Blog

Welcome our new Visiting Clinical Assistant Professor, Tiffany Li!

By Andrew F. SellarsAugust 26th, 2019

We are a week away from the start of our new academic year here at the Technology Law Clinic, and we are delighted to announce that Tiffany Li will be joining the BU/MIT Technology Law Clinic this year as a Visiting Clinical Assistant Professor!

Prof. Li's work focuses on privacy, artificial intelligence, and tech platform governance. Li’s writing has appeared in popular publications including the Washington Post, the Atlantic, NBC News, and Slate. Li is also an Affiliate Fellow at Yale Law School's Information Society Project and has held past affiliations with Princeton's Center for Information Technology Policy and UC Berkeley's Center for Technology, Society and Policy. Her legal experience includes roles at the Wikimedia Foundation, General Assembly, and Ask.com. She also holds CIPP/US, CIPP/E, CIPT, and CIPM certifications from the International Association of Privacy Professionals. Li received a J.D. from Georgetown University Law Center, where she was a Global Law Scholar, and a B.A. in English from University of California Los Angeles.

Please join us in welcoming Prof. Li to the Technology Law Clinic!

The clinic is hiring a Visiting Clinical Assistant Professor!

By Technology Law ClinicJuly 26th, 2019

The Technology Law Clinic seeking an attorney with technology law experience to join us for the 2019–20 Academic Year as a Visiting Clinical Assistant Professor! The professor will supervise and assist the students in the Technology Law Clinic as they work with our clients on matters of data privacy, intellectual property, and cybersecurity. The professor will also work with the Clinic Director in scholarly research, curricular and program development, and as time allows, to develop generalized legal resources for the MIT and BU student communities. Ideal candidates are attorneys admitted or eligible for admission in Massachusetts, with at least 1–3 years of experience representing clients on cutting-edge issues in technology law, especially in the area of data privacy compliance.

The full job description is here. Applications received before August 9 will be given full consideration.

SCOTUS: Confidential Sale of Invention Triggers 1-Year Deadline for Patent Application

By Technology Law ClinicApril 2nd, 2019

(We are delighted to host this client alert written by Kristen Elia, a student in our companion clinic, the Startup Law Clinic.)

Attention all aspiring inventors! Your patent applications could be rejected as a result of prior confidential sales of your patentable invention, as re-emphasized in a recent Supreme Court ruling. In brief:

  • The on-sale patent bar prohibits the issuance of a patent to an inventor where the invention was sold more than a year prior to the filing of a patent application for the invention.
  • On January 22, 2019 in Helsinn Healthcare S.A. v. Teva Pharmaceuticals USA, Inc., the Supreme Court affirmed that even “secret sales”, in which the invention is required to be kept confidential, trigger the on-sale patent bar.
  • The fact that the patent bar is triggered by confidential sales is not new law. However, this Supreme Court ruling confirms the scope of the on-sale bar in the wake of ambiguous language in the America Invents Act, a 2013 update to the federal patent statute.
  • Inventors should consult a patent attorney before commencing sales of their inventions or executing manufacturing, development, or other similar commercialization agreements, as language in those agreements that is unnecessarily broad could potentially trigger the on-sale bar.

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Thursday (3/28/19) Talk at BU: Pressing Legal Issues for Blockchain Technology

By David GrossMarch 27th, 2019

Talk FlyerPlease join us at the BUild Lab at 12pm on Thursday, March 28 to learn about the legal issues that arise in applications of blockchain technologies, including information on cryptocurrencies, exchanges, and smart contracts.

This presentation, by TLC student Zachary Sisko, will cover the following topics:

  • The practical and legal differences between cryptocurrencies and tokens
  • Government responses to crypto exchanges and markets
  • The enforceability and structure of smart contracts
  • Data security practices (and failures) relating to blockchain technology

Click here to RSVP

Thursday, March 28, 2019
12:00 pm - 1:00 pm

BUild Lab
730 Commonwealth Ave
Brookline, MA
Enter at red awning next to Pavement Coffee

BU Law students! Apply by April 5 for the 2019-2020 clinic!

By Technology Law ClinicMarch 25th, 2019

Attention BU Law students! Applications are open to rising 2Ls and 3Ls to join the Technology Law Clinic for the 2019–2020 academic year. We're looking for all students with an interest in technology legal issues, be that in data privacy, intellectual property, cybersecurity, or media and First Amendment law.

No prior background in technology is required, but the clinic has a course co-requisite requirement: students must have taken or take a class in the areas of intellectual property, privacy, or cybersecurity. A number of courses meet this criteria.

