<div dir="ltr"><div class="" style="font-size:12.8px"><div class="im"><b style="font-size:12.8px"><span class="">Big</span> <span class="">Data</span> / </b><a href="http://www.liebertpub.com/big" target="_blank" style="font-size:12.8px">http://www.<wbr>liebertpub.com/<span class="">big</span></a><b style="font-size:12.8px"><br></b><span style="font-size:12.8px"><span class="">Call</span> <span class="">for</span> <span class="">Papers</span>: <span class="">Special</span> <span class="">Issue</span> on Social and Technical Trade-Offs</span><br style="font-size:12.8px"><br style="font-size:12.8px"><span style="font-size:12.8px">Guest Editors:</span><br style="font-size:12.8px"><span style="font-size:12.8px">Solon Barocas / Microsoft Research</span><br style="font-size:12.8px"><span style="font-size:12.8px">danah boyd / </span><span style="font-size:12.8px">Data</span><span style="font-size:12.8px"> & Society and Microsoft Research</span><br style="font-size:12.8px"><span style="font-size:12.8px">Sorelle Friedler / Haverford College and </span><span style="font-size:12.8px">Data</span><span style="font-size:12.8px"> & Society</span><br style="font-size:12.8px"><span style="font-size:12.8px">Hanna Wallach / Microsoft Research and UMass Amherst</span><br style="font-size:12.8px"><br style="font-size:12.8px"><span style="font-size:12.8px">Deadline <span class="">for</span> manuscript submission: <b>September 15, 2016</b></span><br style="font-size:12.8px"><br style="font-size:12.8px"><span style="font-size:12.8px">This <span class="">special</span> <span class="">issue</span> on Social and Technical Trade-Offs aims to serve two main purposes:</span><br style="font-size:12.8px"><div style="font-size:12.8px"><ol><li style="margin-left:15px">To highlight exciting and novel work in machine learning, artificial intelligence, <span class="">data</span> mining, and <span class="">data</span> science that articulates, examines, challenges, and addresses the technical and social trade-offs involved in the analysis and interpretation of <span class="">big</span> <span class="">data</span>.</li><li style="margin-left:15px">To pose practical, grounded, and socially-oriented challenges <span class="">for</span> researchers in machine learning, artificial intelligence, <span class="">data</span> mining, and <span class="">data</span> science to motivate and guide their research.</li></ol></div><div style="font-size:12.8px"><br></div><span style="font-size:12.8px">Working with “</span><span style="font-size:12.8px">big</span><span style="font-size:12.8px"> </span><span style="font-size:12.8px">data</span><span style="font-size:12.8px">” isn't easy, especially when it involves social </span><span style="font-size:12.8px">data</span><span style="font-size:12.8px">. Researchers and practitioners must make hard choices when cleaning and processing </span><span style="font-size:12.8px">data</span><span style="font-size:12.8px">, grapple with biased </span><span style="font-size:12.8px">data</span><span style="font-size:12.8px"> sets and missing </span><span style="font-size:12.8px">data</span><span style="font-size:12.8px">, and evaluate the social and technical trade-offs involved in analysis and interpretation. What are the ethical implications of these choices? What happens when we get it wrong? How can we prioritize reproducibility? What happens when biased </span><span style="font-size:12.8px"><span class="">data</span> </span><span style="font-size:12.8px">and imperfect methods are combined in unexpected ways? This <span class="">special</span> <span class="">issue</span> will examine the trade-offs that emerge from the interconnected nature of the social and technical decision-making that lies at the heart of </span><span style="font-size:12.8px">big</span><span style="font-size:12.8px"> </span><span style="font-size:12.8px">data</span><span style="font-size:12.8px">.</span><br style="font-size:12.8px"><br style="font-size:12.8px"><span style="font-size:12.8px">We encourage submissions that focus on challenges and questions involving large-scale social </span><span style="font-size:12.8px">data</span><span style="font-size:12.8px">, and that are deployed (or are in the process of being deployed) in the real world.</span><br style="font-size:12.8px"><br style="font-size:12.8px"><span style="font-size:12.8px">Area of focus include (but are not limited to):</span><br style="font-size:12.8px"><div style="font-size:12.8px"><ul><li style="margin-left:15px">Surveillance and privacy</li><li style="margin-left:15px">Healthcare, medicine, and public health</li><li style="margin-left:15px">Criminal justice and policing</li><li style="margin-left:15px">Education and learning</li><li style="margin-left:15px">Disaster relief</li><li style="margin-left:15px">Urban planning, housing, and infrastructure</li><li style="margin-left:15px">Finance, scoring, and insurance</li><li style="margin-left:15px">Public administration and public policy</li><li style="margin-left:15px">Autonomous experimentation</li><li style="margin-left:15px">Targeted advertising</li></ul></div><div style="font-size:12.8px">Example questions that are relevant include (but are not limited to):</div><div style="font-size:12.8px"><ul><li style="margin-left:15px">How should we strike a balance between model performance and interpretability?</li><li style="margin-left:15px">How can we formalize social concepts in ways that are amenable to machine learning methods? How do these formalizations influence the choice of machine learning method?