Balancing Privacy Obligations and Research Aims in a Learning Health Care System

Speaker: Frank Pasquale
Date recorded: May 7, 2015
Health information technology can save lives, cut costs, and expand access to care. But its full promise will only be realized if policymakers broker a “grand bargain” between providers, patients, and administrative agencies.

Health information technology can save lives, cut costs, and expand access to care. But its full promise will only be realized if policymakers broker a “grand bargain” between providers, patients, and administrative agencies. In exchange for subsidizing systems designed to protect intellectual property and secure personally identifiable information, health regulators should have full access to key data those systems collect (once properly anonymized). Moreover, patients deserve to be able to channel certain information flows and gain some basic controls over the presentation, disclosure, and redisclosure of sensitive information. This podcast will describe and examine some legal and technical infrastructure designed to help realize these goals.

Genetic Coercion

Speaker: Ifeoma Ajunwa
Date recorded: Jun 11, 2015
Although we cannot disclaim the utility of genetic data, it is important to consider whether we are being socially and governmentally coerced to relinquish our genetic data.

Ifeoma Ajunwa on genetic coercion. Although we cannot disclaim the utility of genetic data, it is important to consider whether we are being socially and governmentally coerced to relinquish our genetic data. If so, what does this mean for privacy and discrimination? What are the obstacles and potential solutions to securing genetic data?

Recorded on 6/11/2015

When Algorithms Become Culture

Speaker: Tarleton Gillespie
Date recorded: Feb 25, 2016
Search engines, recommendation systems, and edge algorithms on social networking sites: these not only help us find information, they provide a means to know what there is to know and to participate in social and political discourse.

Tarleton Gillespie on how algorithms may now be our most important knowledge technologies, “the scientific instruments of a society at large.” Algorithms are increasingly vital to how we organize human social interaction, produce authoritative knowledge, and choreograph our participation in public life. Search engines, recommendation systems, and edge algorithms on social networking sites: these not only help us find information, they provide a means to know what there is to know and to participate in social and political discourse.

If not as pervasive and structurally central as search and recommendation, trending has emerged as an increasingly common feature of such interfaces and seems to be growing in cultural importance. It represents a fundamentally different logic for how to algorithmically navigate social media: besides identifying and highlighting what might be relevant to “you” specifically, trending algorithms identify what is popular with “us” more broadly.

But while the techniques may be new, the instinct is not: what today might be identified as “trending” is the latest instantiation of the instinct to map public attention and interest, be it surveys and polling, audience metrics, market research, forecasting, and trendspotting. Understanding the calculations and motivations behind the production of these “calculated publics,” in this historical context, helps highlight how these algorithms are relevant to our collective efforts to know and be known.

Rather than discuss the effect of trending algorithms, I want to ask what it means that they have become a meaningful element of public culture. Algorithms, particularly those involved in the movement of culture, are both mechanisms of distribution and valuation, part of the process by which knowledge institutions circulate and evaluate information, the process by which new media industries provide and sort culture. This essay examines the way these algorithmic techniques themselves become cultural objects, get taken up in our thinking about culture and the public to which it is addressed, and get contested both for what they do and what they reveal. We should ask not just how algorithms shape culture, but how they become culture.

Recorded on 2/25/2016