Privacy concerns with using public data for suicide risk prediction algorithms: a public opinion survey of contextual appropriateness

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Michael Zimmer ◽  
Sarah Logan
Keyword(s):  
Public Opinion ◽  
Risk Prediction ◽  
Suicide Risk ◽  
Content Type ◽  
Privacy Concerns ◽  
Prediction Algorithms ◽  
Sociodemographic Data ◽  
Suicide Prediction ◽  
Level Of Concern ◽  
Socioeconomic Data

Purpose Existing algorithms for predicting suicide risk rely solely on data from electronic health records, but such models could be improved through the incorporation of publicly available socioeconomic data – such as financial, legal, life event and sociodemographic data. The purpose of this study is to understand the complex ethical and privacy implications of incorporating sociodemographic data within the health context. This paper presents results from a survey exploring what the general public’s knowledge and concerns are about such publicly available data and the appropriateness of using it in suicide risk prediction algorithms. Design/methodology/approach A survey was developed to measure public opinion about privacy concerns with using socioeconomic data across different contexts. This paper presented respondents with multiple vignettes that described scenarios situated in medical, private business and social media contexts, and asked participants to rate their level of concern over the context and what factor contributed most to their level of concern. Specific to suicide prediction, this paper presented respondents with various data attributes that could potentially be used in the context of a suicide risk algorithm and asked participants to rate how concerned they would be if each attribute was used for this purpose. Findings The authors found considerable concern across the various contexts represented in their vignettes, with greatest concern in vignettes that focused on the use of personal information within the medical context. Specific to the question of incorporating socioeconomic data within suicide risk prediction models, the results of this study show a clear concern from all participants in data attributes related to income, crime and court records, and assets. Data about one’s household were also particularly concerns for the respondents, suggesting that even if one might be comfortable with their own being used for risk modeling, data about other household members is more problematic. Originality/value Previous studies on the privacy concerns that arise when integrating data pertaining to various contexts of people’s lives into algorithmic and related computational models have approached these questions from individual contexts. This study differs in that it captured the variation in privacy concerns across multiple contexts. Also, this study specifically assessed the ethical concerns related to a suicide prediction model and determining people’s awareness of the publicness of select data attributes, as well as which of these data attributes generated the most concern in such a context. To the best of the authors’ knowledge, this is the first study to pursue this question.

How privacy concerns and cultural differences affect public opinion during the COVID-19 pandemic: a case study

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yunfei Xing ◽  
Yuhai Li ◽  
Feng-Kwei Wang
Keyword(s):  
Public Opinion ◽  
Comparative Analysis ◽  
Cultural Differences ◽  
National Level ◽  
Cultural Perspective ◽  
National Policies ◽  
Content Type ◽  
Privacy Concerns ◽  
The Us

PurposeCOVID-19, an infectious disease first identified in China, has resulted in an ongoing pandemic all over the world. Most of the countries have been experiencing a difficult period during the fighting of this pandemic. The purpose of this study is to explore the effect of privacy concerns and cultural differences on public opinion related to the pandemic. The authors conducted a comparative analysis of public opinion in the US and in China as a case study, in order to determine the results.Design/methodology/approachNational policies on important issues faced during the COVID-19 pandemic in the US and in China were examined through a comparative analysis. The authors used text clustering and visualization to mine public opinion on two popular social media platforms, Twitter and Weibo. From the perspectives of concern for privacy and of national culture, this study combines qualitative and quantitative analysis to discover the acceptance level of national policies by the public in the two countries.FindingsThe anti-pandemic policies and measures of the US and China reflect the different characteristics of their respective political systems and national cultures. When considering the culture of the US, it is hard to establish and enforce a rigorous regulation on either mask wearing in public or home quarantine on the national level. The opinions of US people are diverse, regarding national COVID-19 policies, but they are rather unified on privacy issues. On the other hand, Chinese people show a high acceptance of national policies based on their mask-wearing customs and their culture of collectivism.Originality/valuePrior studies have paid insufficient attention to the ways in which user privacy and cultural difference affect public opinion on national policies between the US and China. This case study that compares public opinion on current and topical issues which are closely bound up with public life shows originality, as it innovatively provides a cross-cultural perspective on the research of public opinion dissemination during emergencies by considering the ongoing COVID-19 pandemic.

scholarly journals Do implicit measures improve suicide risk prediction? An 18‐month prospective study using different tasks