The deadline to apply for the clinics is 5pm on April 5. Full application instructions are available here.

We hope that you'll join!

Understanding and avoiding risks associated with machine learning

By Technology Law ClinicFebruary 13th, 2019

Written by Imran Malek, Technology Law Clinic

Introduction

Nearly every aspect of our lives is either enhanced by or depends on computing technology – whether it be through the networks and platform that we access using the devices on our desktops, under our TVs, or in our pockets. With the advent of machine learning, the technology that essentially allows to make informed decisions using up to date data with minimal user input, and the “perfect storm” of ubiquitous connected computing devices and affordable data processing mechanisms, we are now at a place where the more we use our technology, the better (or at least more tailored) our experiences can be. machine learning plays a big part in most of our lives. Among many other things, technology that uses machine learning powers our spam filters, optimizes inventory to prevent food waste due to spoilage in grocery stores, and even curates our digital radio stations to the point where never feel the need to skip a track.

With machine learning’s ubiquity, however, come pitfalls. Although implemented with the best of intentions, machine learning has in some areas reinforced biases, stifled opportunities, or produced wholly negative outcomes:[1]

The benefits—and these specific drawbacks—of machine learning also point to another area of concern: personal data privacy. As more and more of our lives are lived online, we produce more data. This increase in production has led to a data “gold rush” where businesses compete to collect the most data possible so that they can make the most informed decisions. With such significant financial implications come increased scrutiny to the data that we produce or is produced on our behalf. In the context of innovative applications of machine learning where personal data is often a critical part of the algorithmic decision-making process, legislation like the European Union’s General Data Protection Regulation (“GDPR”) provides rights to  end users, which could lead to serious consequences for a startup that doesn’t respect those rights. With these drawbacks also come legal challenges that a startup implementing may face, including challenges arising from the unauthorized use of data to “train” machine learning algorithms, algorithmic bias and anti-discrimination law, and collecting, processing, and transmitting data at the expense of user privacy.

With all that in mind, these posts will dive into machine learning and elaborate on the legal issues surrounding it through three major topics:

  1. An introduction to machine learning
  2. How startups can source the right data for a machine learning algorithm while maintaining user privacy, especially in the context of relevant laws like the GDPR.
  3. Implementing machine learning models live while ensuring transparency and promoting fairness in decision making

An Introduction to Machine Learning

While there are plenty of online tutorials (including one of the first and most well-known Massive Online Open Courses) that discuss the details, techniques, and nuances associated with machine learning (concepts like “supervised learning” or “naive bayes classification,” for example) for this post and for the other posts in this series we will simplify machine learning with a definition paraphrased from Sujit Pal, and Antonio Gulli:

Machine learning focuses on teaching computers how to learn from and make predictions based on data.

The data that feeds those predictions, especially today, usually encompass what the business and technology worlds have labeled “big data.” Without going into too much detail, we can define big data and its machine learning application through “three Vs”:

  1. Volume: The amount of data out there is vast, and continues to expand every second.
  2. Variety: As more and more devices, applications, and services are brought online, data is efficiently stored in ways that are easily accessible and usable.
  3. Velocity: Data is now collected in real time, and real time collection, when paired with cheap computing power, means that data can instantly be used.

The scale and speed at which data is generated, classified, and stored make it impossible for humans alone to analyze, interpret, and execute on that data. Accordingly, machine learning has become one of the tools that businesses, governments, researchers, and even individuals use to influence or even make decisions.

The actual mechanism of turning data into a decision is usually referred in the context of “models.” These models represent collections of patterns that, when combined, produce generalizable trends that empower decision making. To put it simply, models are generalized mathematical representations of real-world processes such that when data is input into a model the result would mirror the real-world output.[2]  Since processes in the real world are often the result of multiple independent variables, models need to be “trained” in order to be accurate. It is through this training that the “learning” behind machine learning becomes evident.

