Intellectual Property in an Age of Open Innovation, Machine Learning, and “Public” Policy Creation — Research Roundup February 2018

Intellectual Property in an Age of Open Innovation, Machine Learning, and “Public” Policy Creation — Research Roundup February 2018

*The Research Roundup is a semi-regular list of outside research we have found interesting and think is worth sharing. The views and conclusions of the papers’ authors do not necessarily reflect the opinions of anyone affiliated with TPI.*

This edition of Research Roundup begins with several articles that focus on the interactions between intellectual property (IP) institutions, digital innovation, and creativity. The first paper below, for example, “Digital ‘Mash-Ups,’ Patents, and Copyright,” by Kevin Boudreau, Lars Bo Jeppesen, and Milan Miric, examines patents and copyright in mobile app development. The authors note that new types of products will require more focused attention on how and when patent and copyright use happen in tandem. Where developers of similar apps have historically relied on similar patterns of patent use, new “mash-up” products – each a combination of components with their own patent or copyright – now require developers to get creative. Click through for a more detailed description of this work, other research into IP, and research into crowdsourcing, open innovation, and digital platforms as facilitators of public (in every sense of the word) policy creation.

Speaking of digital IP, on March 20, 2018, TPI will host an event on music licensing in the digital age. The conference will explore the tensions and challenges in the industry today and discuss four pieces of reform legislation currently up for debate.  (Click here to register!).


Digital ‘Mash-Ups,’ Patents, and Copyright

Copy Rights: The Politics of Copying and Creativity

Copyright, Technology and the CJEU: An Empirical Study

Where and when AI and CI Meet

Crowdsourcing: a new tool for policy-making?

Machine learning, social learning and the governance of self-driving cars

Autonomous Driving Systems: A Preliminary Naturalistic Study of the Tesla Model S


Descriptions of papers below are edited abstracts from authors


Intellectual Property: Can Its Institutions Keep Pace with Technological Change?

Digital ‘Mash-Ups,’ Patents, and Copyright
Kevin Boudreau, Lars Bo Jeppesen, and Milan Miric

Are our intellectual property (IP) institutions effective for a new generation of digital innovations? This paper analyzes a novel dataset on mobile app developers’ use of patents and copyright, product revenues, licensing and outsourcing, and product and developer characteristics. It finds within-industry heterogeneity in patent and copyright use, even among seemingly similar suppliers selling similar products. This pattern of IP use, along with consequent revenues and propensity to engage in IP trade is closely associated with the specific nature of innovations embodied in the products. Therefore, whereas patent and copyright use historically have differed across industries while tending to be similar across suppliers within the same industry, the “mash-up” nature of digital products (amalgams of programs, datasets, graphics, algorithms, etc.) results in unusually finer-grained differences within industries. Pliant digital product development choices and IP choices go hand-in-hand.


Copy Rights: The Politics of Copying and Creativity
John Street, Keith Negus, Adam Behr

This article analyzes the politics of copyright and copying. Copyright is an increasingly important driver of the modern economy, but its significance extends beyond this. This paper argues that the Internet matters not just for the distribution of rewards and resources in the creative industries, but as a site upon which established political concerns – collective and individual interests and identities – are articulated and negotiated and within which notions of ‘originality’, ‘creativity’ and ‘copying’ are politically constituted. Set against the background of the increasing economic value attributed to the creative industries, the impact of digitalization on them and the European Union’s Digital Single Market strategy, this article reveals how copyright policy and the underlying assumptions about ‘copying’ and ‘creativity’ express (often unexamined) political values and ideologies. Drawing on a close reading of policy statements, official reports, court cases and interviews with stakeholders, we explore the multiple political aspects of copyright, showing how copyright policy operates to advantage particular interests and practices and to acknowledge only specific forms of creative work.