2021 ◽  
Author(s):  
Jakob Scheunemann ◽  
Lena Jelinek ◽  
Judith Peth ◽  
Anne Runde ◽  
Sönke Arlt ◽  
...  
Keyword(s):  
Prospective Study ◽  
Risk Prediction ◽  
Suicide Risk ◽  
Implicit Measures

Workplace romance versus sexual harassment: a call to action regarding sexual hubris and sexploitation in the #MeToo era

2020 ◽  
Vol 35 (4) ◽  
pp. 329-347
Author(s):  
Lisa Mainiero
Keyword(s):  
Sexual Harassment ◽  
Power Relations ◽  
Organizational Leaders ◽  
Content Type ◽  
Privacy Concerns ◽  
Call To Action ◽  
Workplace Romance ◽  
Lower Status ◽  
Sexual Harassment Policies ◽  
Dominant Coalition

Purpose The #MeToo movement has brought questions of sexuality and power in the workplace to the forefront. The purpose of this paper is to review the research on hierarchial consensual workplace romances and sexual harassment examining the underlying mechanisms of power relations. It concludes with a call to action for organizational leaders to adopt fair consensual workplace romance policies alongside strong sexual harassment policies. Design/methodology/approach This paper represents a conceptual review of the literature on consensual workplace romance, sexual harassment, passive leadership and power relations. Passive leadership leads to a climate of incivility that in turn suppresses disclosures of sexual harassment (Lee, 2016). Consensual workplace romances across hierarchical power relations carry significant risks and may turn into harassment should the romance turn sour. Findings Two new concepts, sexual hubris and sexploitation, are defined in this paper. Sexual hubris, defined as an opportunistic mindset that allows the powerful to abuse their power to acquire sexual liaisons, and its opposite, sexploitation, defined as a lower-status member using sexuality to gain advantage and favor from an upper-level power target, are dual opportunistic outcomes of an imbalanced power relation. Sexual hubris may increase the likelihood for sexual harassment such that a mindset occurs on the part of the dominant coalition that results in feelings of entitlement. Sexploitation is a micromanipulation tactic designed to create sexual favoritism that excludes others from the power relation. Research limitations/implications Sexual hubris and sexploitation are conceptualized as an opportunistic mechanisms associated with imbalanced power relations to spur future research to tease out complex issues of gender, sexuality and hierarchy in the workplace. Sexual hubris serves to protect the dominant coalition and shapes organizational norms of a climate of oppression and incivility. Conversely, sexploitation is a micromanipulation tactic that allows a lower-status member to receive favoritism from a higher-power target. Four research propositions on sexual hubris and sexploitation are presented for future scholarship. Practical implications Most organizational leaders believe consensual romance in the office cannot be legislated owing to privacy concerns. Passive leadership is discussed as a leadership style that looks the other way and does not intervene, leading to workplace hostility and incivility (Lee, 2016). Inadequate leadership creates a climate of passivity that in turn silences victims. Policies concerning consensual workplace romance should stand alongside sexual harassment policies regardless of privacy concerns. Social implications The #MeToo movement has allowed victims to disclose sexual misconduct and abuse in the workplace. However, the prevalence of sexual harassment claims most often can be traced to a leadership problem. Employers must recognize that sexual hubris and sexploitation arise from imbalances of power, where sex can be traded for advancement, and that often consensual workplace romances end badly, leading to claims of sexual harassment. Consensual romance policies must stand alongside sexual harassment policies. Originality/value Sexual hubris and sexploitation are offered as novel concepts that provide a mechanism for conceptualizing the potential for abuse and manipulation from unbalanced power relations. These are original concepts derived from the arguments within this paper that help make the case for consensual workplace romance policies alongside sexual harassment policies.

The economic costs of protests and policy recommendations for preventing them: the case of Hong Kong

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anson Cheuk-Ho Au
Keyword(s):  
Hong Kong ◽  
Public Opinion ◽  
Political Ideology ◽  
Institutional Policy ◽  
Economic Research ◽  
Policy Recommendations ◽  
Economic Costs ◽  
Business Decision ◽  
Content Type ◽  
Social Transformations

