Data science ethical considerations: a systematic literature review and proposed project framework

2019 ◽  
Vol 21 (3) ◽  
pp. 197-208 ◽  
Author(s):  
Jeffrey S. Saltz ◽  
Neil Dewar
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Data Science ◽  
Ethical Considerations

scholarly journals The evidence of impact and ethical considerations of Multimodal Learning Analytics: A Systematic Literature Review

2021 ◽  
Author(s):  
Haifa Alwahaby ◽  
Mutlu Cukurova ◽  
Zacharoula Papamitsiou ◽  
Michail Giannakos
Keyword(s):  
Literature Review ◽  
Learning Outcomes ◽  
Systematic Literature Review ◽  
Real World ◽  
Learning Analytics ◽  
Multimodal Learning ◽  
Ethical Considerations ◽  
Multimodal Data ◽  
Multimodal Learning Analytics ◽  
Evidence Of Impact

There is a growing interest in the research and use of multimodal data in learning analytics. This paper presents a systematic literature review of multimodal learning analytics (MMLA) research to assess i) the available evidence of impact on learning outcomes in real-world contexts and ii) explore the extent to which ethical considerations are addressed. A few recent literature reviews argue for the promising value of multimodal data in learning analytics research. However, our understanding of the challenges associated with MMLA research from real-world teaching and learning environments is limited. To address this gap, this paper provides an overview of the evidence of impact and ethical considerations stemming from an analysis of the relevant MMLA research published in the last decade. The search of the literature resulted in 663 papers, of which 100 were included in the final synthesis. The results show that the evidence of real-world impact on learning outcomes is weak, and ethical aspects of MMLA work are rarely addressed. We discuss our results through the lenses of two theoretical frameworks for evidence of impact types and ethical dimensions of MMLA. We conclude that for MMLA to stay relevant and become part of mainstream education, future research should directly address the gaps identified in this review.

scholarly journals Big data and analytics in hospitality and tourism: a systematic literature review

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marcello Mariani ◽  
Rodolfo Baggio
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Data Science ◽  
The Body ◽  
Future Research ◽  
Content Type ◽  
Tourism Management ◽  
Scientific Outputs ◽  
Hospitality And Tourism

Purpose The purpose of this work is to survey the body of research revolving around big data (BD) and analytics in hospitality and tourism, by detecting macro topical areas, research streams and gaps and to develop an agenda for future research. Design/methodology/approach This research is based on a systematic literature review of academic papers indexed in the Scopus and Web of Science databases published up to 31 December 2020. The outputs were analyzed using bibliometric techniques, network analysis and topic modeling. Findings The number of scientific outputs in research with hospitality and tourism settings has been expanding over the period 2015–2020, with a substantial stability of the areas examined. The vast majority are published in academic journals where the main reference area is neither hospitality nor tourism. The body of research is rather fragmented and studies on relevant aspects, such as BD analytics capabilities, are virtually missing. Most of the outputs are empirical. Moreover, many of the articles collected relatively small quantities of records and, regardless of the time period considered, only a handful of articles mix a number of different techniques. Originality/value This work sheds new light on the emergence of a body of research at the intersection of hospitality and tourism management and data science. It enriches and complements extant literature reviews on BD and analytics, combining these two interconnected topics.

scholarly journals A Systematic Literature Review (SLR) On The Beginning of Resume Parsing in HR Recruitment Process & SMART Advancements in Chronological Order

2021 ◽  
Author(s):  
Aakankshu Rawat ◽  
Siddharth Malik ◽  
Seema Rawat ◽  
Deepak Kumar ◽  
Praveen Kumar
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Fast Track ◽  
Extensive Study ◽  
Ethical Considerations ◽  
Hiring Process ◽  
Main Emphasis ◽  
The World ◽  
Talent Acquisition ◽  
Shed Light

Abstract Talent acquisition, also known as recruitment, is definitely amongst one of the most difficult decisions that an organization has to take. The workforce is the most crucial pillar of any organization and surely a deciding factor for its fate. Each organization/company/firm has a dedicated department in place for completely managing an employee’s life cycle starting from recruitment till termination, known as the Human Resource (HR) department. National Cash Register Co. was the first company ever to include an HR department in 1900 for resolving conflicts among existing employees. Since then, the world of recruitment has grown rapidly with a lot of advancements to fast-track the hiring process. Realising the importance of hiring fitting and apt employees, HR units around the world have undergone substantial changes from traditional recruitment methodologies to network recruitment and finally to smart hybrid recruitment strategies for efficient hiring with a significantly less human workload. The purpose of this Systematic Literature Review (SLR) article is to shed light on all the advancements in the hiring process from a technical perspective. The article follows the defined format and all appropriate guidelines of an SLR and would provide an extensive study of various Machine Learning (ML) and Deep Learning (DL) approaches to facilitate hiring. The main emphasis has been given to “Resume/CV Parsing” as an enhancer of fast-track hiring. The SLR would be instrumental in providing various fast-track approaches to go with for resume parsing, the scope of improvement and also focus on various challenges/ethical considerations to keep in mind while automating the hiring process.