</li><li style="margin-left:15px">How does uncertainty and noise inherent to real-world <span class="">data</span> sets affect the use of these <span class="">data</span> sets and the use of results obtained from them via machine learning methods?</li><li style="margin-left:15px">How can we incorporate social and ethical considerations into our validation methods and choices? What are the social costs of errors or class imbalance and the distribution of those errors across populations? What are the social implications of prioritizing false positive rates vs. false negative rates?</li><li style="margin-left:15px">When is it appropriate to collect additional <span class="">data</span> about minority or underrepresented populations? How should we address the need <span class="">for</span> balanced <span class="">data</span>sets without imposing a “diversity tax?” How should we weigh the social and financial associated costs and benefits?</li><li style="margin-left:15px">What are the social consequences and tradeoffs involved in feature selection?</li><li style="margin-left:15px">We encourage submissions from organizations that may do not typically write research <span class="">papers</span>. In addition to submissions from universities and corporations, we welcome submissions from government agencies, nonprofit organizations, startups, and foundations.</li></ul></div><span style="font-size:12.8px">These submissions might be:</span><br style="font-size:12.8px"><div><ul style="font-size:12.8px"><li style="margin-left:15px"><span class="">Papers</span> that describe and evaluate new and/or existing methods that balance social and technical factors in decision-making using or surrounding <span class="">big </span><span class="">data</span>.</li><li style="margin-left:15px"><span class="">Papers</span> that describe trade-offs that emerged during the design and implementation of <span class="">big</span> <span class="">data</span> systems in industry, government, or nonprofit settings.</li><li style="margin-left:15px">Position <span class="">papers</span> that highlight sociotechnical challenges that need to be overcome in order to make methods that are suited to responsibly solving large-scale social challenges.</li></ul></div></div></div><div class="" style="margin:2px 0px 0px;font-size:12.8px"><div id=":168" class="" tabindex="0"><img class="" src="https://ssl.gstatic.com/ui/v1/icons/mail/images/cleardot.gif"></div></div><div class="" style="font-size:12.8px"><span class="im"><span style="font-size:12.8px">Deadline <span class="">for</span> manuscript submission: September 15, 2016.  Submit here: </span><a href="http://www.liebertpub.com/manuscript/big" target="_blank" style="font-size:12.8px">http://www.liebertpub.<wbr>com/manuscript/<span class="">big</span></a><br style="font-size:12.8px"><br style="font-size:12.8px"><span style="font-size:12.8px">Please address any questions to: </span><a href="mailto:bd-tradeoffs@lists.datasociety.net" target="_blank" style="font-size:12.8px">bd-tradeoffs@lists.<wbr>datasociety.net</a><span style="font-size:12.8px"> </span><br style="font-size:12.8px"><br style="font-size:12.8px"><span style="font-size:12.8px"><span class="">Big</span></span><span style="font-size:12.8px"> </span><span style="font-size:12.8px"><span class="">Data</span></span><span style="font-size:12.8px"> is a highly innovative, peer-reviewed journal, provides a unique forum <span class="">for</span> world-class research exploring the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of </span><span style="font-size:12.8px"><span class="">data</span></span><span style="font-size:12.8px">, including </span><span style="font-size:12.8px"><span class="">data</span></span><span style="font-size:12.8px"> science, </span><span style="font-size:12.8px"><span class="">big</span></span><span style="font-size:12.8px"> </span><span style="font-size:12.8px">da<wbr>ta</span><span style="font-size:12.8px"> infrastructure and analytics, and pervasive computing.</span><br style="font-size:12.8px"><br style="font-size:12.8px"><span style="font-size:12.8px">Advantages of publishing in </span><span style="font-size:12.8px"><span class="">Big</span></span><span style="font-size:12.8px"> </span><span style="font-size:12.8px"><span class="">Data</span></span><span style="font-size:12.8px"> include:</span><br style="font-size:12.8px"><br style="font-size:12.8px"><div style="font-size:12.8px"><span style="white-space:pre-wrap">      </span>• <span class="">Big</span> <span class="">Data</span> is indexed in Thomson Reuters Emerging Sources Citation Index<br></div><div style="font-size:12.8px"><span style="white-space:pre-wrap">       </span>• Attractive open access options<br></div><div style="font-size:12.8px"><span style="white-space:pre-wrap"> </span>• Fast and user-friendly electronic submission<br></div><div style="font-size:12.8px"><span style="white-space:pre-wrap">   </span>• Rapid, high-quality peer review<br></div><div style="font-size:12.8px"><span style="white-space:pre-wrap">        </span>• Maximum exposure: accessible in 170 countries worldwide<br></div><div style="font-size:12.8px"><br></div><br style="font-size:12.8px"><i style="font-size:12.8px">A web version of this <span class="">call</span> is available at: </i><a href="http://www.datasociety.net/blog/2016/03/10/big-data-cfp-social-technical-trade-offs/" target="_blank" style="font-size:12.8px">http://www.datasociety.<wbr>net/blog/2016/03/10/<span class="">big</span>-<span class="">data</span>-<wbr><span class="">cfp</span>-social-technical-trade-<wbr>offs/</a></span></div></div>