The Legal Accountability Project Independent Clinical Opportunity

Who :  Aliza Shatzman, President and Founder, The Legal Accountability Project

Aliza Shatzman is the President and Founder of The Legal Accountability Project (LAP), a nonprofit aimed at ensuring that law clerks have positive clerkship experiences, while extending support and resources to those who do not. Aliza earned her BA from Williams College in 2013 and her JD from Washington University in St. Louis School of Law in 2019. Aliza clerked in D.C. Superior Court during the 2019-2020 term.

Aliza writes and speaks regularly about judicial accountability, clerkships, and diversity in the courts. She has been published in numerous forums, including the Columbia Law Review , Harvard Journal on Legislation, UCLA Journal of Gender & Law , Yale Law & Policy Review , NYU Journal of Legislation & Public Policy , Administrative Law Review , Above the Law , Law360 , Slate , Ms. Magazine , and Balls & Strikes . You may have encountered Aliza on campus in October 2022, when she visited HLS for an event with the Women’s Law Association and HJOL entitled “Fixing Our Clerkship System.”

The Legal Accountability Project (LAP)  is a nonprofit aimed at increasing transparency, diversity, and accountability in judicial clerkships, the legal profession, and the judiciary through advocacy, public education, and innovative legal technology. LAP is the resource Aliza wishes existed when she was a law student applying for clerkships; a law clerk experiencing mistreatment and unsure where to go for help; and a former clerk engaging in the formal judicial complaint process.

LAP’s major initiative this year is a Centralized Clerkships Database, where law clerk alumni from across the country can submit post-clerkship surveys about judges as managers and clerkship experiences, and law students from participating law schools can read all the survey responses, in order to identify judges who will create positive work environments and avoid judges who mistreat their clerks. The Database democratizes information and replaces the “whisper networks” which are currently one of the only ways for prospective clerks to obtain information about judges. This initiative ensures that law students have as much information about as many judges as possible, before making important career decisions, considering the outsized influence of a clerkship on a new attorney’s future career success. This initiative also empowers more diverse students to pursue judicial clerkships, because historically marginalized groups disproportionately lack access to the formal networks and information channels that help some of their peers obtain clerkships.

LAP also works on several initiatives, including educational programming, an employment attorney database to connect law clerks seeking judicial accountability with attorneys who can help, a workplace culture assessment of the federal and state judiciaries, and robust writing and research projects. LAP engages in limited legislative advocacy.

Where : Located in Washington, DC, but internship will be remote.

What : Interns:  We seek entrepreneurial, innovative, startup-inclined law students to work with us in part-time in fall 2023. Interns will assist with the following areas: law/policy; nonprofit administration; legal technology; and development. LAP considers interns to be valuable team members, and the difficulty of assignments will increase with experience level.

Aliza wears all the hats as an entrepreneur and startup founder, and LAP’s interns do, too. One of the most important things LAP’s interns will learn is what entrepreneurship looks like – the day-to-day of launching and leading a startup. 

The ideal candidate is a motivated, reliable self-starter. We are a mission-driven startup, so flexibility and the ability to pivot and prioritize assignments are necessary. Strong writing, legal research, and communication skills, as well as attention to detail, are required.

APPLICATION REQUIREMENTS:   Resume, cover letter, writing sample, and list of 3 references. Please send materials to Aliza Shatzman at [email protected] . Applications will be reviewed on a rolling basis. Deadline: July 31, 2023.  

Independent Clinical Next Steps: 

In order to earn clinical credits selected students must complete the online independent clinical application which is available here: .  

Students will earn 3 fall clinical credits (12 hours/week). Deadlines: Fall August 18; (applications reviewed as received)

Please note that  independent clinical projects  are open to 2L and 3L students.  

J.D students in active F-1 student status cannot participate in Domestic Independent Clinicals because these opportunities are not eligible for CPT authorization.

Questions about the Independent Clinical application process may be directed to  [email protected] .