scholarly journals Applications of data science to game learning analytics data: A systematic literature review

2019 ◽  
Vol 141 ◽  
pp. 103612 ◽  
Author(s):  
Cristina Alonso-Fernández ◽  
Antonio Calvo-Morata ◽  
Manuel Freire ◽  
Iván Martínez-Ortiz ◽  
Baltasar Fernández-Manjón
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Learning Analytics ◽  
Data Science

scholarly journals Gender identity assessment with trans individuals – findings of a systematic literature review of assessment instruments and ethical considerations

2020 ◽  
Vol 6 (3) ◽  
pp. 203-216
Author(s):  
Sören Henrich
Keyword(s):  
Literature Review ◽  
Gender Identity ◽  
Systematic Literature Review ◽  
Minnesota Multiphasic Personality Inventory ◽  
Sex Role ◽  
Future Research ◽  
Assessment Instruments ◽  
Ethical Considerations ◽  
Content Type ◽  
Assessment Approach

Purpose In several Western legislations, trans individuals must frequently undergo some form of gender identity assessment, for example, to receive legal recognition of their gender or to access therapeutic interventions. Thus, a standardised and empirically supported assessment approach becomes necessary. The purpose of this paper is to critically reflect on the current international guidelines for assessments by the World Professional Association for Transgender Health, which will be compared to standards in secure forensic settings, illustrated by British prison policies. Design/methodology/approach Findings of a systematic literature review following preferred reporting items for systematic reviews and meta-analysis standards are presented, summarising the current state of research pertaining to gender identity assessment instruments. Studies were included, when they presented empirical details pertaining to assessment approaches and passed the quality appraisal, but were excluded when they did not use a trans sample or presented clinical assessments not linked to gender identity. Findings A total of 21 included English articles, which mostly have been published in the USA in the past 20 years, propose ten different assessment approaches. Most of the studies support the use of the Minnesota Multiphasic Personality Inventory-2, the Bem Sex-Role Inventory, Body Image Scale for Transsexuals and the Gender Identity/Gender Dysphoria Questionnaire for Adolescents and Adults. The instruments are briefly summarised. Practical implications It becomes apparent that this field is severely understudied and that there is no consensus regarding the best assessment approach. Hence, any recommendations are only preliminary and are contextualised with further ethical considerations and suggestions for future research. Originality/value To the best of the author’s knowledge, this is the first systematic literature review pertaining to the (semi-)structured assessment of gender identity.

scholarly journals Is it alright to use artificial intelligence in digital health? A systematic literature review on ethical considerations

2021 ◽  
Vol 27 (4) ◽  
pp. 146045822110523
Author(s):  
Nicholas RJ Möllmann ◽  
Milad Mirbabaie ◽  
Stefan Stieglitz
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Digital Health ◽  
Status Quo ◽  
Ethical Principles ◽  
Ethical Considerations ◽  
Autonomous Decision ◽  
The Status

The application of artificial intelligence (AI) not only yields in advantages for healthcare but raises several ethical questions. Extant research on ethical considerations of AI in digital health is quite sparse and a holistic overview is lacking. A systematic literature review searching across 853 peer-reviewed journals and conferences yielded in 50 relevant articles categorized in five major ethical principles: beneficence, non-maleficence, autonomy, justice, and explicability. The ethical landscape of AI in digital health is portrayed including a snapshot guiding future development. The status quo highlights potential areas with little empirical but required research. Less explored areas with remaining ethical questions are validated and guide scholars’ efforts by outlining an overview of addressed ethical principles and intensity of studies including correlations. Practitioners understand novel questions AI raises eventually leading to properly regulated implementations and further comprehend that society is on its way from supporting technologies to autonomous decision-making systems.

scholarly journals What can machines learn about heart failure? A systematic literature review

2021 ◽  
Author(s):  
A. Jasinska-Piadlo ◽  
R. Bond ◽  
P. Biglarbeigi ◽  
R. Brisk ◽  
P. Campbell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Clinical Practice ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Data Science ◽  
Prediction Models ◽  
Critical Evaluation ◽  
Support Vector ◽  
Clustering Methods

