human Trafficking in the Movies, Stop The Traffik (Oct. 2, 2020), https://www.stopthetraffik.org/movies/#:~:text=When%20people%20think%20of%20human,trafficking%20ring%20while%20on%20holiday.

[13] See id.

[14] See id.

[15] See id.

[16] See id.

[17] See Polaris Statement on Wayfair Sex Trafficking Claims, Polaris, https://polarisproject.org/press-releases/polaris-statement-on-wayfair-sex-trafficking-claims/ (last visited Nov. 13, 2020).

[18] See id.

[19] See id.

[20] See id.

[21] See Tom Jackson, Backpage CEO Carl Ferrer Pleads Guilty in Three States, Agrees to Testify Against Other Website Officials, Wash. Post (April 13, 2018, 6:00 AM), https://www.washingtonpost.com/news/true-crime/wp/2018/04/13/backpage-ceo-carl-ferrer-pleads-guilty-in-three-states-agrees-to-testify-against-other-website-officials/.

[22] See id.

[23] See Charlie Savage & Timothy Williams, U.S. Seizes Backpage.com, a Site Accused of Enabling Prostitution, N.Y. Times (Apr. 17, 2018), https://www.nytimes.com/2018/04/07/us/politics/backpage-prostitution-classified.html.

[24] See id.

Image Source: https://apnews.com/article/9d54570ebba5e406667c38cb29522ec6

E-Commerce… What Protections Exists?

By Eleni Poulos

 

Websites such as eBay and Amazon have allowed online shopping to flourish in the last ten years. With COVID-19 and quarantines across the United States and the world, e-commerce has only continued to increase tenfold.[1] But with an increase in e-commerce, what protections are afforded to consumers and what can producers be held liable for?

 

Protections for consumers were first introduced in the 1980s, as members of Congress became increasingly concerned with the lack of governance on the issue.[2] As a response for their growing concerns, the Computer Fraud and Abuse Act was enacted, which “prohibits anyone from accessing a computer or computer network without the owner’s consent.”[3]As internet presence increased, the legislation was extended to protect against e-commerce fraud, as well.[4]

 

Additionally, the Restore Online Shoppers’ Confidence Act (ROSCA) was passed in 2010.[5] A main feature of the legislation protects the consumer from third-party payment processors from selling online shoppers’ information.[6] The legislation also protects consumers from unknowingly signing up for a subscription or reoccurring charge by requiring a company to disclose all material terms of the transaction” before consumers are required to submit billing information.[7]Furthermore, other federal laws include the Fair Credit Billing Act, which allows consumers to dispute any charges that were never made, charges that were incorrect, or goods that were not delivered.[8] Federal law further requires that all items ordered online be shipped within 30 days.[9] The Electronic Signatures in Global and National Commerce Act was passed with the goal of ensuring consumers have provided “adequate consent to an electronic transaction.”[10]

 

In addition to legislation from Congress, the Federal Trade Commission and the Federal Communications Commission have worked together to provide protections for online consumers.[11] The “Restoring Internet Freedom” Order, signed during Trump’s presidency, essentially eliminates net neutrality policy passed during Obama’s presidency.[12] However, in general, regulation has seen a decrease during Trump’s presidency.[13]

 

In addition to general consumer laws, states have passed legislation more specifically directed towards online purchases by consumers.[14] In 2007, Governor Eliot Spitzer of New York signed a law that gives consumers who purchase items online the same protections had they made those purchases over the phone or through the mail.[15] Some of these protections include the consumers’ ability to cancel orders if the products are not shipped within the required time frame and the producers’ requirement to keep all complaints from consumers.[16]

 

Courts have also furthered protections for the consumers should there be a product defect in a consumer’s purchase. More recently, in California, the court held that an online retailer could be held strictly liable “for the defects in third-party product sold on its website.”[17] Previously, online retailers were shielded from liability claims brought by consumers of third-party products.[18] This issue has expanded to several other states including Pennsylvania, Arizona, and Tennessee.[19]

 

As the world continues to evolve into a more technologically connected world, it is more important than ever for consumers to have under the protections they received from both Congress and the courts, as well as for producers to understand where they may be susceptible to liability.

 

[1] Problems With Online Shopping, Find Law (Mar. 31, 2020), https://www.digitalcommerce360.com/article/coronavirus-impact-online-retail/.