Copyright, Technology, and the [Court of Justice of the European Union]: An Empirical Study
Tito Rendas

The framework of rights and exceptions in EU copyright law is often criticized for lacking the flexibility to keep up with rampant technological change. Courts, however, occasionally refuse to abide by the framework’s interpretative constraints, in order to accommodate certain technology-enabled uses. In some cases, the Court of Justice of the European Union (CJEU) has adopted flexible readings of the exceptions in question. In other cases, national courts have openly construed the three-step test as an enabling standard, rather than as a restrictive one. Using the relevant case law of the CJEU as its research sample, this article aims to empirically investigate the extent to which European courts are deciding in such a flexible manner and rendering technology-enabled uses to be non-infringing. This study reveals that the number of uses that the CJEU has deemed non-infringing exceeds those that have been held infringing. It shows, moreover, that the CJEU has circumvented interpretative constraints in the majority of these cases. These findings suggest that the existing framework is indeed unfit for times of accelerated technological change, but for a different reason than that commonly thought.


Technology & Governance

Where and when AI and CI Meet: Exploring the Intersection of Artificial and Collective Intelligence Towards the Goal of Innovating how we Govern
Stefaan G. Verhulst

This paper explores the intersection of Artificial Intelligence (AI) and Collective Intelligence (CI), within the context of innovating how we govern. It begins with the premise that advances in technology provide policymakers with two important new assets: data and connected people. The application of AI and CI allows them to leverage these assets toward solving public problems. However, both AI and CI have serious challenges that may limit their value within a governance context, including biases embedded in datasets and algorithms, undermining trust in AI; and high transaction costs to manage people’s engagement limiting CI to scale. The main argument in this paper is that some of the challenges of AI and CI can be addressed through greater interaction of CI and AI. Several real-world examples are provided throughout the paper to illustrate emerging trends toward both types of intelligence, and their applications to solve public problems or make policy decision differently.


Technology & Public Opinion

Crowdsourcing: a new tool for policy-making?
Araz Taeihagh

Crowdsourcing is rapidly evolving and applied in situations where ideas, labor, opinion or expertise of large groups of people is used. Crowdsourcing is now used in various policy-making initiatives; however, this use has usually focused on open collaboration platforms and specific stages of the policy process, such as agenda-setting and policy evaluations. Other forms of crowdsourcing have been neglected in policy-making, with a few exceptions. This article examines crowdsourcing as a tool for policy-making and explores the nuances of the technology and its use and implications for different stages of the policy process. The article addresses questions surrounding the role of crowdsourcing and whether it can be considered as a policy tool or as a technological enabler and investigates the current trends and future directions of crowdsourcing.


Artificial Intelligence, Machine Learning, and Human Learning in Autonomous Vehicles

Machine learning, social learning and the governance of self-driving cars
Jack Stilgoe

Self-driving cars, a quintessentially ‘smart’ technology, are not born smart. The algorithms that control their movements are learning as the technology emerges. Self-driving cars represent a high-stakes test of the powers of machine learning, as well as a test case for social learning in technology governance. Society is learning about the technology while the technology learns about society. Understanding and governing the politics of this technology means asking ‘who is learning, what are they learning and how are they learning?’ Focusing on the successes and failures of social learning around the much-publicized crash of a Tesla Model S in 2016, the author argues that trajectories and rhetoric of machine learning in transport pose a substantial governance challenge. ‘Self-driving’ or ‘autonomous’ cars are misnamed. As with other technologies, they are shaped by assumptions about social needs, solvable problems, and economic opportunities. Governing these technologies in the public interest means improving social learning by constructively engaging with the contingencies of machine learning.


Autonomous Driving Systems: A Preliminary Naturalistic Study of the Tesla Model S
Mica R. Endsley

More than 14 companies are currently developing autonomous and semi-autonomous vehicles. These vehicles may improve driving safety and convenience. Or, they may create new challenges for drivers, particularly with regard to situation awareness (SA) and autonomy interaction. This paper presents a naturalistic driving study of the autonomy features in the Tesla Model S, recording experiences over a 6-month period. It includes assessments of SA and issues with the autonomy. This analysis provides insights into the challenges that drivers may face in dealing with new autonomous automobiles in realistic driving conditions, and it extends previous research on human-autonomy interaction to the driving domain. This study identified issues with driver training, mental model development, mode confusion, unexpected mode interactions, SA, and susceptibility to distraction.



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