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Cover Letter Template (PDF)

General tips and formatting suggestions to create a strong cover letter… See also: Cover Letter , Grow Professionally , Representing Yourself

Covid updates. The bivalent COVID-19 booster is required for on-campus presence.

How to Write a Great Resume and Cover Letter

Linda Spencer offers helpful tips and resources to help you write your resumé and cover letter.

What makes a great résumé and cover letter? Linda Spencer, associate director and coordinator of career advising at Harvard Extension School, shares examples of a few strong résumés and explains what makes them stand out.

Perfect Your Marketing Documents

Spencer stresses it’s important to know that your résumé and cover letter are marketing documents. Also keep in mind that the average employer takes about seven seconds to review these documents. They’re not reading: they’re skimming. So you need to make it clear right off the bat how you can add value.

Strong résumés don’t have to be lengthy. One to two pages that feature your most top accomplishments works well.

Use Action Words and Customize Your Pitch

When highlighting your professional experience, use accomplishment statements rather than descriptions of your role. Start with an action verb. Then detail the impact that action had: Did you increase, decrease, modify, or change anything in your work? Finally, be sure to quantify the accomplishments. Data helps.

Your cover letter should be one page, highly customized to each position you’re applying for. It answers two questions: why are you the right fit for the position? And how will you add value to the organization?

While it’s important to have a strong résumé and cover letter, it’s also important to remember that the number one job search strategy is networking. You don’t want to simply be reactive, applying blindly to job postings. You want to conduct a series of informational meetings so that you build a network of people you can reach out to when it comes time to start your job search.

Any Extension student can attend first-come, first-served 15-minute call-ins (via phone or Skype) with Linda. See Career Services for more information.

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Learn how to remove filler words from formal speeches to present with confidence.

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Related Stories

How to write a cover letter in 5 minutes, says Harvard career expert: 'It's professional Mad Libs'


Cover letters can be tedious. They're less formulaic than a resume, more freeform like an essay and for those who struggle with writing in general, can feel like a homework assignment to drag yourself through. But they're critical for showing an employer why you're right for the role.

"A cover letter is like a written introduction of yourself," says Gorick Ng , a Harvard career advisor and author of "The Unspoken Rules." It helps you address some of the questions interviewers will potentially ask you down the line such as, "Why this role? Why this company?" he says. It's like putting a human face to a resume.

And though this document can seem daunting, Ng himself has a developed a hack that cuts his writing time down significantly. He creates a basic cover letter template including all of the key talking points, then leaves out components like the HR representative he'd address and the specific company he's applying for. Going forward, he simply plugs in the relevant information and saves each cover letter as a new document.

"It's professional Mad Libs," he says, adding that, "It takes me, what? Five, max 10 minutes to do a new cover letter" each time.

Follow this template

To replicate Ng's hack, start by writing your cover letter template, including the following four paragraphs:

"Whereas your first template might have taken several hours, each additional template should only take several minutes to update," he says. Save the master template (or templates) under a title you'll recognize later and, going forward, simply make a copy of each and fill in the blanks.

Ng recommends sending your final cover letter as a PDF, so make sure to read through for correct company spellings and any other mistakes thoroughly. Finally, before sending, save the files under names that would help the companies keep track of your documents. Include your full name, for example, the role you're applying for and the date you're applying.

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The AI Hype Cycle Is Distracting Companies

cover letters harvard

By focusing on sci-fi goals, they’re missing out on projects that create real value right now.

Machine learning has an “AI” problem. With new breathtaking capabilities from generative AI released every several months — and AI hype escalating at an even higher rate — it’s high time we differentiate most of today’s practical ML projects from those research advances. This begins by correctly naming such projects: Call them “ML,” not “AI.” Including all ML initiatives under the “AI” umbrella oversells and misleads, contributing to a high failure rate for ML business deployments. For most ML projects, the term “AI” goes entirely too far — it alludes to human-level capabilities. In fact, when you unpack the meaning of “AI,” you discover just how overblown a buzzword it is: If it doesn’t mean artificial general intelligence, a grandiose goal for technology, then it just doesn’t mean anything at all.