[2] Online Consumer Protection In E-Commerce Transactions – Module 3 of 5, Law Shelf https://lawshelf.com/videocoursesmoduleview/online-consumer-protection-in-e-commerce-transactions-module-3-of-5/.

[3] Id.

[4] Id.

[5] Id.

[6] Id.

[7] Id.

[8] Fair Credit Billing Act: What Is It And What Do You Need To Know?, Lexington Law (Nov. 21, 2019),

https://www.lexingtonlaw.com/credit/fair-credit-billing-act#:~:text=The%20Fair%20Credit%20Billing%20Act,and%20undelivered%20goods%20or%20services.

[9] 16 C.F.R. § 435.2 (2014).

[10] Laws Pertaining To Commerce On The Internet, Stimmel Law https://www.stimmel-law.com/en/articles/laws-pertaining-commerce-internet (last visited Oct. 29, 2020).

[11] Online Consumer Protection in E-Commerce Transactions – Module 3 of 5, Law Shelf https://lawshelf.com/videocoursesmoduleview/online-consumer-protection-in-e-commerce-transactions-module-3-of-5/.

[12] Id.

[13] Id.

[14] Laws Pertaining To Commerce On The Internet, Stimmel Law https://www.stimmel-law.com/en/articles/laws-pertaining-commerce-internet (last visited Oct. 29, 2020).

[15] Linda Rosencrance, U.S. State Law Protects Consumers Buying Online, PC World (Jun. 8, 2007), https://www.pcworld.com/article/132733/article.html

[16] See id.

[17] Ryan S. Landis, Crack in the Dam that Shields Online Retail Platforms from Liability for Defective Products from Third-Parties, 10 Nat’l. L. Rev. (Aug. 28, 2020),

https://www.natlawreview.com/article/crack-dam-shields-online-retail-platforms-liability-defective-products-third-parties.

[18] Id.

[19] Id.

Image Source: https://search.creativecommons.org/photos/388e1407-5c17-48ed-98f9-f95b3f7d1f3a

Confronting Racial Disparities in Coronavirus Vaccine Clinical Trials

By Chloe Hillard

 

Coronavirus has ravaged our country. More than eight million Americans have been infected and over 217,000 Americans have died due to coronavirus.[1] Although some call the pandemic “the great equalizer”[2], statistics show that’s a myth. The coronavirus is killing Black and Latinx Americans at staggeringly high rates compared to white Americans. There’s nothing equal about the destruction caused of coronavirus. Unfortunately, nothing seems equal about the solution either. Early studies show that although Black and Latinx Americans are disproportionately impacted by the coronavirus, they are severely underrepresented in vaccine clinical trials.[3]

 

Racial disparities in coronavirus infection and mortality are largely due to structural racism.[4] Black Americans have higher rates of chronic conditions, often lack access to quality health care, and disproportionately hold essential frontline jobs that increase virus exposure.[5] Similarly, Latinx Americans disproportionately hold jobs that increase virus exposure, and often live in multi-generational households where virus transmission is easier.[6]

 

The result of such structural racism is staggering. Earlier this year, The New York Times filed a Freedom of Information Act lawsuit against the Centers for Disease Control and Prevention (CDC) to force the CDC to release racial and ethnic data on the coronavirus.[7] The Times investigation found that Black and Latinx Americans are three times more likely to become infected than their white counterparts.[8] Black and Latinx Americans are also twice as likely to die from coronavirus.[9]

 

Despite being disproportionately harmed by coronavirus, Black and Latinx Americans are severely underrepresented in vaccine clinical trials.[10] For example, only 20% of patients in the placebo-controlled Adaptive Covid-19 Treatment Trial (ACTT-1) were Black and only 11% of the patients randomly assigned to 5 or 10 days of remdesivir in the Gilead-funded study were Black.[11] The percent of Latinx patients in the Gilead study were not reported, but only 23% of patients in the ACTT-1 study were Latinx.[12]

 

Minority representation in clinical trials is important because drug efficacy is known to vary by race.[13] Furthermore, it is important to include high-risk patients in clinical trials, because high-risk patients are more likely to be treated by the medical product.[14]

 

