Blog

Summer in Review: Renewed Partnerships and Team Updates

By Bridget DonovanAugust 17th, 2020

The Technology Law Clinic has had a busy and exciting summer. Though campuses have gone quiet, operations have moved online and the Clinic is still open for business and representing MIT and BU students in their ventures on matters related to their technology-related research, advocacy, and innovation.

Five More Years

Boston University and the Massachusetts Institute of Technology have renewed their partnership, solidifying the work of the Technology and Startup Law Clinics through 2025. Read more about this powerful partnership and the work that we’ve accomplished here.

New Team Members

Jocelyn Hanamirian has joined the team as the Assistant Director of the Technology Law Clinic. Prof. Hanamirian joins the clinic from the Walt Disney Company where she was Associate Principal Counsel. At the Walt Disney Company, Hanamirian worked in the Intellectual Property legal department, counseling Walt Disney Imagineering and other internal clients on copyright, trademark, right of publicity, and matters of freedom of expression. Hanamirian holds a J.D. from Columbia Law School, where she was a Harlan Fiske Stone Scholar and Editor in Chief of the Journal of Law and the Arts, and an A.B. from Princeton University.  She currently serves as vice-chair of the International Trademark Association’s Famous & Well-Known Marks U.S. Subcommittee.

Also new to the BU / MIT Clinics is Bridget Donovan, who serves as the team’s Senior Program Coordinator. Bridget joined Boston University School of Law from MENTOR, where she served as Corporate Engagement Project Manager. Bridget holds a B.A. from Smith College.

Please join us in welcoming them to the team!

We’re Hiring an Assistant Director!

By Andrew F. SellarsMarch 28th, 2020

We are excited to announce that BU Law is hiring a Lecturer and Clinical Instructor to serve as the first Assistant Director of the BU/MIT Technology Law Clinic! Applications received on or before April 10, 2020 will be given full consideration.

The Clinic is part of a unique collaboration between BU Law and the Massachusetts Institute of Technology, and is part of BU Law’s Entrepreneurship, Intellectual Property, and Cyberlaw Program. BU Law believes that the cultural and social diversity of our faculty, staff, and students is vitally important to the distinction and excellence of our academic programs. To that end, we are especially eager to hear from applicants who support our institutional commitment to BU as an inclusive, equitable, and diverse community.

The Clinic represents current students at MIT and BU on matters related to their technology-related research, advocacy, and innovation. The Clinic frequently advises clients in the areas of data privacy, intellectual property, computer access laws, media law, and technology regulatory compliance. Clinic faculty help law students assist clients in counseling, pre-litigation, and transactional settings, and possibly also in litigation matters, including response to cease-and-desist letters and litigation under open records laws. Clients often present novel questions of law in emerging areas of technology, including artificial intelligence and machine learning, encryption and cryptography, and novel methods of online platform scrutiny and analysis.

The Assistant Director will assist and advise the Clinic Director in all aspects of the operation and development of the program. Their primary responsibilities will be to supervise and train law students with client matters, teach and develop curricular materials for the Clinic’s year-long seminar, and assist the Clinic Director in the strategic growth and development of the Clinic.

See our job posting for full details and application instructions.

Summer Job Opportunities at the BU/MIT Law Clinics!

By Technology Law ClinicJanuary 13th, 2020

We are happy to announce that the two BU/MIT Law Clinic are now receiving applications from current BU Law 1Ls and 2Ls to participate in our summer program. We'll be hosting an information session on Thursday, January 16 at 1pm in Room 101 to discuss these opportunities.

The deadline to apply is Tuesday, January 21, at 4pm, via Symplicity (see below for more information).

About the Clinics

Both clinics work with graduate and undergraduate students at MIT and BU with their legal issues, under the supervision and teaching of BU Law faculty. The two clinics handle similar but distinct legal issues:

  • The Technology Law Clinic works with technology-focused student researchers, advocates, and entrepreneurs on the legal and regulatory issues that the students encounter as they do their work. This includes work in data privacy, intellectual property, cybersecurity, and media/First Amendment law. Law students will counsel clients, respond to legal threats, and, from time to time, represent the students in negotiation or litigation related to their academic or innovative work.
  • The Startup Law Clinic addresses the transactional and corporate needs of student-led startups, including counseling on entity formation, intellectual property licensing and registration, and equity and investment issues. Students counsel clients through the entire life-cycle of a startup, including representing clients in negotiation and drafting contracts and other documents where appropriate.

Students work 35 hours per week for 10 weeks over the summer.

The Matthew Z. Gomes Fellowship

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 each as our third class of Matthew Z. Gomes Fellows, along with our other summer 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.

Qualifying students are encouraged to apply for the Gomes Fellowship.

How to Apply

Current BU Law 1Ls and 2Ls students are encouraged to apply. Applicants should submit a cover letter, resume, and transcript via Symplicity by Tuesday, January 21, at 4pm. If applying for the Matthew Z. Gomes Fellowship, the cover letter should address the student'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 34380
  • Summer Fellow, Technology Law Clinic – listing 34378
  • Matthew Z. Gomes Fellow, Startup Law Clinic – listing 34379
  • Summer Fellow, Startup Law Clinic – listing 34376

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

IAP 2020, Law & Technology: Know Your Rights

By David GrossJanuary 10th, 2020

We're excited to announce the fourth edition of our annual MIT IAP class, Law & Technology: Know Your Rights. In a week-long series of lunch talks, we will review current events and hot topics in technology law, and how they affect student research, activism, and startups. We'll also have special guest speakers!

Please see our IAP page for more details and to RSVP. We hope to see you there!

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.

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