Related Insights

CDT Comments to the U.S. State Department on Proposed Collection of Visa Applicants' Social Media Information

CDT urges the State Department to withdraw the agency’s proposed information collection under Public Notices 10260 and 10261. The proposal asks all immigrant and nonimmigrant visa applicants to provide social media identifiers, and email addresses used in the past five years, among other information. This astronomical collection would have an immediate impact on 14.7 million visa applicants, and thousands, if not millions, more third parties whose data could be collaterally reviewed.

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Mixed Messages? The Limits of Automated Social Media Content Analysis

This paper explains the capabilities and limitations of tools for analyzing the text of social media posts and other online content. It is intended to help policymakers understand and evaluate available tools and the potential consequences of using them, and focuses specifically on the use of natural language processing (NLP) tools for analyzing the text of social media posts.

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CDT Statement on Government Use of Algorithmic Decision-Making Tools to NYC Council Committee on Technology

The City of New York has an obligation to understand, scrutinize, and explain how its algorithms make decisions affecting New Yorkers. At minimum, the city should ensure and demonstrate to the public that NYC’s algorithmic decision-making tools (1) are aligned with the city’s policy goals and the public interest; (2) work as intended; (3) do not use data to marginalize minority or vulnerable populations and exacerbate inequality; (4) provide meaningful transparency to New Yorkers so that they can appeal and seek remedy for automated decisions that are incorrect, unjust, or contrary to law.

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Comments to the EEOC on the Implications of Big Data in Employment

CDT commends the Equal Employment Opportunity Commission’s effort to protect equal opportunity in employment by examining how the collection and use of data may exacerbate structural inequality. In particular, we urge the Commission to scrutinize the use of automated decision-making systems in hiring, management, and employee evaluation practices and to ensure that such systems promote fairness, equality, and diversity.

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Digital Decisions: Policy Tools in Automated Decision-Making

Digital technology has empowered new voices, made the world more accessible, and increased the speed of almost every decision we make as businesses, communities, and individuals. Much of this convenience is powered by lines of code that rapidly execute instructions based on rules set by programmers (or, in the case of machine learning, generated from statistical correlations in massive datasets)—otherwise known as algorithms.

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Digital Decisions: Building Trust in Algorithms

Sophisticated statistical analysis is a pillar of decision making in the 21st Century, including employment, lending, and policing. Automated systems also mediate our access to information and community through search results and social media. These technologies are pivotal to day-to-day life, but the processes that govern them are not transparent.

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