scholarly journals Risk Surface: A Visualization of Data Sharing Risk for Enterprise Users

2019 ◽  
Vol 63 (1) ◽  
pp. 297-301
Author(s):  
Mark F. St. John ◽  
Woodrow Gustafson ◽  
April Martin ◽  
Ronald A. Moore ◽  
Christopher A. Korkos
Keyword(s):  
Data Sharing ◽  
Data Sets ◽  
Surface Design ◽  
Trade Off ◽  
Shared Data ◽  
Risks And Benefits ◽  
Trade Offs ◽  
Different Types ◽  
Risk Visualization ◽  
Visualization Of Data

Enterprises share a wide variety of data with different partners. Tracking the risks and benefits of this data sharing is important for avoiding unwarranted risks of data exploitation. Data sharing risk can be characterized as a combination of trust in data sharing partners to not exploit shared data and the sensitivity, or potential for harm, of the data. Data sharing benefits can be characterized as the value likely to accrue to the enterprise from sharing the data by making the enterprise’s objectives more likely to succeed. We developed a risk visualization concept called a risk surface to support users monitoring for high risks and poor risk-benefit trade-offs. The risk surface design was evaluated in a series of two focus groups conducted with human factors professionals. Across the two studies, the design was improved and ultimately rated as highly useful. A risk surface needs to 1) convey which data, as joined data sets, are shared with which partners, 2) convey the degree of risk due to sharing that data, 3) convey the benefits of the data sharing and the trade-off between risk and benefits, and 4) be easy to scan at scale, since enterprises are likely to share many different types of data with many different partners.

Challenges in the Empirical Study of Global Governance Legitimacy

2018 ◽  
Author(s):  
Steven Bernstein
Keyword(s):  
Empirical Study ◽  
Global Governance ◽  
Research Agenda ◽  
Cultural Factors ◽  
Theory Building ◽  
Trade Off ◽  
Trade Offs ◽  
Different Types ◽  
Political Communities

This commentary discusses three challenges for the promising and ambitious research agenda outlined in the volume. First, it interrogates the volume’s attempts to differentiate political communities of legitimation, which may vary widely in composition, power, and relevance across institutions and geographies, with important implications not only for who matters, but also for what gets legitimated, and with what consequences. Second, it examines avenues to overcome possible trade-offs from gains in empirical tractability achieved through the volume’s focus on actor beliefs and strategies. One such trade-off is less attention to evolving norms and cultural factors that may underpin actors’ expectations about what legitimacy requires. Third, it addresses the challenge of theory building that can link legitimacy sources, (de)legitimation practices, audiences, and consequences of legitimacy across different types of institutions.

scholarly journals Getting Started Creating Data Dictionaries: How to Create a Shareable Data Set

2021 ◽  
Vol 4 (1) ◽  
pp. 251524592092800
Author(s):  
Erin M. Buchanan ◽  
Sarah E. Crain ◽  
Ari L. Cunningham ◽  
Hannah R. Johnson ◽  
Hannah Stash ◽  
...  
Keyword(s):  
Data Collection ◽  
Data Sharing ◽  
Search Engine ◽  
Web Applications ◽  
Data Sets ◽  
Data Dictionary ◽  
Data Set ◽  
Entire Process ◽  
Shared Data ◽  
Source Data

As researchers embrace open and transparent data sharing, they will need to provide information about their data that effectively helps others understand their data sets’ contents. Without proper documentation, data stored in online repositories such as OSF will often be rendered unfindable and unreadable by other researchers and indexing search engines. Data dictionaries and codebooks provide a wealth of information about variables, data collection, and other important facets of a data set. This information, called metadata, provides key insights into how the data might be further used in research and facilitates search-engine indexing to reach a broader audience of interested parties. This Tutorial first explains terminology and standards relevant to data dictionaries and codebooks. Accompanying information on OSF presents a guided workflow of the entire process from source data (e.g., survey answers on Qualtrics) to an openly shared data set accompanied by a data dictionary or codebook that follows an agreed-upon standard. Finally, we discuss freely available Web applications to assist this process of ensuring that psychology data are findable, accessible, interoperable, and reusable.

