Privacy Commitments

What responsibilities do corporations have with regard to their consumers’ information? Many articles have looked at ways to make personal information the “property” of the consumer. Property approaches attempt to overlay personal information on the legal frameworks of trade secret, trademark, and copyright law. While each approach has its merits, and contributes to the field, none of the proposals generate a concrete way for a consumer to enforce his or her rights against a company. The proposals all suffer from the same fatal flaw, a new system must not just create a consumer right but also balance the inequities in bargaining power between a consumer and a large corporation. In patent law, there are similar conflicts of interest between a private property owner’s (patent holder’s) right to create a successful business and the ability of others (potential patent licensees) to negotiate a reasonable royalty rate. In response to this conflict, the patent field relies upon a self-regulatory system where patent holders agree to be “Reasonable and Non-Discriminatory” in their licensing practices. This system produces two concrete benefits. First, it helps correct the power imbalance between two negotiating parties. Second, it creates a third-party breach of contract right for a party who could not normally bring a case. As a process, a patent holder agrees to Reasonable and Non-Discriminatory practices (“RAND”) with a standards-setting organization. Then, if the patent holder does not reasonably license their patent to a third party who wishes to negotiate for said license, the third party can sue the patent holder, even though the two parties never finalized an agreement. This paper argues a similar system would lend much-needed structure to online data use. Creating a voluntary, quasi-self-regulatory regime would allow greater transparency as to corporate data practices, facilitate the creation of industry standards as to “reasonable” data use, balance the interests of corporation and consumer, and create a legal right for consumers who have had their personal data misused (in a way that could more easily support a classaction). The paper proceeds in four parts. The first part looks at current norms of data use and the issues a proposed system would need to address. The second part reviews and summarizes past intellectual property approaches to privacy, as well as each approach’s respective drawbacks. The third part examines RAND commitments and their operation in the realm of patent law. The fourth part discusses a system for implementing RAND commitments in privacy law, and addresses potential benefits and drawbacks of the approach

Smart Contracts, Blockchain, and the Next Frontier of Transactional Law

Smart contracts are an emerging technology that could revolutionize commercial transactions by eliminating inefficiencies and uncertainty created by the current transactional ecosystem of lawyers, courts, regulators, banks, and other parties with divergent interests. However, a lack of consensus around how smart contracts are implemented, uncertainty regarding enforceability, and scarcity of on point statutes and case law means that a stable legal, commercial and technical smart contract landscape has yet to emerge. The implementation of universal legal, technical and commercial standards and best practices will reduce uncertainty and promote widespread adoption and use of smart contracts

How Machines Learn: Where Do Companies Get Data for Machine Learning and What Licenses Do They Need?

Machine learning services ingest customer data in order to provide refined, customized services. Machine learning algorithms are increasingly prominent in multiple sectors within the software-as-a-service industry including online advertising, health diagnostics, and travel. However, very little has been written on the rights a company utilizing machine learning needs to obtain in order to use customer data to improve its own products or services. Machine learning encompasses multiple types of data use and analysis, including (a) supervised machine learning algorithms, which take specific data provided in a tagged and classified format to deliver specific predictable output; and (b) unsupervised machine learning algorithms, where untagged data is processed in order to look for patterns and correlations without a specified output. This Article introduces the reader to the types of data use involved in various machine learning models, the level of data retention normally required for each model, and the risks of using personal information or re-identifiable data in connection with machine learning. The paper also discusses the type of license a commercial provider and consumer would need to enter into for various types of machine learning software. Finally, the paper proposes best practices for ensuring adequate rights are obtained through legal agreements so that machines may self-improve and innovate

Automated Vehicles

https://digitalcommons.law.uw.edu/techclinic/1000/thumbnail.jp