Category: Blog Posts Page 29 of 75

By M. Walker Upchurch

 

Very rarely is there a game that captures the essence of what it aims to portray better than the EA Sports NCAA Football Franchise. The game aimed to please collegiate fans and did their best to accurately portray what made every University special. Whether it was touching the banner in Ann Arbor, slapping Howards Rock at Clemson, or Playing Like a Champion in South Bend, the franchise got it right. The game was engaging and made every school spectacular, from the fans in the stands with ESPN signs to the college football season’s highlights like the Army-Navy game with the cadets. It understood what made the college experience so exciting. More simply put, it made the alma mater’s and small college towns feel like home.

 

Unfortunately, this magisterial game came to a close in large part due to the O’Bannon V. National Collegiate Athletics Ass’n lawsuit that took place in 2014 through 2015.[1] The case was an antitrust lawsuit decided on September 30th, 2015, in which the court held that “The NCAA’s compensation rules were subject to antitrust scrutiny; the plaintiffs suffered an antitrust injury as a result of the compensation rules; the compensation rules were subject to analysis pursuant to rule of reason; the district court did not clearly err in finding that allowing NCAA member schools to award grants-in-aid up to their full cost would be substantially less restrictive alternative to current compensation rules; but the district court clearly erred in finding it a viable alternative to allow students to receive Name, Image, and Likeness (NIL) cash payments untethered to their education expenses.”[2]

 

Section one of the Sherman Antitrust Act States, “Every contract, combination in the form of trust or otherwise, or conspiracy in restraint of trade or commerce among the several States, or with foreign nations, is declared to be illegal. Every person who shall make any contract or engage in combination or conspiracy hereby declared to be illegal shall be deemed guilty of a felony, and, on conviction thereof, shall be punished by fine not exceeding $100,000,000 if a corporation, or, if any other person, 1,000,0000, or by imprisonment not exceeding 10 years, or by both said punishments, in the discretion of the court.”[3]

 

The court found that the rule of the NCAA keeping schools from competing on price in compensating college athletes for the commercial use of their Names, Image, and Likeness was an unlawful restraint of trade in violation of Section 1 of the Sherman Antitrust Act. [4]

 

This decision presented the NCAA with a bevy of amateurism problems while still wanting to make as much money as possible. Flatly, to understand this, you must understand that the NCAA did not wish to pay the players. On their website, they state that “Allowing student-athletes to be paid for athletics performance would undermine the balance between the two [athlete and non-athlete] and detract from the integration of academics and athletics in the campus community.” [5]

 

Whether you believe that to be the case, or if you are more inclined to think along the lines of Jay Bilas, Esq. who stated, “We are selling these players for literally billions of dollars, paying the coaches millions of dollars, and building gigantic facilities. . . . It’s professional in every way.” There has long been the tension of paying these players.[6]

 

However, on October 29th, the NCAA Board of Governors voted to allow athletes to profit from their name, image, and likeness.[7] According to USA Today, these players will be able to profit off of their Name, Image, and Likeness as soon as August 1st, 2021.[8] Not so coincidentally, less than four months later, after this news broke, we have the official announcement that EA Sports will be bringing back a huge moneymaker for them and the NCAA. The Official announcement that EA sports and the NCAA are partnering with The College Licensing Company to bring back college football videogames broke on February 2nd.[9]

 

In conclusion, while it will be nice for nostalgic reasons for this game to come back, many of the larger issues that have plagued college athletics remain. While the players will profit from their name, image, and likeness, we are still yet to see whether the NCAA will do well by them. College Athletics is a humongous money maker. In the years 2018-2019, the top five colleges in total revenue from athletic departments were Texas, Texas A&M, Ohio State, Michigan, and Georgia. Their overall total revenue from their athletics department was cumulatively over $1,000,000,000.[10] Simply a tremendous amount. This is particularly true when you consider that they don’t pay the players for the services rendered. Hopefully, this game coming back will build a bridge for these athletes to a more even playing field.