To understand training, and machine learning in general, let’s think through a simple example where I want to build a tool that helps me make a decision that many of us face every day: what to order at a coffee shop. In this simple example, I want my model to take in three variables and then use them to decide what I drink I should buy:

Variable

Possible Values

Time of Day

Morning, Afternoon, Evening

Temperature Outside

Hot, Cold

Day of Week

Monday, Tuesday, Wednesday, Thursday,
Friday, Saturday, Sunday

Right now, without any training, my model doesn’t know what to do, it doesn’t know what to do, so I need to train it. To do so, I’ll start by keeping track of one week’s worth of my typical drink purchases:

Day of Week

Time of Day

Temperature Outside

Drink I Ordered

Monday

Morning

Cold

Hot Coffee

Tuesday

Morning

Hot

Iced Coffee

Wednesday

Morning

Cold

Hot Coffee

Thursday

Evening

Cold

Iced Herbal Tea

Friday

Afternoon

Hot

Hot Coffee

Saturday

Evening

Cold

Hot Herbal Tea

Sunday

Morning

Hot

Iced Coffee

Immediately, you can see that there’s a pattern forming – when it’s Hot outside, I prefer cold drinks, and when it’s Cold outside, I prefer hot drinks. You can also see that when it’s in the Evening, I prefer to drink tea instead of coffee.

When the new week starts and it’s Monday, I load my now trained model into an application on my phone, go to my local coffee shop, and try to make a decision. I go in the Morning, and it happens to be Cold outside, so naturally, my model suggests that I should get a Hot Coffee. This is great! I don’t have to worry about making a decision. I

subsequently order my hot coffee and make sure to record that it was Morning, it was Cold outside, and I ordered a Hot Coffee on a Monday. I then input this data into my model. At this point, I’ve not only trained my model, I’ve also provided feedback using to it help validate it.

If we were making a “production ready” machine learning model (for example, one that might be built into a coffee chain’s mobile app), there would be many more variables factored in to the algorithm, including: weather on the current day, what people around me have ordered, what month it is, whether or not I ordered food, what coffee shop location I happen to be visiting, what genre of music is playing in the coffee shop, and many, many, many more!

Now, let’s take it one step further and talk about what might happen if there’s no training data available – on the next day, a Tuesday morning, I skip my morning cup of coffee and decide to walk in to the shop on a Cold Afternoon. My model is left with a dilemma – there’s no training data for this exact combination of variables. Does it suggest an Iced Coffee, because that’s what I ordered on a Tuesday? Or does it suggest a Hot Coffee, because that’s what I ordered the last time I went in to my coffee shop in the Afternoon? While a human might intuitively think that temperature is a more important factor than day of the week when it comes to making a beverage decision, my model doesn’t know that, so it randomly picks from the two likely options and suggests an Iced Coffee. Scoffing at the prospect of drinking something cold on a cold day, I reject that decision, input my rejection, and order a Hot Coffee. My model now knows that on Cold Tuesday Afternoons, I drink Hot Coffee. It also has learned, based off of my decision, that when I make a decision that I haven’t made before, I weigh the temperature as more decisive factor than the day of week.

In the real world, the data used from training can come from a variety of sources, but that data most often comes from historical data that was collected, analyzed, and processed by humans. Unfortunately, since that data is derived from human behavior, historical data may also reinforce preexisting biases (like we saw earlier with the concerns around predictive policing algorithms).

Conclusion

In the next post of this series, we’ll touch on the relevant laws that touch algorithmic bias, and we’ll also provide recommendations to organizations using machine learning on how to diagnose and overcome bias in the tools and technologies that they build.

More

BU Law students: come work at the BU/MIT clinics this summer!

By David GrossJanuary 23rd, 2019

Attention BU Law students! We are happy to announce that applications for the 2019 summer fellowships with the two BU/MIT clinics are now available on Symplicity. We'll be hosting an information session on Monday, February 4 at 1pm in Room 209 to discuss these opportunities. The deadline to submit is 4pm on February 8.

The Technology Law Clinic is a pro bono service for students at MIT and BU who seek legal assistance with their innovation-related academic and extracurricular activities. Fellows will represent students on legal matters related to technology research and innovation, including in the areas of intellectual property, data privacy, cybersecurity, and media law. Clients work in fields such as artificial intelligence, Internet platforms, drones and robotics, and novel forms of technology research, ventures, and advocacy.

Fellows in the Startup Law Clinic will provide strategic legal and business advice to startups, assisting student entrepreneurs in the MIT and BU communities with the corporate, transactional, and intellectual property issues that arise in the process of turning their ideas into operating businesses.  Working under the guidance of the clinic directors, the Fellows will manage each step of the client relationship, from the initial intake interview through the completion of the engagement.

We are honored to announce that thanks to a generous contribution by Antonio Gomes (BUSL '96), both the Technology Law Clinic and the Startup Law Clinic will be hiring two students as Matthew Z. Gomes Fellows.