Unfortunately, underrepresentation of minorities in clinical trials is not a new problem. For example, even though Black Americans are disproportionately affected by heart conditions, the Food and Drug Administration (FDA) found that only 2.5% of cardiovascular clinical trial participants were Black.[15] In addition, in 2019 drug trials, only 9% of trial participants were Black and 18% were Latinx.[16] These inequities persist despite National Institute of Health (NIH) and FDA policies demanding minority inclusion in clinical trials. The NIH mandates inclusion of minorities in NIH-funded research.[17] NIH-defined phase 3 clinical trials are also required to report outcomes stratified according to sex or gender and according to race and ethnicity, unless data suggests that differences are unlikely to be observed.[18]  Furthermore, FDA regulations require product developers who submit applications for medical products to analyze clinical trial data by sex, age, and race.[19]

 

Clinical trials for the coronavirus vaccine illustrate that NIH and FDA regulations have not achieved their intended outcome. However, regulatory regimes cannot be a cure-all for lack of diversity in clinical trials. Just as racial disparities in infection and mortality rates have roots in structural racism, so do disparities in clinical trials. For example, minority scientists are often underrepresented when it comes to funding.[20] In addition, there’s a history of mistrust of clinical trials, born from abusive research practices such as the Tuskegee syphilis study.[21] Such distrust makes it difficult to enroll minority participants in trials.[22] These impediments must be addressed in any solution for lack of diversity in coronavirus vaccine clinical trials. The pandemic certainly has not been “the great equalizer”. If we don’t address structural racism in clinical trials, a vaccine might not be “the great equalizer” either.

 

[1] See Covid in the U.S.: Latest Map and Case Count, N.Y. Times (Oct. 16, 2020, 12:06 PM), https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html.

[2] See Bethany L. Jones & Jonathan S. Jones, Gov. Cuomo is Wrong, Covid-19 is Anything but an Equalizer, Wash. Post (Apr. 5, 2020), https://www.washingtonpost.com/outlook/2020/04/05/gov-cuomo-is-wrong-covid-19-is-anything-an-equalizer/.

[3] Daniel B. Chastain et al., Racial Disproportionality in Covid Clinical Trials, New Eng. J. Med. (Aug. 27, 2020),  https://www.nejm.org/doi/full/10.1056/NEJMp2021971.

[4] See Maria Godoy, What Do Coronavirus Racial Disparities Look Like State By State?, NPR (May 30, 2020, 6:00 AM), https://www.npr.org/sections/health-shots/2020/05/30/865413079/what-do-coronavirus-racial-disparities-look-like-state-by-state.

[5] Id.

[6] Id.

[7] Richard A. Oppel Jr., et al., The Fullest Look Yet at the Racial Inequity of Coronavirus, N.Y. Times (Jul. 5, 2020), https://www.nytimes.com/interactive/2020/07/05/us/coronavirus-latinos-african-americans-cdc-data.html.

[8] Id.

[9] Id.

[10] Daniel B. Chastain et al., Racial Disproportionality in Covid Clinical Trials, New Eng. J. Med. (Aug. 27, 2020),  https://www.nejm.org/doi/full/10.1056/NEJMp2021971.

[11] Id.

[12] Id.

[13] See e.g., Nicholas Weiler, Genomic Analysis Reveals Why Asthma Inhalers Fail Minority Children, UCSF (Mar. 14, 2018), https://www.ucsf.edu/news/2018/03/410041/genomic-analysis-reveals-why-asthma-inhalers-fail-minority-children

[14] See FDA Encourages More Participation, Diversity in Clinical Trials, FDA (Jan. 16, 2018), https://www.fda.gov/consumers/consumer-updates/fda-encourages-more-participation-diversity-clinical-trials.

[15] U.S. Food & Drug Admin., 2015-2016 Global Participation in Clinical Trials Report 24 (2017).

[16] U.S. Food & Drug Admin., 2019 Drug Trials Snapshots Summary Report 3 (2020).

[17] See Inclusion of Women and Minorities as Participants in Research Involving Human Subjects, NIH (Dec. 27, 2019), https://grants.nih.gov/policy/inclusion/women-and-minorities.htm.

[18] See id.

[19] FDA Encourages More Participation, Diversity in Clinical Trials, FDA (Jan. 16, 2018), https://www.fda.gov/consumers/consumer-updates/fda-encourages-more-participation-diversity-clinical-trials.

[20] See Mary Chris Jaklevic, Researchers Strive to Recruit Hard-Hit Minorities Into COVID-19 Vaccine Trials, JAMA Network (Aug. 13, 2020),  https://jamanetwork.com/journals/jama/fullarticle/2769611?guestAccessKey=21455965-ff5d-4474-a38a-77f5e6f27815&utm.

[21] See id.

[22] See id.

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