An Unreasonable Ban on Reasonable Competition: The Legal Profession’s Protectionist Stance Against Noncompete Agreements Binding In-House Counsel

In the vast majority of jurisdictions in the United States, a business may protect its confidential information and customer goodwill by conditioning employment on an employee’s acceptance of a covenant not to compete. These covenants are beneficial to the marketplace because they allow employers to provide employees with necessary skills, knowledge, and proprietary information without any fear of misappropriation. Accordingly, noncompete agreements are upheld by courts so long as they pass a fact-specific “reasonableness” test. Notwithstanding the widespread acceptance of reasonable noncompete agreements for all other professionals—including doctors and corporate executives—forty-eight states, following the American Bar Association’s lead, prohibit all noncompete agreements among lawyers. This prohibition is purportedly designed to protect both an attorney’s professional autonomy and a client’s right to choose his counsel. Despite legal commentators’ criticism of the prohibition, several state bar associations have recently extended it beyond the traditional law-firm context to agreements between companies and their in-house counsel. This expansion has transformed a questionable policy of professional self-regulation into an unjustifiable infringement on the legitimate interests of corporate employers. In addition to providing an analysis of the history and ethical norms that justify rejection of the ban’s application to in-house counsel, this Note argues that bar committees that issue opinions supporting the ban’s extension may be susceptible to antitrust liability under the Supreme Court’s new Dental Board standard pertaining to state-action immunity

Reverse-Engineering Satire, or "Paper on Computational Humor Accepted Despite Making Serious Advances"

Humor is an essential human trait. Efforts to understand humor have called out links between humor and the foundations of cognition, as well as the importance of humor in social engagement. As such, it is a promising and important subject of study, with relevance for artificial intelligence and human-computer interaction. Previous computational work on humor has mostly operated at a coarse level of granularity, e.g., predicting whether an entire sentence, paragraph, document, etc., is humorous. As a step toward deep understanding of humor, we seek fine-grained models of attributes that make a given text humorous. Starting from the observation that satirical news headlines tend to resemble serious news headlines, we build and analyze a corpus of satirical headlines paired with nearly identical but serious headlines. The corpus is constructed via Unfun.me, an online game that incentivizes players to make minimal edits to satirical headlines with the goal of making other players believe the results are serious headlines. The edit operations used to successfully remove humor pinpoint the words and concepts that play a key role in making the original, satirical headline funny. Our analysis reveals that the humor tends to reside toward the end of headlines, and primarily in noun phrases, and that most satirical headlines follow a certain logical pattern, which we term false analogy. Overall, this paper deepens our understanding of the syntactic and semantic structure of satirical news headlines and provides insights for building humor-producing systems.Comment: Proceedings of the 33rd AAAI Conference on Artificial Intelligence, 201

A Utility-Theoretic Approach to Privacy in Online Services

Online offerings such as web search, news portals, and e-commerce applications face the challenge of providing high-quality service to a large, heterogeneous user base. Recent efforts have highlighted the potential to improve performance by introducing methods to personalize services based on special knowledge about users and their context. For example, a user's demographics, location, and past search and browsing may be useful in enhancing the results offered in response to web search queries. However, reasonable concerns about privacy by both users, providers, and government agencies acting on behalf of citizens, may limit access by services to such information. We introduce and explore an economics of privacy in personalization, where people can opt to share personal information, in a standing or on-demand manner, in return for expected enhancements in the quality of an online service. We focus on the example of web search and formulate realistic objective functions for search efficacy and privacy. We demonstrate how we can find a provably near-optimal optimization of the utility-privacy tradeoff in an efficient manner. We evaluate our methodology on data drawn from a log of the search activity of volunteer participants. We separately assess users’ preferences about privacy and utility via a large-scale survey, aimed at eliciting preferences about peoples’ willingness to trade the sharing of personal data in returns for gains in search efficiency. We show that a significant level of personalization can be achieved using a relatively small amount of information about users