 

[1] See O’Bannon V. Natl. Collegiate Athletic Ass’n, 802 F.3d 1049 (9th Cir. 2015).

[2] Id.

[3] 15 U.S.C. § 1

[4] See Brief in Opposition at i, O’Bannon V. Natl. Collegiate Athletic Ass’n, 802 F.3d 1049 (9th Cir. 2015).

2016 WL 3626736

[5] See NCAA DEFENDS SCHOLARSHIPS FOR COLLEGE ATHLETICS, NCAA, https://www.ncaa.org/about/resources/media-center/feature/ncaa-defends-scholarships-college-athletes (last visited Feb. 5, 2021).

[6] See Golic and Wingo, ‘There is no such thing as a student-athlete!’ – Jay Bilas on the NCAA’s new rules, ESPN https://www.youtube.com/watch?v=qQuoDncRuL8&ab_channel=ESPN, (last visited Feb. 5, 2021).

[7] See Board of Governors starts process to enhance name, image and likeness opportunities, National Collegiate Athletics Association,  https://www.ncaa.org/about/resources/media-center/news/board-governors-starts-process-enhance-name-image-and-likeness-opportunities, (last visited Feb. 5, 2021).

[8] Steve Berkowitz, NCAA unveils proposed rules changes related to athletes’ name image and likeness, USA Today, https://www.usatoday.com/story/sports/college/2020/11/13/ncaa-nil-name-image-likeness-proposal/6281507002/, (last visited Feb. 5th 2021).

[9] Chris Bengal, EA Sports plans to revive its college football video game franchise, CBS Sports, (Feb. 2, 2021 at 5:04 PM), https://www.cbssports.com/college-football/news/ea-sports-plans-to-revive-its-college-football-video-game-franchise/

[10] Steve Berkowitz, Matt Wynn, Camille McManus Jodi Upton, Rocio Fortuny, Veer Badani, Mitch Bannon, Jishnu Nair, Santino Primerano, Samantha Rothman, Tanner Russ, Peyton Smith, Frankie Vernouski, Thomas Vielkind, NCAA Finances, https://sports.usatoday.com/ncaa/finances(last visited Feb. 5, 2021.

Image Source: https://www.shutterstock.com/image-illustration/american-soccer-stadium-3d-rendering-516354493

 

By Anna Hargett

 

Smartmatic Corporation, a voting technology company, is suing Fox News for defamation.[1] Smartmatic claimed that Fox News’ disinformation surrounding election fraud “irreparably harmed” the company.[2] The plaintiffs are seeking 2.7 billion dollars in damages, which makes this case one of the largest defamation suits ever filed.[3]

 

Smartmatic alleges that Fox News wrongly accused the company in order to create a villain in the election fraud narrative.[4] Fox has responded to the allegation by noting that they are proud of their election coverage. [5]

 

This isn’t the first time that Smartmatic has gone up against Fox News.[6] In late 2020, Smartmatic warned Fox about the spread of disinformation about the election.[7] Fox responded to earlier warnings from Smartmatic by hosting technology experts to debunk the fraud theories by opining that the Smartmatic software was not used to “delete, change, or alter anything related to vote tabulation.”[8]

 

Smartmatic’s suit is not the only defamation lawsuit that has been filed by an election technology company regarding election fraud disinformation.[9] Dominion Voting Systems, which operates in 28 states, filed a defamation suit against Rudy Giuliani for $1.3 billion. Giuliani denounced the suit as an “act of intimidation.”[10]

 

Smartmatic operated its technology during the 2020 election in Los Angeles County.[11] Although Smartmatic’s role in the election was minimal, the company alleges that it was harmed by the disinformation that was disseminated by Fox News after Attorney General Barr announced that the DOJ found no evidence of voter fraud.[12]

 