scholarly journals Origin of perseveration in the trade-off between reward and complexity

2020 ◽  
Author(s):  
Samuel J. Gershman
Keyword(s):  
Parameter Estimation ◽  
Model Comparison ◽  
Functional Form ◽  
Data Sets ◽  
Trade Off ◽  
Information Theoretic ◽  
Trade Offs ◽  
Action Policy

AbstractWhen humans and other animals make repeated choices, they tend to repeat previously chosen actions independently of their reward history. This paper locates the origin of perseveration in a trade-off between two computational goals: maximizing rewards and minimizing the complexity of the action policy. We develop an information-theoretic formalization of policy complexity and show how optimizing the trade-off leads to perseveration. Analysis of two data sets reveals that people attain close to optimal trade-offs. Parameter estimation and model comparison supports the claim that perseveration quantitatively agrees with the theoretically predicted functional form.

scholarly journals Privacy-preserving data sharing infrastructures for medical research: systematization and comparison

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Felix Nikolaus Wirth ◽  
Thierry Meurers ◽  
Marco Johns ◽  
Fabian Prasser
Keyword(s):  
Medical Research ◽  
Data Sharing ◽  
Privacy Protection ◽  
Data Privacy ◽  
Privacy Preserving ◽  
Distributed Data ◽  
Research Gaps ◽  
Individual Level ◽  
Shared Data ◽  
Trade Offs

Abstract Background Data sharing is considered a crucial part of modern medical research. Unfortunately, despite its advantages, it often faces obstacles, especially data privacy challenges. As a result, various approaches and infrastructures have been developed that aim to ensure that patients and research participants remain anonymous when data is shared. However, privacy protection typically comes at a cost, e.g. restrictions regarding the types of analyses that can be performed on shared data. What is lacking is a systematization making the trade-offs taken by different approaches transparent. The aim of the work described in this paper was to develop a systematization for the degree of privacy protection provided and the trade-offs taken by different data sharing methods. Based on this contribution, we categorized popular data sharing approaches and identified research gaps by analyzing combinations of promising properties and features that are not yet supported by existing approaches. Methods The systematization consists of different axes. Three axes relate to privacy protection aspects and were adopted from the popular Five Safes Framework: (1) safe data, addressing privacy at the input level, (2) safe settings, addressing privacy during shared processing, and (3) safe outputs, addressing privacy protection of analysis results. Three additional axes address the usefulness of approaches: (4) support for de-duplication, to enable the reconciliation of data belonging to the same individuals, (5) flexibility, to be able to adapt to different data analysis requirements, and (6) scalability, to maintain performance with increasing complexity of shared data or common analysis processes. Results Using the systematization, we identified three different categories of approaches: distributed data analyses, which exchange anonymous aggregated data, secure multi-party computation protocols, which exchange encrypted data, and data enclaves, which store pooled individual-level data in secure environments for access for analysis purposes. We identified important research gaps, including a lack of approaches enabling the de-duplication of horizontally distributed data or providing a high degree of flexibility. Conclusions There are fundamental differences between different data sharing approaches and several gaps in their functionality that may be interesting to investigate in future work. Our systematization can make the properties of privacy-preserving data sharing infrastructures more transparent and support decision makers and regulatory authorities with a better understanding of the trade-offs taken.

Dual Goals for Speed and Accuracy on the Same Performance Task

2012 ◽  
Vol 11 (3) ◽  
pp. 118-126 ◽  
Author(s):  
Olive Emil Wetter ◽  
Jürgen Wegge ◽  
Klaus Jonas ◽  
Klaus-Helmut Schmidt
Keyword(s):  
Memory Scanning ◽  
Performance Task ◽  
Performance Tasks ◽  
Trade Off ◽  
Test Experiment ◽  
Trade Offs ◽  
New Finding ◽  
Sternberg Paradigm ◽  
Speed Accuracy ◽  
Speed And Accuracy