You might think that news of “major AI breakthroughs” would do nothing but help machine learning’s (ML) adoption. If only. Even before the latest splashes — most notably OpenAI’s ChatGPT and other generative AI tools — the rich narrative about an emerging, all-powerful AI was already a growing problem for applied ML. That’s because for most ML projects, the buzzword “AI” goes too far. It overly inflates expectations and distracts from the precise way ML will improve business operations.

Most practical use cases of ML — designed to improve the efficiencies of existing business operations — innovate in fairly straightforward ways. Don’t let the glare emanating from this glitzy technology obscure the simplicity of its fundamental duty: the purpose of ML is to issue actionable predictions , which is why it’s sometimes also called predictive analytics . This means real value, so long as you eschew false hype that it is “highly accurate,” like a digital crystal ball.

This capability translates into tangible value in an uncomplicated manner. The predictions drive millions of operational decisions. For example, by predicting which customers are most likely to cancel, a company can provide those customers incentives to stick around. And by predicting which credit card transactions are fraudulent, a card processor can disallow them. It’s practical ML use cases like those that deliver the greatest impact on existing business operations, and the advanced data science methods that such projects apply boil down to ML and only ML.

Here’s the problem: Most people conceive of ML as “AI.” This is a reasonable misunderstanding. But “AI” suffers from an unrelenting, incurable case of vagueness — it is a catch-all term of art that does not consistently refer to any particular method or value proposition. Calling ML tools “AI” oversells what most ML business deployments actually do. In fact, you couldn’t overpromise more than you do when you call something “AI.” The moniker invokes the notion of artificial general intelligence (AGI), software capable of any intellectual task humans can do.

This exacerbates a significant problem with ML projects: They often lack a keen focus on their value — exactly how ML will render business processes more effective. As a result, most ML projects fail to deliver value . In contrast, ML projects that keep their concrete operational objective front and center stand a good chance of achieving that objective.

What Does AI Actually Mean?

“‘AI-powered’ is tech’s meaningless equivalent of ‘all natural.’”

–Devin Coldewey, TechCrunch

AI cannot get away from AGI for two reasons. First, the term “AI” is generally thrown around without clarifying whether we’re talking about AGI or narrow AI , a term that essentially means practical, focused ML deployments. Despite the tremendous differences, the boundary between them blurs in common rhetoric and software sales materials.

Second, there’s no satisfactory way to define AI besides AGI. Defining “AI” as something other than AGI has become a research challenge unto itself , albeit a quixotic one. If it doesn’t mean AGI, it doesn’t mean anything — other suggested definitions either fail to qualify as “intelligent” in the ambitious spirit implied by “AI” or fail to establish an objective goal. We face this conundrum whether trying to pinpoint 1) a definition for “AI,” 2) the criteria by which a computer would qualify as “intelligent,” or 3) a performance benchmark that would certify true AI. These three are one and the same.

The problem is with the word “intelligence” itself. When used to describe a machine, it’s relentlessly nebulous. That’s bad news if AI is meant to be a legitimate field. Engineering can’t pursue an imprecise goal. If you can’t define it, you can’t build it. To develop an apparatus, you must be able to measure how good it is — how well it performs and how close you are to the goal — so that you know you’re making progress and so that you ultimately know when you’ve succeeded in developing it.

In a vain attempt to fend off this dilemma, the industry continually performs an awkward dance of AI definitions that I call the AI shuffle . AI means computers that do something smart (a circular definition). No, it’s intelligence demonstrated by machines (even more circular, if that’s possible). Rather, it’s a system that employs certain advanced methodologies, such as ML, natural language processing, rule-based systems, speech recognition, computer vision, or other techniques that operate probabilistically (clearly, employing one or more of these methods doesn’t automatically qualify a system as intelligent).