The company alleges that two distinct conspiracy theories stemmed from Fox’s spread of disinformation.[13] One conspiracy theory that stemmed from the Fox newscasts included false allegations of Smartmatic ties to Venezuela.[14] Another conspiracy theory accused Dominion Voting Systems of colluding with Smartmatic by using Smartmatic’s software in swing states and that “votes were exported out of the country to be tabulated.” [15]Both theories have proven to be false.[16]

 

Lauren Coates, a legal analyst with CNN, stated that the case has merit because the defamation elements of knowingly false statements, malice, and financial damage are fulfilled by the allegations.[17]

 

Another strength of the case is that the statements made are easily debunked and thus clearly false.[18] Because of the nature of the patently false messaging, it may be easier to prove that the Fox hosts known or should have known that the statements were false.[19]

 

Jonathan Peters, a professor at UGA Law, noted that the main point of dispute in the case will likely be the public figures involved and the free speech regarding a matter of public concern.[20] Although these issues may arise, he believes Smartmatic will prevail. [21]

 

Smartmatic’s attorney responded to concerns about the free press implications of the case.[22] He stated that this suit will actually be beneficial to the profession by bringing it “back to factual reporting.”[23]

 

Smartmatic is seeking damages that were calculated based on a $767.4 decrease in profits of Smartmatic’s parent company, along with threats against its staff members and “undermined business relationships around the world.”[24] Smartmatic is also requesting a full retraction of the false statements from the Fox News hosts regarding the company.[25]

 

[1] Meghan Roos, Smartmatic Hits Fox News, Rudy Giuliani With One of the Largest Defamation Suits Ever Filed in U.S., Newsweek (Feb. 4, 2021, 2:05PM), https://www.newsweek.com/smartmatic-hits-fox-news-rudy-giuliani-one-largest-defamation-suits-ever-filed-us-1566933.

[2] Id.

[3] Id.

[4] Oriana Gonzalez & Sara Fischer, Smartmatic files $2.7 billion lawsuit against Fox, Rudy Giuliani, Sidney Powell, Axios (Feb. 4, 2021), https://www.axios.com/smartmatic-lawsuit-fox-giuliani-powell-4c20b97f-3009-4b79-abfa-70dd17a381ea.html.

[5] Id.

[6] Id.

[7] Id.

[8] Id.

[9] Roos, supra, note 1.

[10] Id.

[11] Gonzalez & Fischer, supra, note 4.

[12] Associated Press, Fox News and Three Hosts Sued for $2.7 billion by Voting Machine Company Over Election-Fraud Claims, USA Today (Feb. 4, 2021, 2:47pm), https://www.usatoday.com/story/entertainment/tv/2021/02/04/fox-news-three-hosts-trump-lawyers-sued-over-election-fraud-claims/4392023001/.

[13] Oliver Darcy, Voting Technology Company Smartmatic Files $2.7 Billion Lawsuit Against Fox News, Rudy Giuliani and Sidney Powell Over ‘Disinformation Campaign’, Cnn Business (Feb. 4, 2021, 1:59PM), https://www.cnn.com/2021/02/04/media/smartmatic-fox-news-giuliani-powell-lawsuit/index.html.

[14]Id.

[15] Id.

[16] Id.

[17] Oliver Darcy, ‘There Is Real Teeth to This:’ Legal Experts Weigh In On Smartmatic’s$2.7 Billion Lawsuit Against Fox News, Cnn Business (Feb. 5, 2021, 6:48 AM), https://www.cnn.com/2021/02/05/media/smartmatic-fox-news-reliable-sources/index.html.

[18] Id.

[19] Id.

[20] Id.

[21] Id.

[22] Id.

[23] Id.

[24] Oliver Darcy, supra, note 13.

[25] NPR Morning Edition, NPR (Feb. 4, 2021, 2:38PM), https://www.npr.org/2021/02/04/964097006/election-tech-company-sues-fox-news-giuliani-and-others-for-2-7-billion.

Image Source: https://www.nbcnews.com/tech/security/fox-news-sued-smartmatic-2-7-billion-over-rigged-election-n1256759

 

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

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

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).

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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).

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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.

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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.

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