Purpose This paper aims to examine the economic costs of protests at micro-to-firm, market sector and aggregate levels. This paper then develops institutional policy recommendations for allaying these costs. Design/methodology/approach This paper conducts a case study of the anti-extradition bill protests in Hong Kong by examining news articles, online discussions and economic indices from the Hong Kong Census and Statistics Department. This paper further develops policy insights from an analysis of the Hong Kong Basic Law (the city’s mini-constitution) and insights from economic research. Findings This paper discovers that the protests may have caused overall volatility in firms, market sectors and the overall economy, measured in production disruptions, revenue losses and declines in employment. Among Hong Kong’s four major industries, the most severely stunted market sectors were tourism and retail, as well as trading and logistics, whereas financial services and professional and producer services experienced mixed effects. This paper develops two institutional policy recommendations for government and corporate policymaking for reducing volatility and ultimately safeguarding economic growth: the separation of political ideology and economics; the systematic use of public opinion analytics to pre-test the reception of policies. Practical implications Corporate strategists and policymakers would benefit from and advance the economy by better insulating business decision-making from political biases and by investing in public opinion analytics. Originality/value Much of economic theory treats social transformations as externalities. This paper adopts a different approach by foregrounding the role that social transformations play in shaping the economy. To this end, to the best of the author’s knowledge, this paper is among the first to examine the anti-extradition bill protests of Hong Kong, arguably the most significant and widespread protests in the city’s and the region’s history.

G-GERT network model of online public opinion reversal based on kernel and grey degree

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shuli Yan ◽  
Xiangyan Zeng ◽  
Pingping Xiong ◽  
Na Zhang
Keyword(s):  
Public Opinion ◽  
Network Model ◽  
Design Methodology ◽  
Average Frequency ◽  
Decision Makers ◽  
Content Type ◽  
Gert Network ◽  
Frequency Probability ◽  
And Control ◽  
Difficult Issue

PurposeIn recent years, online public opinion reversal incidents have been occurring frequently, which has increased the complexity of the evolution of online public opinion, and they have become a difficult issue for public opinion management and control. It is of great significance to explore the regularity of online public opinion reversal.Design/methodology/approachCombined with the grey characteristics of online public opinion information, a grey graphical evaluation review technique (G-GERT) network model is constructed based on kernel and grey degree, and the frequency, probability and time of online public opinion reversal nodes are calculated using C-marking method and Z-marking method.FindingsThroughout the online public opinion reversal events, there are all repeated outbreak nodes occurring, so the authors regard the repeated occurrence of outbreak nodes as reversal. According to the average frequency, probability and time of repeated outbreak nodes in the G-GERT network model, the authors predict the corresponding key information of reversal. It can simulate the evolution process of public opinion events accurately.Originality/valueThe G-GERT network model based on kernel and grey degree reveals the regulation of public opinion reversal, predicts the frequency, probability and time of reversal nodes, which are the most concerned and difficult issues for decision-makers. The model provides the decision basis and reference for government decision-making departments.

A review of literature on risk prediction tools for hospital readmissions in older adults

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Michelle Louise Gatt ◽  
Maria Cassar ◽  
Sandra C. Buttigieg
Keyword(s):  
Risk Prediction ◽  
Prediction Models ◽  
Hospital Readmissions ◽  
Predictive Ability ◽  
Chronic Obstructive ◽  
Risk Category ◽  
Extensive Literature ◽  
Content Type ◽  
Prediction Tools ◽  
Readmission Risk

Purpose The purpose of this paper is to identify and analyse the readmission risk prediction tools reported in the literature and their benefits when it comes to healthcare organisations and management.Design/methodology/approach Readmission risk prediction is a growing topic of interest with the aim of identifying patients in particular those suffering from chronic diseases such as congestive heart failure, chronic obstructive pulmonary disease and diabetes, who are at risk of readmission. Several models have been developed with different levels of predictive ability. A structured and extensive literature search of several databases was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analysis strategy, and this yielded a total of 48,984 records.Findings Forty-three articles were selected for full-text and extensive review after following the screening process and according to the eligibility criteria. About 34 unique readmission risk prediction models were identified, in which their predictive ability ranged from poor to good (c statistic 0.5–0.86). Readmission rates ranged between 3.1 and 74.1% depending on the risk category. This review shows that readmission risk prediction is a complex process and is still relatively new as a concept and poorly understood. It confirms that readmission prediction models hold significant accuracy at identifying patients at higher risk for such an event within specific context.Research limitations/implications Since most prediction models were developed for specific populations, conditions or hospital settings, the generalisability and transferability of the predictions across wider or other contexts may be difficult to achieve. Therefore, the value of prediction models remains limited to hospital management. Future research is indicated in this regard.Originality/value This review is the first to cover readmission risk prediction tools that have been published in the literature since 2011, thereby providing an assessment of the relevance of this crucial KPI to health organisations and managers.

