Key Concepts for a Data Science Ethics Curriculum

2018 ◽  
Author(s):  
Jeffrey S. Saltz ◽  
Neil I. Dewar ◽  
Robert Heckman
Keyword(s):  
Data Science ◽  
Science Ethics ◽  
Key Concepts ◽  
Ethics Curriculum

scholarly journals Data science ethics in government

2016 ◽  
Vol 374 (2083) ◽  
pp. 20160119 ◽  
Author(s):  
Cat Drew
Keyword(s):  
Data Science ◽  
New Technologies ◽  
Unintended Consequences ◽  
Ethical Framework ◽  
Social Good ◽  
Science Ethics ◽  
Policy Goals ◽  
Challenges And Opportunities ◽  
Government Data ◽  
The Government

Data science can offer huge opportunities for government. With the ability to process larger and more complex datasets than ever before, it can provide better insights for policymakers and make services more tailored and efficient. As with all new technologies, there is a risk that we do not take up its opportunities and miss out on its enormous potential. We want people to feel confident to innovate with data. So, over the past 18 months, the Government Data Science Partnership has taken an open, evidence-based and user-centred approach to creating an ethical framework. It is a practical document that brings all the legal guidance together in one place, and is written in the context of new data science capabilities. As part of its development, we ran a public dialogue on data science ethics, including deliberative workshops, an experimental conjoint survey and an online engagement tool. The research supported the principles set out in the framework as well as provided useful insight into how we need to communicate about data science. It found that people had a low awareness of the term ‘data science’, but that showing data science examples can increase broad support for government exploring innovative uses of data. But people's support is highly context driven. People consider acceptability on a case-by-case basis, first thinking about the overall policy goals and likely intended outcome, and then weighing up privacy and unintended consequences. The ethical framework is a crucial start, but it does not solve all the challenges it highlights, particularly as technology is creating new challenges and opportunities every day. Continued research is needed into data minimization and anonymization, robust data models, algorithmic accountability, and transparency and data security. It also has revealed the need to set out a renewed deal between the citizen and state on data, to maintain and solidify trust in how we use people's data for social good. This article is part of the themed issue ‘The ethical impact of data science’.

Data science ethics

2021 ◽  
pp. 159-180
Author(s):  
Benjamin S. Baumer ◽  
Daniel T. Kaplan ◽  
Nicholas J. Horton
Keyword(s):  
Data Science ◽  
Science Ethics

Delivering Business Analytics Competencies and Skills: A Supply Side Assessment

2020 ◽  
Vol 50 (4) ◽  
pp. 239-254
Author(s):  
Kala C. Seal ◽  
Linda A. Leon ◽  
Zbigniew H. Przasnyski ◽  
Greg Lontok
Keyword(s):  
Data Science ◽  
Soft Skills ◽  
Supply Side ◽  
Business Analytics ◽  
Job Advertisements ◽  
Key Concepts ◽  
Business Component ◽  
Elective Course ◽  
University Curricula ◽  
The University

This paper investigates the alignment of the demand side of business analytics (based on advertised job positions) with the supply side (based on the university curricula of U.S. business analytics programs). We text-mined job advertisements and core and elective course descriptions to identify the competencies demanded by business analytics–related jobs and those being taught in graduate analytics programs that include a business component. A comparative analysis of the competencies reveals that, although some of the competencies required by the jobs are taught adequately at the universities, a few key concepts and topics are not covered at sufficient depth as needed by the jobs. The research also shows that many traditional analytics topics are being over-taught at the universities when compared with the demand, whereas some key soft skills are not addressed at a sufficient level in many programs. The study provides a basis for future comparison of data-science positions and programs with the jobs and programs in business analytics.

scholarly journals Google's Project Nightingale highlights the necessity of data science ethics review

2020 ◽  
Vol 12 (3) ◽  
Author(s):  
Christophe Olivier Schneble ◽  
Bernice Simone Elger ◽  
David Martin Shaw
Keyword(s):  
Data Science ◽  
Science Ethics ◽  
Ethics Review

scholarly journals The hidden assumptions in public engagement: A case study of engaging on ethics in government data analysis

2019 ◽  
Vol 3 (2) ◽  
pp. 180-190
Author(s):  
Emily S Rempel ◽  
Julie Barnett ◽  
Hannah Durrant
Keyword(s):  
Public Engagement ◽  
Data Science ◽  
Advisory Group ◽  
Final Report ◽  
Public Dialogue ◽  
Science Ethics ◽  
Stakeholder Interests ◽  
Government Data ◽  
Underlying Mechanisms ◽  
The Uk

This study examines the hidden assumptions around running public-engagement exercises in government. We study an example of public engagement on the ethics of combining and analysing data in national government – often called data science ethics. We study hidden assumptions, drawing on hidden curriculum theories in education research, as it allows us to identify conscious and unconscious underlying processes related to conducting public engagement that may impact results. Through participation in the 2016 Public Dialogue for Data Science Ethics in the UK, four key themes were identified that exposed underlying publicengagement norms. First, that organizers had constructed a strong imagined public as neither overly critical nor supportive, which they used to find and engage participants. Second, that official aims of the engagement, such as including publics in developing ethical data regulations, were overshadowed by underlying meta-objectives, such as counteracting public fears. Third, that advisory group members, organizers and publics understood the term 'engagement' in varying ways, from creating interest to public inclusion. And finally, that stakeholder interests, particularly government hopes for a positive report, influenced what was written in the final report. Reflection on these underlying mechanisms, such as the development of meta-objectives that seek to benefit government and technical stakeholders rather than publics, suggests that the practice of public engagement can, in fact, shut down opportunities for meaningful public dialogue.