But surely a machine would qualify as intelligent if it seemed sufficiently humanlike, if you couldn’t distinguish it from a human, say, by interrogating it in a chatroom — the famous Turing Test. But the ability to fool people is an arbitrary, moving target, since human subjects become wiser to the trickery over time. Any given system will only pass the test at most once — fool us twice, shame on humanity. Another reason that passing the Turing Test misses the mark is because there’s limited value or utility in doing so. If AI could exist, certainly it’s supposed to be useful.

What if we define AI by what it’s capable of? For example, if we define AI as software that can perform a task so difficult that it traditionally requires a human, such as driving a car, mastering chess, or recognizing human faces. It turns out that this definition doesn’t work either because, once a computer can do something, we tend to trivialized it. After all, computers can manage only mechanical tasks that are well-understood and well-specified. Once surmounted, the accomplishment suddenly loses its charm and the computer that can do it doesn’t seem “intelligent” after all, at least not to the whole-hearted extent intended by the term “AI.” Once computers mastered chess, there was little sentiment that we’d “solved” AI.

This paradox, known as The AI Effect , tells us that, if it’s possible, it’s not intelligent. Suffering from an ever-elusive objective, AI inadvertently equates to “getting computers to do things too difficult for computers to do” — artificial impossibility. No destination will satisfy once you arrive; AI categorically defies definition. With due irony, the computer science pioneer Larry Tesler famously suggested that we might as well define AI as “whatever machines haven’t done yet.”

Ironically, it was ML’s measurable success that hyped up AI in the first place. After all, improving measurable performance is supervised machine learning in a nutshell. The feedback from evaluating the system against a benchmark — such as a sample of labeled data — guides its next improvement. By doing so, ML delivers unprecedented value in countless ways. It has earned its title as “the most important general-purpose technology of our era,” as Harvard Business Review put it. More than anything else, ML’s proven leaps and bounds have fueled AI hype.

All in with Artificial General Intelligence

“I predict we will see the third AI Winter within the next five years… When I graduated with my Ph.D. in AI and ML in ’91, AI was literally a bad word. No company would consider hiring somebody who was in AI.”

–Usama Fayyad, June 23, 2022, speaking at Machine Learning Week

There is one way to overcome this definition dilemma: Go all in and define AI as AGI, software capable of any intellectual task humans can do. If this science fiction-sounding goal were achieved, I submit that there would be a strong argument that it qualified as “intelligent.” And it’s a measurable goal, at least in principle if not in practicality. For example, its developers could benchmark the system against a set of 1,000,000 tasks, including tens of thousands of complicated email requests you might send to a virtual assistant, various instructions for a warehouse employee you’d just as well issue to a robot, and even brief, one-paragraph overviews for how the machine should, in the role of CEO, run a Fortune 500 company to profitability.

AGI may set a clear-cut objective, but it’s out of this world — as unwieldy an ambition as there can be. Nobody knows if and when it could be achieved.

Therein lies the problem for typical ML projects. By calling them “AI,” we convey that they sit on the same spectrum as AGI, that they’re built on technology that is actively inching along in that direction. “AI” haunts ML. It invokes a grandiose narrative and pumps up expectations, selling real technology in unrealistic terms. This confuses decision-makers and dead-ends projects left and right.

It’s understandable that so many would want to claim a piece of the AI pie, if it’s made of the same ingredients as AGI. The wish fulfillment AGI promises — a kind of ultimate power — is so seductive that it’s nearly irresistible.

But there’s a better way forward, one that’s realistic and that I would argue is already exciting enough: running major operations — the main things we do as organizations — more effectively! Most commercial ML projects aim to do just that. For them to succeed at a higher rate, we’ve got to come down to earth. If your aim is to deliver operational value, don’t buy “AI” and don’t sell “AI.” Say what you mean and mean what you say. If a technology consists of ML, let’s call it that.