In most work contexts, several performance goals coexist, and conflicts between them and trade-offs can occur. Our paper is the first to contrast a dual goal for speed and accuracy with a single goal for speed on the same task. The Sternberg paradigm (Experiment 1, n = 57) and the d2 test (Experiment 2, n = 19) were used as performance tasks. Speed measures and errors revealed in both experiments that dual as well as single goals increase performance by enhancing memory scanning. However, the single speed goal triggered a speed-accuracy trade-off, favoring speed over accuracy, whereas this was not the case with the dual goal. In difficult trials, dual goals slowed down scanning processes again so that errors could be prevented. This new finding is particularly relevant for security domains, where both aspects have to be managed simultaneously.

The Effects of Decision Time on Perceptions of Decisions and Decision Makers in (Moral) Trade-Off Scenarios

2019 ◽  
Author(s):  
Anna Katharina Spälti ◽  
Mark John Brandt ◽  
Marcel Zeelenberg
Keyword(s):  
Decision Process ◽  
Decision Time ◽  
Decision Makers ◽  
Process Information ◽  
Total N ◽  
Trade Off ◽  
Financial Gain ◽  
Trade Offs ◽  
Sacred Values ◽  
Moral Context

People often have to make trade-offs. We study three types of trade-offs: 1) "secular trade-offs" where no moral or sacred values are at stake, 2) "taboo trade-offs" where sacred values are pitted against financial gain, and 3) "tragic trade-offs" where sacred values are pitted against other sacred values. Previous research (Critcher et al., 2011; Tetlock et al., 2000) demonstrated that tragic and taboo trade-offs are not only evaluated by their outcomes, but are also evaluated based on the time it took to make the choice. We investigate two outstanding questions: 1) whether the effect of decision time differs for evaluations of decisions compared to decision makers and 2) whether moral contexts are unique in their ability to influence character evaluations through decision process information. In two experiments (total N = 1434) we find that decision time affects character evaluations, but not evaluations of the decision itself. There were no significant differences between tragic trade-offs and secular trade-offs, suggesting that the decisions structure may be more important in evaluations than moral context. Additionally, the magnitude of the effect of decision time shows us that decision time, may be of less practical use than expected. We thus urge, to take a closer examination of the processes underlying decision time and its perception.

Predicting Undesired Treatment Outcome in Mental Healthcare: Machine Learning Study (Preprint)

2019 ◽  
Author(s):  
Kasper Van Mens ◽  
Joran Lokkerbol ◽  
Richard Janssen ◽  
Robert de Lange ◽  
Bea Tiemens
Keyword(s):  
Machine Learning ◽  
Treatment Outcome ◽  
Mental Health Treatment ◽  
Mental Healthcare ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Trade Off ◽  
Trade Offs ◽  
Outcome Monitoring ◽  
Extreme Gradient Boosting

BACKGROUND It remains a challenge to predict which treatment will work for which patient in mental healthcare. OBJECTIVE In this study we compare machine algorithms to predict during treatment which patients will not benefit from brief mental health treatment and present trade-offs that must be considered before an algorithm can be used in clinical practice. METHODS Using an anonymized dataset containing routine outcome monitoring data from a mental healthcare organization in the Netherlands (n = 2,655), we applied three machine learning algorithms to predict treatment outcome. The algorithms were internally validated with cross-validation on a training sample (n = 1,860) and externally validated on an unseen test sample (n = 795). RESULTS The performance of the three algorithms did not significantly differ on the test set. With a default classification cut-off at 0.5 predicted probability, the extreme gradient boosting algorithm showed the highest positive predictive value (ppv) of 0.71(0.61 – 0.77) with a sensitivity of 0.35 (0.29 – 0.41) and area under the curve of 0.78. A trade-off can be made between ppv and sensitivity by choosing different cut-off probabilities. With a cut-off at 0.63, the ppv increased to 0.87 and the sensitivity dropped to 0.17. With a cut-off of at 0.38, the ppv decreased to 0.61 and the sensitivity increased to 0.57. CONCLUSIONS Machine learning can be used to predict treatment outcomes based on routine monitoring data.This allows practitioners to choose their own trade-off between being selective and more certain versus inclusive and less certain.

