scholarly journals From AI ethics principles to data science practice: a reflection and a gap analysis based on recent frameworks and practical experience

AI and Ethics ◽  
2022 ◽  
Author(s):  
Ilina Georgieva ◽  
Claudio Lazo ◽  
Tjerk Timan ◽  
Anne Fleur van Veenstra
Keyword(s):  
Data Science ◽  
Gap Analysis ◽  
Practical Experience ◽  
Science Practice ◽  
Ai Ethics

Building an Effective Data Science Practice

2022 ◽  
Author(s):  
Vineet Raina ◽  
Srinath Krishnamurthy
Keyword(s):  
Data Science ◽  
Science Practice

scholarly journals MaDaScA: Instruction of Data Science to Managers

10.28945/4271 ◽  
2019 ◽  
Keyword(s):  
Data Science ◽  
Engineering Students ◽  
Model Building ◽  
Practical Experience ◽  
Future Research ◽  
Ethical Aspects ◽  
Ethical Considerations ◽  
Science Activity ◽  
Background Data ◽  
Management Students

Aim/Purpose: Build a program that teaches prospect managers the skills that are relevant for leading data science activity. Background: Data science becomes ubiquitous in organizations. It is imperative to train students in management departments in the skills that are relevant to this field. Most courses in data science focus on technical knowledge like model building methods, and neglect organizational knowledge such as team roles, ethical considerations and project stages. This work suggests a complementary program that supplies the students with the required knowledge. The authors believe that this program is most suitable for management-students, and that it can also be adapted to software engineering students, in order to provide them with a wider scope. Contribution: We present the MaDaScA (Managing Data Science Activity) program. The program defines a list of topics that are required for managers’ education in order to lead data science activity. This work suggests the content and take-away messages of each topic. The paper surveys several existing courses that teach data-science to managers. Findings: All existing courses supply a part of the suggested topics, either focusing on technical aspects of data-science or on organizational aspects. In particular, only a small minority of the courses discuss ethical aspects of data science. Recommendations for Practitioners: We recommend adopting MaDaScA in management departments in order to prepare managers for the challenges in data-science. Recommendations for Researchers: We recommend adapting the MaDaScA model to the curriculum of the faculty of engineering, especially for the department of industrial engineering. Impact on Society: Educating prospect managers on the capabilities of data science and responsibilities that come with it is key for making sure organizations become much more data driven, efficient and ethical. Future Research: It is possible to make this program more effective by adding practical experience

scholarly journals Introduction to the Special Issue on Advanced Technologies in Assessment: A Science-Practice Concern

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Cornelius König ◽  
Andrew Demetriou ◽  
Philipp Glock ◽  
Annemarie Hiemstra ◽  
Dragos Iliescu ◽  
...  
Keyword(s):  
Psychological Assessment ◽  
Data Science ◽  
Interdisciplinary Collaboration ◽  
The European Union ◽  
Special Issue ◽  
Assessment Practice ◽  
Science Practice ◽  
Personnel Assessment ◽  
Advanced Technologies ◽  
Computer Scientists

This article is based on conversations from the project “Big Data in Psychological Assessment” (BDPA) funded by the European Union, which was initiated because of the advances in data science and artificial intelligence that offer tremendous opportunities for personnel assessment practice in handling and interpreting this kind of data. We argue that psychologists and computer scientists can benefit from interdisciplinary collaboration. This article aims to inform psychologists who are interested in working with computer scientists about the potentials of interdisciplinary collaboration, as well as the challenges such as differing terminologies, foci of interest, data quality standards, approaches to data analyses, and diverging publication practices. Finally, we provide recommendations preparing psychologists who want to engage in collaborations with computer scientists. We argue that psychologists should proactively approach computer scientists, learn computer scientific fundamentals, appreciate that research interests are likely to converge, and prepare novice psychologists for a data-oriented scientific future.