scholarly journals What We Learned When We Compared Discussion Posts from One MOOC Hosted on Two Platforms

2021 ◽  
Vol 25 (4) ◽  
Author(s):  
Rebecca M. Quintana ◽  
Juan D. Pinto ◽  
Yuanru Tan
Keyword(s):  
Content Analysis ◽  
Data Science ◽  
Learning Experience ◽  
The Other ◽  
Science Ethics ◽  
Open Platform ◽  
Reflective Activity ◽  
Conventional Content Analysis ◽  
Insight Into

We compared discussion posts from a data science ethics MOOC that was hosted on two platforms. We characterized one platform as “open” because learners can respond to discussion prompts while viewing and responding to others. We characterized the other platform as “locked” because learners must respond to a discussion prompt before they can view and respond to others. Our objective is to determine whether these platform differences are consequential and have the potential to impact learning. We analyzed direct responses to two discussion prompts from two modules located in modules two and six of an eight module course. We used conventional content analysis to derive codes directly from the data. Posts on the “open” platform were characterized by failure to completely address the prompt and showed evidence of persuasion tactics and reflective activity. Posts on the “locked” platform were characterized by an apparent intent to complete the task and an assertive tone. Posts on the “locked” platform also showed a diversity of ideas through the corpus of responses. Our findings show that MOOC platform interfaces can lead to qualitative differences in discussion posts in ways that have the potential to impact learning. Our study provides insight into how “open” and “locked” platform designs have the potential to shape ways that learners respond to discussion prompts in MOOCs. Our study offers guidance for instructors making decisions on MOOC platform choice and activities situated within a learning experience.We used conventional content analysis to derive codes directly from the data. Posts on the “open” platform were characterized by failure to completely address the prompt and showed evidence of persuasion tactics and reflective activity. Posts on the “locked” platform were characterized by an apparent intent to complete the task and an assertive tone. Posts on the “locked” platform also showed a diversity of ideas through the corpus of responses. Our findings show that MOOC platform interfaces can lead to qualitative differences in discussion posts in ways that have the potential to impact learning. Our study provides insight into how “open” and “locked” platform designs have the potential to shape ways that learners respond to discussion prompts in MOOCs. Our study offers guidance for instructors making decisions on MOOC platform choice and activities situated within a learning experience.

scholarly journals Reading datasets: Strategies for interpreting the politics of data signification

2021 ◽  
Vol 8 (2) ◽  
pp. 205395172110293
Author(s):  
Lindsay Poirier
Keyword(s):  
Reading Strategies ◽  
Data Science ◽  
Science Students ◽  
Data Ethics ◽  
Double Binds ◽  
Data Semantics ◽  
Critical Attention ◽  
Vested Interests ◽  
Political Commitments ◽  
Ethics Curriculum

All datasets emerge from and are enmeshed in power-laden semiotic systems. While emerging data ethics curriculum is supporting data science students in identifying data biases and their consequences, critical attention to the cultural histories and vested interests animating data semantics is needed to elucidate the assumptions and political commitments on which data rest, along with the externalities they produce. In this article, I introduce three modes of reading that can be engaged when studying datasets—a denotative reading (extrapolating the literal meaning of values in a dataset), a connotative reading (tracing the socio-political provenance of data semantics), and a deconstructive reading (seeking what gets Othered through data semantics and structure). I then outline how I have taught students to engage these methods when analyzing three datasets in Data and Society—a course designed to cultivate student competency in politically aware data analysis and interpretation. I show how combined, the reading strategies prompt students to grapple with the double binds of perceiving contemporary problems through systems of representation that are always situated, incomplete, and inflected with diverse politics. While I introduce these methods in the context of teaching, I argue that the methods are integral to any data practice in the conclusion.

Ethnogeriatrics: Development and Application of the Cohort Analysis Tool

2014 ◽  
Vol 21 (2) ◽  
pp. 37-52
Author(s):  
Melen McBride
Keyword(s):  
Healthcare Professionals ◽  
Cohort Analysis ◽  
Analysis Tool ◽  
Speech Language Pathology ◽  
Clinical Tool ◽  
Research Activities ◽  
Key Concepts ◽  
Practice Teaching ◽  
Language Pathology ◽  
Significant Factors

Ethnogeriatrics is an evolving specialty in geriatric care that focuses on the health and aging issues in the context of culture for older adults from diverse ethnic backgrounds. This article is an introduction to ethnogeriatrics for healthcare professionals including speech-language pathologists (SLPs). This article focuses on significant factors that contributed to the development of ethnogeriatrics, definitions of some key concepts in ethnogeriatrics, introduces cohort analysis as a teaching and clinical tool, and presents applications for speech-language pathology with recommendations for use of cohort analysis in practice, teaching, and research activities.

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