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Lazer, David

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Lazer

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Lazer, David
Author's Publications: search.filters.isAuthorOfPublication.4c4ee6fd-3c81-41cf-89c6-539c927db1cb

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    Machine Behaviour
    (Springer Science and Business Media LLC, 2019-04) Cebrian Ramos, Manuel; Christakis, Nicholas A.; Couzin, Iain D.; Jackson, Matthew O.; Jennings, Nicholas R.; Kamar, Ece; Kloumann, Isabel M.; Larochelle, Hugo; Lazer, David; McElreath, Richard; Mislove, Alan; Parkes, David; Pentland, Alex ‘Sandy’; Roberts, Margaret E.; Shariff, Azim; Tenenbaum, Joshua B.; Wellman, Michael; Rahwan, Iyad; Obradovich, Nick; Bongard, Josh; Bonnefon, Jean-François; Breazeal, Cynthia; Crandall, Jacob
    Machines powered by artificial intelligence increasingly mediate our social, cultural, economic and political interactions. Understanding the behaviour of artificial intelligence systems is essential to our ability to control their actions, reap their benefits and minimize their harms. Here we argue that this necessitates a broad scientific research agenda to study machine behaviour that incorporates and expands upon the discipline of computer science and includes insights from across the sciences. We first outline a set of questions that are fundamental to this emerging field and then explore the technical, legal and institutional constraints on the study of machine behaviour.
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    Google Flu Trends Still Appears Sick: An Evaluation of the 2013-2014 Flu Season
    (Social Science Electronic Publishing, 2014) Lazer, David; Kennedy, Ryan; King, Gary; Vespignani, Alessandro
    In response to its poor performance during the 2012-2013 flu season, Google Flu Trends (GFT) engineers announced a redesign of the GFT algorithm. Two changes were made: (1) dampening anomalous media spikes and (2) using ElasticNet, rather than regression, for estimation. This paper identifies several problems that persist in the new algorithm. First, the transparency problems identified in our earlier Science paper appear to have, if anything, become worse. Second, there are reasons to doubt whether a spike in media attention was the only, or primary, cause of GFT's errors. Finally, there is strong evidence that GFT is still not using all the information at its disposal to make accurate measurements of flu prevalence. While it is too early to give a complete evaluation of the new algorithm, these results are discouraging.
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    The Parable of Google Flu: Traps in Big Data Analysis
    (American Association for the Advancement of Science (AAAS), 2014) Lazer, David; Kennedy, R.; King, Gary; Vespignani, A.
    Large errors in flu prediction were largely avoidable, which offers lessons for the use of big data. In February 2013, Google Flu Trends (GFT) made headlines but not for a reason that Google executives or the creators of the flu tracking system would have hoped. Nature reported that GFT was predicting more than double the proportion of doctor visits for influenza-like illness (ILI) than the Centers for Disease Control and Prevention (CDC), which bases its estimates on surveillance reports from laboratories across the United States ( 1, 2). This happened despite the fact that GFT was built to predict CDC reports. Given that GFT is often held up as an exemplary use of big data ( 3, 4), what lessons can we draw from this error?
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    The Science of Fake News
    Lazer, David; Baum, Matt; Benkler, Yochai; Berinsky, Adam; Greenhill, Kelly; Menczer, Filippo; Metzger, Miriam; Nyhan, Brendan; Pennycook, Gordon; Rothschild, David; Schudson, Michael; Sloman, Steven; Sunstein, Cass; Thorson, Emily; Watts, Duncan; Zittrain, Jonathan
    The rise of fake news highlights the erosion of long-standing institutional bulwarks against misinformation in the internet age. Concern over the problem is global. However, much remains unknown regarding the vulnerabilities of individuals, institutions, and society to manipulations by malicious actors. A new system of safeguards is needed. Below, we discuss extant social and computer science research regarding belief in fake news and the mechanisms by which it spreads. Fake news has a long history, but we focus on unanswered scientific questions raised by the proliferation of its most recent, politically oriented incarnation. Beyond selected references in the text, suggested further reading can be found in the supplementary materials.
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    Networks, Hierarchies, and Markets: Aggregating Collective Problem Solving in Social Systems
    (John F. Kennedy School of Government, Harvard University, 2009) Lazer, David; Mergel, Ines; Ziniel, Curt; Neblo, Michael
    How do decentralized systems collectively solve problems? Here we explore the interplay among three canonical forms of collective organization—markets, networks, and hierarchies—in aggregating decentralized problem solving. We examine these constructs in the context of how the offices of members of Congress individually and collectively wrestle with the Internet, and, in particular, their use of official websites. Each office is simultaneously making decisions about how to utilize their website. These decisions are only partially independent, where offices are looking at each other for lessons, following the same directives from above about what to do with the websites, and confront the same array of potential vendors to produce their website. Here we present the initial results from interviews with 99 Congressional offices and related survey of 100 offices about their decisions regarding how to use official Member websites. Strikingly, we find that there are relatively few efforts by offices to evaluate what constituents want or like on their websites. Further, we find that diffusion occurs at the “tip of the iceberg”: offices often look at each others’ websites (which are publicly visible), but rarely talk to each other about their experiences or how they manage what is on their websites (which are not publicly visible). We also find that there are important market drivers of what is on websites, with the emergence of a small industry of companies seeking to serve the 440 Members. Hierarchical influences—through the House and through the party conferences—also constrain and subsidize certain practices.
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    Computational Social Science
    (American Association for the Advancement of Science, 2009) Lazer, David; Pentland, Alex; Adamic, Lada; Aral, Sinan; Barabási, Albert-László; Brewer, Devon; Christakis, Nicholas A.; Contractor, Noshir; Fowler, James; Gutmann, Myron; Jebara, Tony; King, Gary; Macy, Michael; Roy, Deb; Van Alstyne, Marshall
    A field is emerging that leverages the capacity to collect and analyze data at a scale that may reveal patterns of individual and group behaviors.