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Big Data

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https://www.readbyqxmd.com/read/28933947/on-the-safety-of-machine-learning-cyber-physical-systems-decision-sciences-and-data-products
#1
Kush R Varshney, Homa Alemzadeh
Machine learning algorithms increasingly influence our decisions and interact with us in all parts of our daily lives. Therefore, just as we consider the safety of power plants, highways, and a variety of other engineered socio-technical systems, we must also take into account the safety of systems involving machine learning. Heretofore, the definition of safety has not been formalized in a machine learning context. In this article, we do so by defining machine learning safety in terms of risk, epistemic uncertainty, and the harm incurred by unwanted outcomes...
September 2017: Big Data
https://www.readbyqxmd.com/read/28933946/detecting-spatial-patterns-of-disease-in-large-collections-of-electronic-medical-records-using-neighbor-based-bootstrapping
#2
Maria T Patterson, Robert L Grossman
We introduce a method called neighbor-based bootstrapping (NB2) that can be used to quantify the geospatial variation of a variable. We applied this method to an analysis of the incidence rates of disease from electronic medical record data (International Classification of Diseases, Ninth Revision codes) for ∼100 million individuals in the United States over a period of 8 years. We considered the incidence rate of disease in each county and its geospatially contiguous neighbors and rank ordered diseases in terms of their degree of geospatial variation as quantified by the NB2 method...
September 2017: Big Data
https://www.readbyqxmd.com/read/28933945/a-message-from-the-editor-in-chief-of-big-data
#3
(no author information available yet)
No abstract text is available yet for this article.
September 2017: Big Data
https://www.readbyqxmd.com/read/28933944/discrn-a-distributed-storytelling-framework-for-intelligence-analysis
#4
Manu Shukla, Raimundo Dos Santos, Feng Chen, Chang-Tien Lu
Storytelling connects entities (people, organizations) using their observed relationships to establish meaningful storylines. This can be extended to spatiotemporal storytelling that incorporates locations, time, and graph computations to enhance coherence and meaning. But when performed sequentially these computations become a bottleneck because the massive number of entities make space and time complexity untenable. This article presents DISCRN, or distributed spatiotemporal ConceptSearch-based storytelling, a distributed framework for performing spatiotemporal storytelling...
September 2017: Big Data
https://www.readbyqxmd.com/read/28933943/what-is-the-role-of-artificial-intelligence-in-sports
#5
Vasant Dhar
No abstract text is available yet for this article.
September 2017: Big Data
https://www.readbyqxmd.com/read/28933942/enhancing-transparency-and-control-when-drawing-data-driven-inferences-about-individuals
#6
Daizhuo Chen, Samuel P Fraiberger, Robert Moakler, Foster Provost
Recent studies show the remarkable power of fine-grained information disclosed by users on social network sites to infer users' personal characteristics via predictive modeling. Similar fine-grained data are being used successfully in other commercial applications. In response, attention is turning increasingly to the transparency that organizations provide to users as to what inferences are drawn and why, as well as to what sort of control users can be given over inferences that are drawn about them. In this article, we focus on inferences about personal characteristics based on information disclosed by users' online actions...
September 2017: Big Data
https://www.readbyqxmd.com/read/28933941/strength-in-numbers-using-big-data-to-simplify-sentiment-classification
#7
Apostolos Filippas, Theodoros Lappas
Sentiment classification, the task of assigning a positive or negative label to a text segment, is a key component of mainstream applications such as reputation monitoring, sentiment summarization, and item recommendation. Even though the performance of sentiment classification methods has steadily improved over time, their ever-increasing complexity renders them comprehensible by only a shrinking minority of expert practitioners. For all others, such highly complex methods are black-box predictors that are hard to tune and even harder to justify to decision makers...
September 2017: Big Data
https://www.readbyqxmd.com/read/28829624/predictive-analytics-for-city-agencies-lessons-from-children-s-services
#8
Ravi Shroff
Many municipal agencies maintain detailed and comprehensive electronic records of their interactions with citizens. These data, in combination with machine learning and statistical techniques, offer the promise of better decision making, and more efficient and equitable service delivery. However, a data scientist employed by an agency to implement these techniques faces numerous and varied choices that cumulatively can have significant real-world consequences. The data scientist, who may be the only person at an agency equipped to understand the technical complexity of a predictive algorithm, therefore, bears a good deal of responsibility in making judgments...
September 2017: Big Data
https://www.readbyqxmd.com/read/28816500/research-challenges-in-financial-data-modeling-and-analysis
#9
Lewis Alexander, Sanjiv R Das, Zachary Ives, H V Jagadish, Claire Monteleoni
Significant research challenges must be addressed in the cleaning, transformation, integration, modeling, and analytics of Big Data sources for finance. This article surveys the progress made so far in this direction and obstacles yet to be overcome. These are issues that are of interest to data-driven financial institutions in both corporate finance and consumer finance. These challenges are also of interest to the legal profession as well as to regulators. The discussion is relevant to technology firms that support the growing field of FinTech...