The Mathew Z. Gomes Fellowship, established in memory of Mr. Gomes’s son, is specifically open to students who: 

  • have demonstrated experience in or commitment to working with historically underserved or underprivileged populations;
  • are the first generation in one’s family to attend law school;
  • are socioeconomically disadvantaged; and/or
  • have overcome substantial educational or economic obstacles or personal adversity.

In addition, the Technology Law Clinic will be hiring an additional Summer Fellow, for a total of three summer opportunities. The Startup Law Clinic will be hiring three additional Summer Fellows, for a total of five summer opportunities.

Working under the guidance of their respective clinic's faculty, these fellows will manage the work of their clinic’s clients over the summer. Total compensation for the 10-week program will be $6,000.00 per fellow.

Rising 2L and 3L students are encouraged to apply. Applicants should submit a cover letter, resume, and transcript via Symplicity by 4pm on February 8, 2019. If applying for the Matthew Z. Gomes Fellowship, the cover letter should address the fellowship's specific qualifications under that fellowship.

Using the BU Law Symplicity portal you can find the listings at:

  • Matthew Z. Gomes Fellow, Technology Law Clinic – listing 30445
  • Summer Fellow, Technology Law Clinic – listing 30444
  • Matthew Z. Gomes Fellow, Startup Law Clinic – listing 30443
  • Summer Fellow, Startup Law Clinic – listing 30442

Please contact Andy Sellars or Jim Wheaton if you have questions about these opportunities.

Protecting Access to Government Databases under Public Records Law

By Andrew F. SellarsJanuary 11th, 2019
Technology Law Clinic students Patrick Wilson (BUSL '20), Ally Faustin (BUSL '20), Zach Sisko (BUSL '19), and Lyndsey Wajert (BUSL '19), outside of the John Adams Courthouse after the oral argument.

On Thursday, the Supreme Judicial Court of Massachusetts heard arguments in the case Boston Globe Media Partners, LLC v. Department of Public Health. The Clinic filed an amicus curiae brief (PDF) in the case on behalf of the editorial staff of The Tech, the MIT student newspaper, together with the Reporters Committee for Freedom of the Press, Metro Corp. (publisher of Boston magazine), the New England Center for Investigative ReportingNew England First Amendment Coalition, the New England Newspaper and Press Association, the New York Times Company, North of Boston Media Group (publisher of several regional newspapers in northeastern Massachusetts and southern New Hampshire), and the editorial staff of the Free Press, the newspaper of the University of Southern Maine. 

The case concerns a request that the Boston Globe made under the Massachusetts Public Records Law (PRL) for an electronic copy of two databases containing Massachusetts birth and marriage records, held by the Department of Public Health. The records in question contain only basic information related to births and marriages, and are already made available to the public on an individual level. The Department denied the request, arguing among other things that the disclosure of the entire set of records would be an unwarranted invasion of privacy, and thus is exempt under specific provisions in the PRL.

The amicus brief, filed in support of the Boston Globe, brings a combination of law, data science, and examples from journalism to show why disclosure of this set of records does not create any privacy harms. Quoting from the brief:

This Court should not abandon its well-settled framework for evaluating exemption requests predicated on privacy concerns under the PRL simply because the Records contain numerous entries. To the extent that large datasets are different than other records, they can be analyzed simply by distinguishing between the “breadth” and the “depth” of the dataset in question. “Broad” but “shallow” datasets, like the Records here, which relate to numerous individuals but contain few details, pose much lower privacy risks than “deep” datasets of any breadth, which contain detailed information about each person in the set. […] In this case, because these Records contain very little information about each person, disclosing the Records will not create any new privacy risks.

The brief also gives a variety of examples of where journalists used records like these on stories about government accountability and other issues of public concern.

"The editors at The Tech were happy to participate in this amicus brief," said Emma Bingham, Editor in Chief of The Tech. "As both journalists and students at one of the world’s top technical institutes, we understand the value of data access. We hope the Department of Public Health datasets in question in this case will soon be available to all newspapers, so that they use them to continue to hold our government accountable and produce interesting and informative reporting. We believe this is especially important for small local and university newspapers, which play an important role in their communities, but often have fewer resources to spend gathering data."

Clinic students Alexandra Faustin (BUSL '20), Zachary Sisko (BUSL '19), Lyndsey Wajert (BUSL '19), and Patrick Wilson (BUSL '20) drafted the brief, with help from Clinic director Andy Sellars, Visiting Clinical Assistant Professor Julissa Milligan, and attorneys at the Reporters Committee for Freedom of the Press.