Life, Liberty, and Trade Secrets: Intellectual Property in the Criminal Justice System

Wexler, Rebecca

doi:10.2139/ssrn.2920883

Cited by 46 publications

(55 citation statements)

References 19 publications

(1 reference statement)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Moreover, algorithms and data are often protected as trade secrets and are inaccessible to the general public-including data regarding the success of the algorithm's past predictions, which is collected and used to train the algorithm so that it can make more accurate predictions in the future (Wexler, 2018). Consequently, there is no way to tell why one piece of content is deemed legitimate and another subject to removal.…”

Section: Oversight and Its Limitsmentioning

confidence: 99%

Contesting algorithms: Restoring the public interest in content filtering by artificial intelligence

Elkin-Koren

2020

Big Data & Society

View full text Add to dashboard Cite

In recent years, artificial intelligence has been deployed by online platforms to prevent the upload of allegedly illegal content or to remove unwarranted expressions. These systems are trained to spot objectionable content and to remove it, block it, or filter it out before it is even uploaded. Artificial intelligence filters offer a robust approach to content moderation which is shaping the public sphere. This dramatic shift in norm setting and law enforcement is potentially game-changing for democracy. Artificial intelligence filters carry censorial power, which could bypass traditional checks and balances secured by law. Their opaque and dynamic nature creates barriers to oversight, and conceals critical value choices and tradeoffs. Currently, we lack adequate tools to hold them accountable. This paper seeks to address this gap by introducing an adversarial procedure— – Contesting Algorithms. It proposes to deliberately introduce friction into the dominant removal systems governed by artificial intelligence. Algorithmic content moderation often seeks to optimize a single goal, such as removing copyright-infringing materials or blocking hate speech, while other values in the public interest, such as fair use or free speech, are often neglected. Contesting algorithms introduce an adversarial design which reflects conflicting values, and thereby may offer a check on dominant removal systems. Facilitating an adversarial intervention may promote democratic principles by keeping society in the loop. An adversarial public artificial intelligence system could enhance dynamic transparency, facilitate an alternative public articulation of social values using machine learning systems, and restore societal power to deliberate and determine social tradeoffs.

show abstract

Section: Oversight and Its Limitsmentioning

confidence: 99%

Contesting algorithms: Restoring the public interest in content filtering by artificial intelligence

Elkin-Koren

2020

Big Data & Society

View full text Add to dashboard Cite

show abstract

“…Regulating the policing aspects of the smart city will post an even greater challenge because of the source of these new technologies. One consequence of greater automation in policing is the increasing influence of privatisation (Wexler, 2018(Wexler, , p. 1349. Police agencies do not create licence-plate readers, facial-recognition programs and other uses of artificial intelligence.…”

Section: The Regulatory Gapmentioning

confidence: 99%

“…The dominant manufacturer of cell site simulators, used by police to trick suspects' phones into providing location information, has relied upon nondisclosure agreements to prevent police agencies from disclosing information about their products in open records requests (Joh, 2017). Similarly, criminal defendants have been unable to learn about the source codes in forensic DNA software and in audio surveillance software used to help prosecute them because of developers' trade secrets claims (Wexler, 2018). As police agencies and smart cities increase their reliance on privately developed technologies, we can expect to see more confrontations between public claims to transparency and private assertions about intellectual property.…”

Section: The Regulatory Gapmentioning

confidence: 99%

Policing the smart city

Joh

2019

International Journal of Law in Context

View full text Add to dashboard Cite

What will be the consequences for policing as cities become increasingly ‘smarter’? The emerging questions about policing and the smart city have thus far focused primarily on the increased surveillance capacity that a highly networked urban setting provides for law enforcement. More cameras and sensors will mean more watching and less freedom from being watched. The perception of ubiquitous government surveillance might quell dissent and inhibit free expression. As a result, concerns about policing and the smart city echo other responses to surveillance technologies. This essay proposes a different analysis: as cities become ‘smarter’, they increasingly embed policing itself into the urban infrastructure. Policing is inherent to the smart city.

show abstract

“…There is an expectation of some degree of transparency about how decisions are made, especially for public sector uses. In the criminal justice context, some legal scholars argue that the use of expert models protected by trade secrecy in judicial decisions effectively constitutes a form of evidentiary privilege that is harmful and ahistorical (Wexler, 2018). At a minimum, a degree of transparency enables the possibility of dissent or recourse for inequitable decisions.…”

Section: Algorithmic Transparency Is Important But Is Not a Panaceamentioning

confidence: 99%

“…This assumption is far from a settled norm, and strong admissible business and operational reasons exist for keeping algorithmic models private. However, some legal scholars have identified negative externalities resulting from assertions of intellectual property restriction in the public-sector use of algorithms or in the development of algorithmic models (Wexler, 2018;Levendowski, 2018). The level of transparency (for data and models) may be subject to deliberation in the purpose review step.…”

Section: Technical Recommendationsmentioning

confidence: 99%

Algorithmic Equity: A Framework for Social Applications

Osoba¹,

Boudreaux²,

Saunders³

et al. 2019

View full text Add to dashboard Cite

This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited. Permission is given to duplicate this document for personal use only, as long as it is unaltered and complete. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial use. For information on reprint and linking permissions, please visit www.rand.org/pubs/permissions. The RAND Corporation is a research organization that develops solutions to public policy challenges to help make communities throughout the world safer and more secure, healthier and more prosperous. RAND is nonprofit, nonpartisan, and committed to the public interest. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.

show abstract

Life, Liberty, and Trade Secrets: Intellectual Property in the Criminal Justice System

Cited by 46 publications

References 19 publications

Contesting algorithms: Restoring the public interest in content filtering by artificial intelligence

Contesting algorithms: Restoring the public interest in content filtering by artificial intelligence

Policing the smart city

Algorithmic Equity: A Framework for Social Applications

Contact Info

Product

Resources

About