AI and privacy concerns: a smart meter case study

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jillian Carmody ◽  
Samir Shringarpure ◽  
Gerhard Van de Venter
Keyword(s):  
Private Information ◽  
Personal Information ◽  
Consumer Information ◽  
Data Sets ◽  
Smart Meter ◽  
Smart Meters ◽  
Content Type ◽  
Privacy Concerns ◽  
Privacy Legislation

Purpose The purpose of this paper is to demonstrate privacy concerns arising from the rapidly increasing advancements and use of artificial intelligence (AI) technology and the challenges of existing privacy regimes to ensure the on-going protection of an individual’s sensitive private information. The authors illustrate this through a case study of energy smart meters and suggest a novel combination of four solutions to strengthen privacy protection. Design/methodology/approach The authors illustrate how, through smart meter obtained energy data, home energy providers can use AI to reveal private consumer information such as households’ electrical appliances, their time and frequency of usage, including number and model of appliance. The authors show how this data can further be combined with other data to infer sensitive personal information such as lifestyle and household income due to advances in AI technologies. Findings The authors highlight data protection and privacy concerns which are not immediately obvious to consumers due to the capabilities of advanced AI technology and its ability to extract sensitive personal information when applied to large overlapping granular data sets. Social implications The authors question the adequacy of existing privacy legislation to protect sensitive inferred consumer data from AI-driven technology. To address this, the authors suggest alternative solutions. Originality/value The original value of this paper is that it illustrates new privacy issues brought about by advances in AI, failings in current privacy legislation and implementation and opens the dialog between stakeholders to protect vulnerable consumers.

The Internet of Things and its impact on the library

2016 ◽  
Vol 117 (3/4) ◽  
pp. 289-292 ◽  
Author(s):  
Bruce Massis
Keyword(s):  
Internet Of Things ◽  
Literature Review ◽  
Design Methodology ◽  
Security And Privacy ◽  
The Internet ◽  
Content Type ◽  
Privacy Concerns ◽  
Potential Impact ◽  
The Internet Of Things

Purpose – The purpose of this paper is to consider the Internet of Things (IOT) and its potential impact on libraries. Design/methodology/approach – This paper presents a literature review and a commentary on this topic that have been addressed by professionals, researchers and practitioners. Findings – In communicating the issues when comprehending the scope of the IOT, libraries need not succumb to the sometimes near-hysteria that surrounds the rhetoric regarding security and privacy. But, librarians must actively engage in the conversation and its subsequent actions to respond to patrons who use library networks and devices with calm, logical and transparent answers to those questions concerning what they are doing to ensure that security and privacy vulnerabilities are regularly addressed. Originality/value – The value in concentrating on this topic is to provide background and suggest several approaches to security and privacy concerns regarding the IOT.

scholarly journals Implementing machine learning algorithms for suicide prevention: Designing a Clinical Decision Support tool in partnership with Native American care providers (Preprint)

2020 ◽  
Author(s):  
Emily Haroz ◽  
Fiona Grubin ◽  
Novalene Goklish ◽  
Shardai Pioche ◽  
Mary Cwik ◽  
...  
Keyword(s):  
Native American ◽  
Risk Prediction ◽  
Suicide Risk ◽  
Clinical Care ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Case Managers ◽  
Machine Learning Algorithms ◽  
Support Tool ◽  
Appropriate Care

BACKGROUND Machine learning algorithms for suicide risk prediction have been developed with notable improvements in accuracy. Implementing these algorithms to enhance clinical care and reduce suicide has not been well studied. OBJECTIVE Our study aimed to design a Clinical Decision Support tool (CDS) and appropriate care pathways for a community-based suicide surveillance and case management systems operating on Native American reservations. METHODS Participants included Native American case managers and supervisors (N = 9) who work on suicide surveillance and case management programs on two Native American reservations. We used in-depth interviews to understand how case managers think about and respond to suicide risk. Results from interviews informed a draft CDS tool, which was then reviewed with supervisors and combined with appropriate care pathways. RESULTS Case managers reported acceptance of risk flags based on a predictive algorithm in their surveillance system tools, particularly if the information was available in a timely way and used in conjunction with their clinical judgement. Implementation of risk flags needed to be programmed on a dichotomous basis so the algorithm could produce output indicating high vs. low risk. To dichotomize the continuous predicted probabilities, we developed a cutoff point that favored specificity, with the understanding that case managers’ clinical judgment would help increase sensitivity. CONCLUSIONS Suicide risk prediction algorithms show promise, but implementation to guide clinical care has remained relatively elusive. Our study demonstrates the utility of working with partners to develop and guide operationalization of risk prediction algorithms to enhance clinical care in a community setting.

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