The development of the data science capability maturity model: a survey-based research

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mert Onuralp Gökalp ◽  
Ebru Gökalp ◽  
Kerem Kayabay ◽  
Altan Koçyiğit ◽  
P. Erhan Eren
Keyword(s):  
Data Science ◽  
Gap Analysis ◽  
Process Capability ◽  
Capability Maturity Model ◽  
International Electrotechnical Commission ◽  
Maturity Model ◽  
Data Governance ◽  
Content Type ◽  
Capability Maturity ◽  
Development Framework

PurposeThe purpose of this paper is to investigate social and technical drivers of data science practices and develop a standard model for assisting organizations in their digital transformation by providing data science capability/maturity level assessment, deriving a gap analysis, and creating a comprehensive roadmap for improvement in a standardized way.Design/methodology/approachThis paper systematically reviews and synthesizes the existing literature-related to data science and 183 practitioners' considerations by employing a survey-based research method. By blending the findings of this research with a well-established process capability maturity model standard, International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 330xx, and following a methodological maturity development framework, a theoretically grounded model, entitled as the data science capability maturity model (DSCMM) was developed.FindingsIt was found that organizations seek a capability/maturity model standard to evaluate and improve their current data science capabilities. To close this research gap, the DSCMM is developed. It consists of six capability maturity levels and twenty-seven processes categorized under five process areas: organization, strategy management, data analytics, data governance and technology management.Originality/valueThis paper validates the need for a process capability maturity model for the data science domain and develops the DSCMM by integrating literature findings and practitioners' considerations into a well-accepted process capability maturity model standard to continuously assess and improve the maturity of data science capabilities of organizations.

scholarly journals Data Science Data Governance [AI Ethics]

2018 ◽  
Vol 16 (6) ◽  
pp. 61-70 ◽  
Author(s):  
Joshua A. Kroll
Keyword(s):  
Data Science ◽  
Data Governance ◽  
Science Data ◽  
Ai Ethics

Introduction to Data Science

2021 ◽  
pp. 1-18
Author(s):  
M. Govindarajan
Keyword(s):  
Data Analysis ◽  
Predictive Modeling ◽  
Data Science ◽  
Science Data ◽  
Science Practice ◽  
Scientific Methods ◽  
Exploratory Data ◽  
Product Creation ◽  
Modeling Data ◽  
Term Data

This chapter focuses on introduction to the field of data science. Data science is the area of study which involves extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes. The term data science has emerged because of the evolution of mathematical statistics, data analysis, and big data. Data science helps to discover hidden patterns from the raw data. It enables to translate a business problem into a research project and then translate it back into a practical solution. The purpose of this chapter is to provide emphasis on integration and synthesis of concepts, techniques, applications, and tools to deal with various facets of data science practice, including data collection and integration, exploratory data analysis, predictive modeling, descriptive modeling, data product creation, evaluation, and effective communication.

scholarly journals Data science skills and domain knowledge requirements in the manufacturing industry: A gap analysis

2021 ◽  
Vol 60 ◽  
pp. 692-706
Author(s):  
Guoyan Li ◽  
Chenxi Yuan ◽  
Sagar Kamarthi ◽  
Mohsen Moghaddam ◽  
Xiaoning Jin
Keyword(s):  
Domain Knowledge ◽  
Manufacturing Industry ◽  
Data Science ◽  
Gap Analysis ◽  
Knowledge Requirements

The Necessity for Collaboration Between Data Scientists and Domain Experts

2021 ◽  
Author(s):  
Patrick Bangert
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Change Management ◽  
Real World ◽  
Data Science ◽  
Effective Communication ◽  
Practical Experience ◽  
Domain Experts ◽  
Science Projects ◽  
Domain Expertise

Abstract A practical data science, machine learning, or artificial intelligence project benefits from various organizational and managerial prerequisites. The effective collaboration between various data scientists and domain experts is perhaps the most important, which is discussed here. Based on practical experience, the principal theses put forward here are that (1) data science projects require domain expertise, (2) domain expertise and data science expertise generally cannot be provided by the same individual, (3) effective communication between the various experts is essential for which everyone requires some limited understanding of the others’ expertise and real-world experience, and (4) management must acknowledge these aspects by reserving sufficient project time and budget for communication and change management.

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