scholarly journals Trade-off Predictivity and Explainability for Machine-Learning Powered Predictive Toxicology: An in-Depth Investigation with Tox21 Data Sets

2021 ◽  
Vol 34 (2) ◽  
pp. 541-549 ◽  
Author(s):  
Leihong Wu ◽  
Ruili Huang ◽  
Igor V. Tetko ◽  
Zhonghua Xia ◽  
Joshua Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Sets ◽  
Predictive Toxicology ◽  
Trade Off

scholarly journals Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem

2021 ◽  
pp. 089443932110122
Author(s):  
Dennis Assenmacher ◽  
Derek Weber ◽  
Mike Preuss ◽  
André Calero Valdez ◽  
Alison Bradshaw ◽  
...  
Keyword(s):  
Social Media ◽  
Data Sharing ◽  
Algorithm Design ◽  
Computational Social Science ◽  
Evaluation Framework ◽  
Social Media Analytics ◽  
Data Sets ◽  
The Public ◽  
Research Areas ◽  
Media Data

Computational social science uses computational and statistical methods in order to evaluate social interaction. The public availability of data sets is thus a necessary precondition for reliable and replicable research. These data allow researchers to benchmark the computational methods they develop, test the generalizability of their findings, and build confidence in their results. When social media data are concerned, data sharing is often restricted for legal or privacy reasons, which makes the comparison of methods and the replicability of research results infeasible. Social media analytics research, consequently, faces an integrity crisis. How is it possible to create trust in computational or statistical analyses, when they cannot be validated by third parties? In this work, we explore this well-known, yet little discussed, problem for social media analytics. We investigate how this problem can be solved by looking at related computational research areas. Moreover, we propose and implement a prototype to address the problem in the form of a new evaluation framework that enables the comparison of algorithms without the need to exchange data directly, while maintaining flexibility for the algorithm design.

scholarly journals Toward Inclusive Landscape Governance in Contested Landscapes: Exploring the Contribution of Participatory Tools in the Upper Suriname River Basin

2021 ◽  
Author(s):  
Lisa Best ◽  
Kimberley Fung-Loy ◽  
Nafiesa Ilahibaks ◽  
Sara O. I. Ramirez-Gomez ◽  
Erika N. Speelman
Keyword(s):  
Land Use ◽  
River Basin ◽  
Scenario Planning ◽  
Community Members ◽  
Trade Off ◽  
3 Dimensional ◽  
Landscape Governance ◽  
Information Tools ◽  
Tribal Communities ◽  
Trade Offs

AbstractNowadays, tropical forest landscapes are commonly characterized by a multitude of interacting institutions and actors with competing land-use interests. In these settings, indigenous and tribal communities are often marginalized in landscape-level decision making. Inclusive landscape governance inherently integrates diverse knowledge systems, including those of indigenous and tribal communities. Increasingly, geo-information tools are recognized as appropriate tools to integrate diverse interests and legitimize the voices, values, and knowledge of indigenous and tribal communities in landscape governance. In this paper, we present the contribution of the integrated application of three participatory geo-information tools to inclusive landscape governance in the Upper Suriname River Basin in Suriname: (i) Participatory 3-Dimensional Modelling, (ii) the Trade-off! game, and (iii) participatory scenario planning. The participatory 3-dimensional modelling enabled easy participation of community members, documentation of traditional, tacit knowledge and social learning. The Trade-off! game stimulated capacity building and understanding of land-use trade-offs. The participatory scenario planning exercise helped landscape actors to reflect on their own and others’ desired futures while building consensus. Our results emphasize the importance of systematically considering tool attributes and key factors, such as facilitation, for participatory geo-information tools to be optimally used and fit with local contexts. The results also show how combining the tools helped to build momentum and led to diverse yet complementary insights, thereby demonstrating the benefits of integrating multiple tools to address inclusive landscape governance issues.

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