September 2017: Big Data
https://www.readbyqxmd.com/read/28632445/critique-and-contribute-a-practice-based-framework-for-improving-critical-data-studies-and-data-science
#10
Gina Neff, Anissa Tanweer, Brittany Fiore-Gartland, Laura Osburn
What would data science look like if its key critics were engaged to help improve it, and how might critiques of data science improve with an approach that considers the day-to-day practices of data science? This article argues for scholars to bridge the conversations that seek to critique data science and those that seek to advance data science practice to identify and create the social and organizational arrangements necessary for a more ethical data science. We summarize four critiques that are commonly made in critical data studies: data are inherently interpretive, data are inextricable from context, data are mediated through the sociomaterial arrangements that produce them, and data serve as a medium for the negotiation and communication of values...
June 2017: Big Data
https://www.readbyqxmd.com/read/28632444/the-structural-consequences-of-big-data-driven-education
#11
Elana Zeide
Educators and commenters who evaluate big data-driven learning environments focus on specific questions: whether automated education platforms improve learning outcomes, invade student privacy, and promote equality. This article puts aside separate unresolved-and perhaps unresolvable-issues regarding the concrete effects of specific technologies. It instead examines how big data-driven tools alter the structure of schools' pedagogical decision-making, and, in doing so, change fundamental aspects of America's education enterprise...
June 2017: Big Data
https://www.readbyqxmd.com/read/28632443/diversity-in-big-data-a-review
#12
Marina Drosou, H V Jagadish, Evaggelia Pitoura, Julia Stoyanovich
Big data technology offers unprecedented opportunities to society as a whole and also to its individual members. At the same time, this technology poses significant risks to those it overlooks. In this article, we give an overview of recent technical work on diversity, particularly in selection tasks, discuss connections between diversity and fairness, and identify promising directions for future work that will position diversity as an important component of a data-responsible society. We argue that diversity should come to the forefront of our discourse, for reasons that are both ethical-to mitigate the risks of exclusion-and utilitarian, to enable more powerful, accurate, and engaging data analysis and use...
June 2017: Big Data
https://www.readbyqxmd.com/read/28632442/social-and-technical-trade-offs-in-data-science
#13
Solon Barocas, Danah Boyd, Sorelle Friedler, Hanna Wallach
No abstract text is available yet for this article.
June 2017: Big Data
https://www.readbyqxmd.com/read/28632441/research-dilemmas-with-behavioral-big-data
#14
Galit Shmueli
Behavioral big data (BBD) refers to very large and rich multidimensional data sets on human and social behaviors, actions, and interactions, which have become available to companies, governments, and researchers. A growing number of researchers in social science and management fields acquire and analyze BBD for the purpose of extracting knowledge and scientific discoveries. However, the relationships between the researcher, data, subjects, and research questions differ in the BBD context compared to traditional behavioral data...
June 2017: Big Data
https://www.readbyqxmd.com/read/28632440/call-for-papers-special-issue-on-profit-driven-analytics
#15
Bart Baesens, Wouter Verbeke, Cristián Bravo
No abstract text is available yet for this article.
June 2017: Big Data
https://www.readbyqxmd.com/read/28632439/call-for-papers-special-issue-on-computational-propaganda-and-political-big-data
#16
Philip N Howard, Gillian Bolsover
No abstract text is available yet for this article.
June 2017: Big Data
https://www.readbyqxmd.com/read/28632438/fair-prediction-with-disparate-impact-a-study-of-bias-in-recidivism-prediction-instruments
#17
Alexandra Chouldechova
Recidivism prediction instruments (RPIs) provide decision-makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time. Although such instruments are gaining increasing popularity across the country, their use is attracting tremendous controversy. Much of the controversy concerns potential discriminatory bias in the risk assessments that are produced. This article discusses several fairness criteria that have recently been applied to assess the fairness of RPIs...
June 2017: Big Data
https://www.readbyqxmd.com/read/28632437/conscientious-classification-a-data-scientist-s-guide-to-discrimination-aware-classification
#18
Brian d'Alessandro, Cathy O'Neil, Tom LaGatta
Recent research has helped to cultivate growing awareness that machine-learning systems fueled by big data can create or exacerbate troubling disparities in society. Much of this research comes from outside of the practicing data science community, leaving its members with little concrete guidance to proactively address these concerns. This article introduces issues of discrimination to the data science community on its own terms. In it, we tour the familiar data-mining process while providing a taxonomy of common practices that have the potential to produce unintended discrimination...
June 2017: Big Data
https://www.readbyqxmd.com/read/28586238/toward-accountable-discrimination-aware-data-mining-the-importance-of-keeping-the-human-in-the-loop-and-under-the-looking-glass
#19
Bettina Berendt, Sören Preibusch
"Big Data" and data-mined inferences are affecting more and more of our lives, and concerns about their possible discriminatory effects are growing. Methods for discrimination-aware data mining and fairness-aware data mining aim at keeping decision processes supported by information technology free from unjust grounds. However, these formal approaches alone are not sufficient to solve the problem. In the present article, we describe reasons why discrimination with data can and typically does arise through the combined effects of human and machine-based reasoning, and argue that this requires a deeper understanding of the human side of decision-making with data mining...
June 2017: Big Data
https://www.readbyqxmd.com/read/28328254/a-perspective-on-natural-language-understanding-capability-an-interview-with-sam-bowman
#20
Vasant Dhar, Sam Bowman
No abstract text is available yet for this article.
March 2017: Big Data
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