Overview
This advanced unit builds on the ethical and legal foundations introduced in the core units LAWS20063 Governance and Business Law and MGMT20130 Operations Management and Business Analytics. The emerging field of data science encompasses "big data" and "data analytics". In this unit you will analyse ethical considerations specific to study design, data collection methods, data analysis and the appropriate dissemination and application of findings. You will apply the ethical duties of researchers and analysts to ensure that ethical protocols have been respected and that the rights and consequences of participants and users have been acknowledged and respected. You will employ practical tools to help you to identify ethical dilemmas and develop strategies for ensuring ethical decision making and resulting behaviours. You will also examine the role of organisational and industry cultures in shaping ethical (or unethical) data analytic practices, thus addressing the United Nation's sustainability development goals with respect to responsible business operations, new business models, investment, innovation and technology and collaboration.
Details
Pre-requisites or Co-requisites
Students must have completed, or have been granted credit for MGMT29009 Operations Management and Business Analytics and LAWS29001 Governance and Business Law to undertake this unit. Students enrolling in this unit must be undertaking the CL84 Master of Business Administration (International).
Important note: Students enrolled in a subsequent unit who failed their pre-requisite unit, should drop the subsequent unit before the census date or within 10 working days of Fail grade notification. Students who do not drop the unit in this timeframe cannot later drop the unit without academic and financial liability. See details in the Assessment Policy and Procedure (Higher Education Coursework).
Offerings For Term 1 - 2022
Attendance Requirements
All on-campus students are expected to attend scheduled classes – in some units, these classes are identified as a mandatory (pass/fail) component and attendance is compulsory. International students, on a student visa, must maintain a full time study load and meet both attendance and academic progress requirements in each study period (satisfactory attendance for International students is defined as maintaining at least an 80% attendance record).
Recommended Student Time Commitment
Each 6-credit Postgraduate unit at CQUniversity requires an overall time commitment of an average of 12.5 hours of study per week, making a total of 150 hours for the unit.
Class Timetable
Assessment Overview
Assessment Grading
This is a graded unit: your overall grade will be calculated from the marks or grades for each assessment task, based on the relative weightings shown in the table above. You must obtain an overall mark for the unit of at least 50%, or an overall grade of ‘pass’ in order to pass the unit. If any ‘pass/fail’ tasks are shown in the table above they must also be completed successfully (‘pass’ grade). You must also meet any minimum mark requirements specified for a particular assessment task, as detailed in the ‘assessment task’ section (note that in some instances, the minimum mark for a task may be greater than 50%). Consult the University’s Grades and Results Policy for more details of interim results and final grades.
All University policies are available on the CQUniversity Policy site.
You may wish to view these policies:
- Grades and Results Policy
- Assessment Policy and Procedure (Higher Education Coursework)
- Review of Grade Procedure
- Student Academic Integrity Policy and Procedure
- Monitoring Academic Progress (MAP) Policy and Procedure – Domestic Students
- Monitoring Academic Progress (MAP) Policy and Procedure – International Students
- Student Refund and Credit Balance Policy and Procedure
- Student Feedback – Compliments and Complaints Policy and Procedure
- Information and Communications Technology Acceptable Use Policy and Procedure
This list is not an exhaustive list of all University policies. The full list of University policies are available on the CQUniversity Policy site.
- Critically reflect on the ethical dimensions of the data science, its purpose, methods and impact in data-driven organisations
- Apply ethical techniques in auditing data-driven processes in organisational contexts
- Analyse established ethical techniques and strategies, independently and within teams, to identify and minimize potential harm associated with data driven organisational processes
- Demonstrate knowledge, skills and ideas related to ethical aspects of data, its collection, management and uses to a range of stakeholders.
Alignment of Assessment Tasks to Learning Outcomes
Assessment Tasks | Learning Outcomes | |||
---|---|---|---|---|
1 | 2 | 3 | 4 | |
1 - Written Assessment - 40% | ||||
2 - Presentation - 30% | ||||
3 - Reflective Practice Assignment - 30% |
Alignment of Graduate Attributes to Learning Outcomes
Graduate Attributes | Learning Outcomes | |||
---|---|---|---|---|
1 | 2 | 3 | 4 | |
1 - Knowledge | ||||
2 - Communication | ||||
3 - Cognitive, technical and creative skills | ||||
4 - Research | ||||
5 - Self-management | ||||
6 - Ethical and Professional Responsibility | ||||
7 - Leadership | ||||
8 - Aboriginal and Torres Strait Islander Cultures |
Alignment of Assessment Tasks to Graduate Attributes
Assessment Tasks | Graduate Attributes | |||||||
---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | |
1 - Written Assessment - 40% | ||||||||
2 - Presentation - 30% | ||||||||
3 - Reflective Practice Assignment - 30% |
Textbooks
There are no required textbooks.
Additional Textbook Information
There are no required textbooks.
Students will utilise a variety of resources including business, economic and market information from the internet and academic journals in their independent research to identify workplace issues and recommend viable applications of data ethics in businesses that enhance organisational effectiveness.
Recommended/Supplementary Textbook Information.
