What follows will trace along the familiar storylines of Joseph Campbell’s construction of the Hero’s Journey or it may venture outward in a similar manner to Santiago, from Ernest Hemingway’s The Old Man and the Sea. A stronger likelihood is that whatever path I am on will exist as a continually moving, practically unidentifiable point on some infinite continuum of these and other stories.

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Alternative title: the young man on the sea (of the banks of the Ölfusa River, South of Selfoss — Iceland)

Lots of things have been going on in my life over the past couple months. Devotees of my instagram feed know that I spent a month traveling around Europe and since returning have taken up residence in Cambridge, Massachusetts. This is because I am now doing research at the MIT Media Lab, where I will be serving as the Managing Editor of the MIT Computational Law Report — a publication that will launch in the fall. …


“Evolution forged the entirety of sentient life on this planet using only one tool, the mistake.” — Robert Ford, Westworld

Last week marks one year after I officially started working as an Innovation Fellow with the ABA Center for Innovation. Getting the opportunity to speak about my experience at the ABA Annual Meeting was something I never imagined I would have the opportunity to do. I truly cannot believe how much the inaugural group of fellows was able to accomplish. …


Legal data is incredibly valuable. In the insurance field, data about the average similarity score of the clauses in a policy — a measure of the relative similarity or divergence for one clause in relation to a set of other similar clauses that are meant to accomplish the same function — can be used to instantly show how one policy stacks up against another. Data about the composition of cyber policies can be used to reveal which clauses or information a given policy might be missing.

In order to unlock the value of this data, however, legal professionals need to understand how to properly leverage it. One of the most straightforward contexts for doing this is in the drafting of legal language. And the key here, I believe is making the data easily digestible at a basic level. …


The following series of posts survey how the seemingly nascent features of an insurance policy — policy metadata — can be levereaged to create artificial intelligence that improve the quality and efficiency of drafting these policies. By using even the most rudementary AI tools, underwriters and legal professionals can begin to speed up the work they are doing by more effectively spotting areas that deviate from industry standards.

On the face of it, something as limited as the number of clauses in an insurance policy would not be a helpful feature. However, this is one example of a feature that can be used in connection with other features to provide more transparency about the areas of an insurance policy that have historically been opaque and difficult to understand. For example, information about the ways usage of a clause changes across different lines of business can be mapped out; the way that different carriers refer to the same type of information can be more easily compared; and, perhaps most importantly, individual underwriters can gain a real-time understanding of whether or not they are drafting a policy that fits within a given regulatory framework or their organization’s goals. …


I. Reviewing documents

The way documents are reviewed is broken. Having been through law school and worked as a doc reviewer in a law firm, I thought I knew what document analysis entailed. It was not until I expanded my perception of what document review could be that I realized how wrong that thought was.

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With advances in computing technology, documents can be broken down into a bunch of different pieces. Those pieces can then be scored against each other to deliver deeper insights about a document or series of documents that would otherwise be difficult to achieve. The above spreadsheet is actually one document, broken down into about 100 rows and 10 columns. …


In March, I started working for RiskGenius. As a company, we store files for different groups within the insurance community and provide layers of analytics on top of that to improve the operations of these groups. The type of analytics we provide range from a red line feature, which allows people to compare language from two policies against each other, to a clause score, which leverages multiple types of machine learning to gain an understanding of how similar one clause is to other clauses within a specific line of business.Since I started, I’ve spent a considerable amount of time thinking about how to better understand the cyber liability insurance marketplace, with specific attention to how language is used in cyber policies. Along with a team of policy analysts, I have looked at the language of 4600+ cyber liability clauses, from 45+ cyber liability insurance policies, from 25+ carriers, from 5+ countries around the world. We reduced the agreements into forms and clauses, broke the clauses down by type, and quantitatively scored them against one another to see what the market for cyber liability actually looks like. …


At the end of the week, the SEC will, one way or another, rule on whether or not to permit the Winklevoss Bitcoin Trust ETF.* This decision will have several direct ramifications on the financial industry. Notably, approval of the ETF would improve the liquidity of the cryptocurrency, the price of Bitcoin would likely continue to rise as it becomes easier for less savvy investors to jump into the game, and we might soon see this and other pending ETFs, notably SolidX, traded on the New York Stock Exchange. Disapproval would likely do the opposite and subsequently preserve the monoculture of national, fiat currencies for the foreseeable future. …

bryan wilson

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