AbstractThis paper presents a systematic literature review with respect to application of data science and machine learning (ML) to heart failure (HF) datasets with the intention of generating both a synthesis of relevant findings and a critical evaluation of approaches, applicability and accuracy in order to inform future work within this field. This paper has a particular intention to consider ways in which the low uptake of ML techniques within clinical practice could be resolved. Literature searches were performed on Scopus (2014-2021), ProQuest and Ovid MEDLINE databases (2014-2021). Search terms included ‘heart failure’ or ‘cardiomyopathy’ and ‘machine learning’, ‘data analytics’, ‘data mining’ or ‘data science’. 81 out of 1688 articles were included in the review. The majority of studies were retrospective cohort studies. The median size of the patient cohort across all studies was 1944 (min 46, max 93260). The largest patient samples were used in readmission prediction models with the median sample size of 5676 (min. 380, max. 93260). Machine learning methods focused on common HF problems: detection of HF from available dataset, prediction of hospital readmission following index hospitalization, mortality prediction, classification and clustering of HF cohorts into subgroups with distinctive features and response to HF treatment. The most common ML methods used were logistic regression, decision trees, random forest and support vector machines. Information on validation of models was scarce. Based on the authors’ affiliations, there was a median 3:1 ratio between IT specialists and clinicians. Over half of studies were co-authored by a collaboration of medical and IT specialists. Approximately 25% of papers were authored solely by IT specialists who did not seek clinical input in data interpretation. The application of ML to datasets, in particular clustering methods, enabled the development of classification models assisting in testing the outcomes of patients with HF. There is, however, a tendency to over-claim the potential usefulness of ML models for clinical practice. The next body of work that is required for this research discipline is the design of randomised controlled trials (RCTs) with the use of ML in an intervention arm in order to prospectively validate these algorithms for real-world clinical utility.

scholarly journals Business intelligence and big data in hospitality and tourism: a systematic literature review

2018 ◽  
Vol 30 (12) ◽  
pp. 3514-3554 ◽  
Author(s):  
Marcello Mariani ◽  
Rodolfo Baggio ◽  
Matthias Fuchs ◽  
Wolfram Höepken
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Business Intelligence ◽  
Data Science ◽  
Academic Research ◽  
Research Field ◽  
Future Research ◽  
Content Type ◽  
Hospitality And Tourism

PurposeThis paper aims to examine the extent to which Business Intelligence and Big Data feature within academic research in hospitality and tourism published until 2016, by identifying research gaps and future developments and designing an agenda for future research.Design/methodology/approachThe study consists of a systematic quantitative literature review of academic articles indexed on the Scopus and Web of Science databases. The articles were reviewed based on the following features: research topic; conceptual and theoretical characterization; sources of data; type of data and size; data collection methods; data analysis techniques; and data reporting and visualization.FindingsFindings indicate an increase in hospitality and tourism management literature applying analytical techniques to large quantities of data. However, this research field is fairly fragmented in scope and limited in methodologies and displays several gaps. A conceptual framework that helps to identify critical business problems and links the domains of business intelligence and big data to tourism and hospitality management and development is missing. Moreover, epistemological dilemmas and consequences for theory development of big data-driven knowledge are still a terra incognita. Last, despite calls for more integration of management and data science, cross-disciplinary collaborations with computer and data scientists are rather episodic and related to specific types of work and research.Research limitations/implicationsThis work is based on academic articles published before 2017; hence, scientific outputs published after the moment of writing have not been included. A rich research agenda is designed.Originality/valueThis study contributes to explore in depth and systematically to what extent hospitality and tourism scholars are aware of and working intendedly on business intelligence and big data. To the best of the authors’ knowledge, it is the first systematic literature review within hospitality and tourism research dealing with business intelligence and big data.

Stakeholder theory in the hospitality field: Insights drawn from a systematic literature review

2021 ◽  
Vol 00 (00) ◽  
pp. 1-25
Author(s):  
Simone R. Barakat ◽  
Elizabeth K. Wada
Keyword(s):  
Literature Review ◽  
Stakeholder Theory ◽  
Systematic Literature Review ◽  
Academic Research ◽  
Holistic Approach ◽  
Ethical Considerations ◽  
Theoretical Perspectives ◽  
Important Approach ◽  
Research Findings ◽  
Value Creation Process

The purpose of this article is to review and analyse the state of stakeholder theory in hospitality scholarship in terms of its themes, contexts, theoretical perspectives and methodological approaches. The authors gathered and summarized relevant theory and empirical research findings that allowed for further theoretical insights to be drawn. A total of 91 articles published between 1984 and 2018 were analysed using a systematic literature review. The review indicates that stakeholder theory offers an important approach for understanding hospitality because of the following benefits: it leads to reflections on the interests and influences of all those involved in the value creation process; it is a holistic approach, integrating economic, social and ethical considerations; it adopts a relational approach rather than just a transactional approach and it provides a strategic framework that managers can use. The study’s findings show that stakeholder-related research remains underexplored in the hospitality field. There is, however, great potential for developing the theory by exploring the connections that exist between the principles of stakeholder theory and knowledge of hospitality. The article also provides suggestions for future applications of stakeholder theory in academic research and highlights its relevance to managerial practice.

Sign in / Sign up

Export Citation Format

Share Document