The following texts are highly recommended as supplementary references:
Business Ethics:
Carroll, A., Brown, J. A. & Buchholz, A.K. (2018). Business and Society: Ethics, Sustainability, and Stakeholder Management, Cengage.
Collins, D. (2009). Essentials of business ethics: Creating an organization of high integrity and superior performance. John Wiley & Sons.
Ferrell, O.C., Fraedrich, J., & Ferrell, L. (2018). Business ethics: ethical decision making & cases, 12th Ed., South West College.
Langley, Q. (2020). Business and the culture of ethics. Business Expert Press.
Schwartz, M. S. (2017). Business ethics: an ethical decision-making approach. John Wiley & Sons, Inc.
Stanwick, P. A., & Stanwick, S. D. (2020). Absolute essentials of business ethics. Routledge.
Weiss, J. (2014). Business ethics. Oakland: Berrett-Koehler Inc.
Wasieleski, D. M. & Weber, J. (2019). Business ethics. Emerald Group Publishing
Data and Ethics:
Bruhn, J. G. (2009). The functionality of gray area ethics in organizations. Journal of Business Ethics, 89(2), 205–214.
Coeckelbergh, M. (2020). AI ethics. MIT Press.
Davis, K. (2012). Ethics of big data: Balancing risk and innovation. O'Reilly Media, Inc.
Floridi, L. & Taddeo, M. (2016). What is data ethics? Philosophical Transactions of the Royal Society A, 374: 20160360.
Franks, B. (2020). 97 things about ethics everyone in data science should know: collective wisdom from the experts. O’Reilly Media, Inc.
Hasselbalch, G. & Tranberg, P. (2016). Data ethics: the competitive advantage. AKA Print.
Keller, S. A., Shipp, S. S., Schroeder, A. D., & Korkmaz, G. (2020). Doing data science: a framework and case study. Harvard Data Science Review, 2(1).
Loukides, M., Mason, H., Patil, D., & Patil, DJ. (2018). Ethics and Data Science. O'Reilly Media, Inc.
Mateosian, R. (2013). Ethics of big data. IEEE MICRO, 33(2), 60-61.
National Academies of Sciences, Engineering, and Medicine. (2018). Data matters: ethics, data, and international research collaboration in a changing world: proceedings of a workshop. Washington, D.C.: The National Academies Press.
Novak, R. & Pavlicek, A. (2021). Data Experts as the Balancing Power of Big Data Ethics. Information, 12, 97.
Richterich, A. (2018). The big data agenda: data ethics and critical data studies. London: University of Westminster Press.
IT Resources
- CQUniversity Student Email
- Internet
- Unit Website (Moodle)
- Zoom (both microphone and webcam capability)
All submissions for this unit must use the referencing style: American Psychological Association 7th Edition (APA 7th edition)
For further information, see the Assessment Tasks.
k.taruc2@cqu.edu.au
g.ilott@cqu.edu.au
Module/Topic
Unit framing of the term.
Definitions, fundamental principles and key concepts of business ethics.
Chapter
Stanwick & Stanwick (2020). Ch. 1.
Carroll, Brown & Buchholz (2018). Ch. 7 & 10
Schwartz (2017). Part One and Part Two.
Events and Submissions/Topic
Module/Topic
Business and society relationships.
The stakeholder approach to business, society, and ethics
Chapter
Stanwick & Stanwick (2020). Ch. 2.
Carroll, Brown & Buchholz (2018). Ch. 1 & 3.
Events and Submissions/Topic
Students choose their individual cases for Reflective Practice Assignment.
Module/Topic
Privacy and security, open data, data asymmetries and data philanthropy, informed consent, algorithmic bias, data economies.
Chapter
Richterich, A. (2018), Ch. 3.
National Academies of Sciences, Engineering, and Medicine (2018). Ch. 2.
Events and Submissions/Topic
Module/Topic
Data-driven business model.
Digital trust in business.
Chapter
Hasselbalch & Tranberg (2016). Ch. 2 & 4.
Events and Submissions/Topic
Module/Topic
Balancing risk and innovation.
Gray area ethics in organizations
Chapter
Davis, K. (2012).
Bruhn, J. G. (2009).
Events and Submissions/Topic
Module/Topic
Chapter
Events and Submissions/Topic
Individual case assignment due: Reflective Practice Assignment
Reflective Practice Assignment Due: Vacation Week Thursday (14 Apr 2022) 6:30 pm AEST
Module/Topic
Data privacy, data protection and political agenda
Chapter
Hasselbalch & Tranberg (2016). Ch. 5-6 and 9.
Events and Submissions/Topic
Group formation and allocation of Case Analysis.
Module/Topic
Data Experts as the balancing power of big data ethics.
Chapter
Novak & Pavlicek (2021)
Events and Submissions/Topic
Module/Topic
Data ethical innovation and investment in a business.
Chapter
Hasselbalch & Tranberg (2016). Ch. 7 & 8.
National Academies of Sciences, Engineering, and Medicine (2018). Ch. 3.
Events and Submissions/Topic
Module/Topic
Data monopoly and value clashes Responsible innovation and embedding values in design.
