' MTTLR | Michigan Telecommunications and Technology Law Review

Recent Articles

Association for Molecular Pathology v. Myriad Genetics: A Critical Reassessment

By  Jorge L. Contreras
Article, Fall 2020
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From Automation to Autonomy: Legal and Ethical Responsibility Gaps in Artificial Intelligence Innovation

By  David Nersessian & Ruben Mancha
Article, Fall 2020
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Will the 'Legal Singularity' Hollow Out Law's Normative Core?

By  Robert F. Weber
Article, Fall 2020
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Antitrust Overreach: Undoing Cooperative Standardization in the Digital Economy

By  Jonathan M. Barnett
Article, Spring 2019
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Bank On We The People: Why and How Public Engagement Is Relevant to Biobanking

By  Chao-Tien Chang
Article, Spring 2019
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Recent Notes

The Contribution of EU Law to the Regulation of Online Speech

By  Luc von Danwitz
Note, Fall 2020
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The Unified Patent Court and Patent Trolls In Europe

By  Jonathon I. Tietz
Note, Spring 2019
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Blog Posts

Anti-Discrimination Laws and Algorithmic Discrimination

Machine algorithms can discriminate. More accurately, machine algorithms can produce discriminatory outcomes. It seems counterintuitive to think that dispassionately objective machines can make biased choices, but it is important to remember that machines are not completely autonomous in making decisions. Ultimately, they follow instructions written by humans to perform tasks with data provided by humans, and there are many ways discriminations and biases can occur during this process. The training data fed to the machine algorithm may contain inherent biases, and the algorithm may then focus on factors in the data that are discriminatory towards certain groups. For example, the natural language processing algorithm “word2vec” learns word associations from a large corpus of text. After finding a strong pattern of males being associated with programming and females being associated with homemakers in the large text datasets fed to it, the algorithm came up with the analogy: “Man is to Computer Programmer as Woman is to Homemaker.” Such stereotypical determinations are among the many discriminatory outcomes algorithms can produce. The European Union (EU), out of fear of these outcomes leading to discriminatory effects produced by decision-making algorithms, included Article 22 when enacting the General Data Protection Regulation, which gives people “the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects concerning him or her or similarly significantly affects him or her.” Although what constitutes “solely automated processing” is debatable, the EU’s concern of algorithmic discrimination is evident. In the United States (U.S.), instead of passing laws that specifically target algorithmic discrimination, such concerns are handled largely under regular anti-discrimination laws,... read more

Data in the Post-Pandemic Era: Zoom Video’s Security and Censorship Controversies

As the use of Zoom Video Conferencing has skyrocketed since the start of the Coronavirus Pandemic, the company’s security infrastructure and alleged interference in virtual events over the platform have come under fire multiple times since the beginning of global quarantines in March 2020. As millions of Americans are now using Zoom and other videoconferencing tools daily, any data breaches may provide unprecedented access to otherwise confidential conversations between users, including any U.S. government and private sector professionals who utilize the app for their work. Furthermore, censorship of certain virtual gatherings may place dangerously restrictive limits on communication and social organizing as the pandemic demands that most of the population continue to conduct its daily business virtually. Most recently, the U.S. Department of Justice has charged former China-Based Zoom executive Xinjiang Jin, also known as “Julien Jin,” with conspiracy to commit interstate harassment and unlawful conspiracy to transfer a means of identification after his alleged participation in a scheme to assist the People’s Republic of China in blocking virtual commemorations of the Tiananmen Square massacre in May and June 2020. News of this potential attempt to censor Chinese dissidents should remind users that their choice to route our communications through this (and other) videoconferencing apps has created new, special pandemic-era censorship concerns, Zoom has released a blog post and S.E.C. filing on its website acknowledging the charge and investigation, reaffirming its “support [for] the U.S. Government to protect American interests from foreign influence,” dedication “to the free and open exchange of ideas,” and ongoing, “aggressiv[e]” actions to “anticipate and combat…data security challenges.” Furthermore, the blog post details subpoenas received... read more

Tracking COVID-19 on College Campuses: False Starts, Missteps, and Considerations for the Future

As colleges and universities reopened campuses to students last fall, a number of schools across the United States turned towards the use of location tracking apps, wearable technology, and other surveillance tools in the hope that they would facilitate contact tracing and potentially mitigate the spread of COVID-19 in residence halls and in-person classes. These efforts to monitor student health and track student activity have been met with skepticism from students and privacy advocates, who cite concerns about the invasive nature of such tools and the risk that the data they generate may be misused by unauthorized parties.   In Michigan, Oakland University had announced earlier in August that it would require students living in residence halls to wear a BioButton, a coin-sized device that would monitor physiological data, such as skin temperature and heart rate as well and physical proximity to others wearing BioButtons. Administrators had hoped that this would allow the university to pinpoint early-stage cases among the student body. The university soon withdrew the policy, however, after receiving significant backlash from students, who, citing privacy and transparency issues, petitioned the school to make usage optional.   Albion College, a private liberal arts college in Michigan, had issued a similar requirement for students to install the Aura app on their phones before they could come on campus. As a contact-tracing app, Aura would record students’ real-time location using phone GPS services and alert students when they had been in close proximity with someone who had tested positive for the virus. Albion had intended for the Aura app to work in tandem with what some considered to be... read more

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