Reports of the human mind’s looming obsolescence have been greatly exaggerated, which means another era of AI disillusionment is nigh. And, in the long run, we will continue to experience AI winters so long as we continue to hyperbolically apply the term “AI.” But if we tone down the “AI” rhetoric — or otherwise differentiate ML from AI — we will properly insulate ML as an industry from the next AI Winter. This includes resisting the temptation to ride hype waves and refrain from passively affirming starry-eyed decision makers who appear to be bowing at the altar of an all-capable AI. Otherwise, the danger is clear and present: When the hype fades, the overselling is debunked, and winter arrives, much of ML’s true value proposition will be unnecessarily disposed of along with the myths, like the baby with the bathwater.

This article is a product of the author’s work as the Bodily Bicentennial Professor in Analytics at UVA Darden School of Business.

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Rehan Staton, in a graduation cap and gown, is conferred his Harvard law degree on Thursday, 25 May 2023.

Former Maryland trash hauler graduates from Harvard Law School

Rehan Staton became a viral media sensation on his admission, and film-maker Tyler Perry covered his tuition fees

The man who worked as a trash hauler in Maryland before earning international news headlines by gaining admission into the prestigious Harvard Law School has graduated.

Rehan Staton received his juris doctorate from Harvard after walking across the stage in his cap and gown during a commencement ceremony on Thursday afternoon at the school’s campus in Cambridge, Massachusetts.

To cap off what is likely to be among the most uplifting stories of this spring’s US university graduation season, spectators treated Staton to some of the loudest cheers heard during the 90-minute ceremony, including one person who could clearly be heard shouting: “My boy – go, Rehan!”

As a profile published recently on Harvard’s website recounted, Staton and his brother were raised by their father, who worked as many as three jobs at a time as he tried to make ends meet. Even then, the family did not always have a reliable supply of food or electricity.

Staton at one point dreamed of a professional career in taekwondo or boxing, hoping that he could earn a fortune in either sport which would let him lift his family out of poverty. But a serious injury to his shoulder cut those aspirations short, and after high school he got a job alongside his older brother, Reggie, working for a trash and recycling company in Bladensburg, Maryland .

He has openly discussed how some of his colleagues at that job had previously spent time in prison, and they pleaded with him to do his absolute best to get a college education. Staton eventually enrolled at Bowie State University, just south of Baltimore, and later transferred to the University of Maryland.

Staton made remarkable sacrifices to set himself up for success, and so did his family. His brother dropped out of college after their father had a stroke to better support their family financially.

Staton also worked, cleaning garbage bins and collecting trash for three hours in the mornings beginning at 4am before going to classes. But, with his brother’s help, he could also dedicate a substantial amount of his time to studying.

It all paid off. Staton won the distinction of serving as his undergraduate class’s commencement speaker at Maryland, and five of the nine law schools to which he applied accepted him.

He chose to attend Harvard in 2020, and he became a viral media sensation when the news reported on his family’s story.

Staton told Harvard’s website that the news coverage on him was uncomfortable at times. “The media presented it as a happy story – something like, ‘Garbage Man Applies to Harvard, Gets In,’” he said. “But I did it to save my family.”

However, he acknowledges that there were benefits to the attention.

Notably, when Tyler Perry heard Staton’s story, the renowned film-maker stepped up to cover his tuition. Many law professors and attorneys also came forward to mentor Staton.

In addition to pursuing his law degree at Harvard, Staton raised more than $70,000 in donations for the school’s support staff, including food servers, custodians and electricians. The effort led him to found a movement which he has dubbed the Reciprocity Effect , which aims to assist support staff at other universities as well as corporations.

Staton has been preparing to begin a job at a New York law firm which he lined up before graduating alongside more than 730 fellow Harvard Law School students. His accomplishments were recently honored during a break in action at a Boston Celtics playoff basketball game. But he said to Harvard that he is taking every chance he can to let everyone know his success was not at all an individual effort.

Staton, 27, said his boss at the Bladensburg trash company would let him leave work to go to class and then come back. A cousin helped him study for the law school admission test. And then there’s his dad, his brother, and, of course, Perry.

“Although I get credit for working hard, working was the easy part because that I could control,” Staton said. “But I just happened to be around people who cared enough about me.

“I got lucky – but I made the most of my luck.”

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