Chapter
Coeckelbergh, M. (2020). Ch. 10, 11, and 12.
Events and Submissions/Topic
Module/Topic
Ethical data management culture as a business strategy
Chapter
Loukides, Mason, Patil & Patil (2018).
Events and Submissions/Topic
Module/Topic
Ethical data, business ethics, and global economy.
Chapter
Ferrell, Fraedrich & Ferrell, (2018), Ch. 8, 9, 10.
Hasselbalch, G. & Tranberg, P. (2016). Ch. 10 & 11.Events and Submissions/Topic
Module/Topic
Group Presentations.
Chapter
Events and Submissions/Topic
Group Presentations 15-20 minutes per group. PowerPoints to be provided to unit coordinator prior to presentation
Group Presentation and Written Assessment Due: Week 12 Thursday (2 June 2022) 6:30 pm AEST
Module/Topic
Unit review and take home paper preparation.
Chapter
Review of key chapters and readings.
Events and Submissions/Topic
Distribution of case for take-home paper.
Information of take-home procedure and expectations.
Module/Topic
Chapter
Events and Submissions/Topic
Take-Home Case Analysis Exam.
Due: June 16, 2022 – 6.30 pm WIB
Take Home Exam Due: Exam Week Thursday (16 June 2022) 6:30 pm AEST
- Kemal Taruc (k.taruc2@cqu.edu.au)
- Gerard Ilott (g.ilott@cqu.edu.au)
1 Reflective Practice Assignment
The key aspect of reflection is a critical evaluation of the self. Students will choose their individual cases between weeks 2-5 and will be using them for the assignment.
Students are to reflect on their decisions by examining their understanding and perceived assumptions about data and ethics in their respective organisations or business setting. Students are to provide a conclusion based on the ethical principles and theories discussed in class.
Vacation Week Thursday (14 Apr 2022) 6:30 pm AEST
This is an individual assessment task.
Week 8 Friday (6 May 2022)
Feedback is provided via Feedback Studio
· Provide a reflection of the ethical dilemma, why the organisation/company made the decision and whether they adopt approaches given the concepts studied in class.
· The use of between 5-10 quality references including key readings and recommended texts using APA referencing and including a reference list.
· These are the minimum requirements. Students should note that satisfactorily meeting the minimum requirements will typically result in the minimum pass grade being awarded.
- Critically reflect on the ethical dimensions of the data science, its purpose, methods and impact in data-driven organisations
- Analyse established ethical techniques and strategies, independently and within teams, to identify and minimize potential harm associated with data driven organisational processes
- Knowledge
- Communication
- Self-management
- Ethical and Professional Responsibility
2 Presentation
All students will be allocated to a group by the unit coordinator in Week 6 of term. These groups will be non-negotiable.
Students will undertake analysis of an organisation examining its engagement through its ethical aspects of data, its collection, management and uses to a range of stakeholders.
This assignment requires an analysis of the organisation's implementation of these policies and procedures against the concepts examined in the literatures.
Each group will be afforded 15-20 minutes to present and must submit an Executive Report of approximately 1000 words with a detailed reference list. A copy of the presentation must be provided to the unit coordinator prior to the presentation. Each student must be in a group and present as part of that group.
This is the minimum standard expected for this assignment. Students who only meet the minimum, should expect to receive the minimum pass grade.
As Masters students, you are required to engage in research as per the Australia Quality Framework (AQF) guidelines.
Two specific requirements need to be considered:
· students need to demonstrate a body of knowledge that includes the understanding of the existing concepts, principles and theories in the data science discipline and/or area of professional practice, and;
· demonstrate knowledge of research principles and methods applicable to a field of work and/or learning.
Please note that Presentations are undertaken by online or a prerecorded presentation. Students are to submit via the assessment folder in Moodle by the due date.
Please note a late penalty of 5% per day applies for each day or part-day (including weekends) for assignments submitted after the due date.
Week 12 Thursday (2 June 2022) 6:30 pm AEST
This is a group task but only 1 person representing each group submits the assignment.
Exam Week Monday (13 June 2022)
Feedback will be provided at the time of the presentation and on the written executive report via Feedback Studio
Students are to demonstrate knowledge of the subject matter, and show effective presentation skills by:
· Providing a concise and structured presentation with introduction, main presentation and conclusion.
· Effectively using audio visuals and verbal communication delivered within the time allocation (15-20 mins per group).
· Identifying of company strengths and weaknesses against the CSR/Sustainability analytical frame. Providing a clear set of recommendations supported by literature (if and when appropriate)
· Demonstrating a commitment to professional business presentation standard.
· Ensuring all group members to make a balanced contribution in the presentation.
· A copy of the presentation (PowerPoints) MUST be provided to the unit coordinator prior to the presentation.
Students are to demonstrate knowledge of the subject matter, and effective written skills by:
· Providing a concise overview of the content of the presentation of approximately 1000 words using key concepts and theories as an analytical framework.
· Identifying the ethical grey areas and contending issues in the case study supported by the literature.
· Using a minimum of 12-16 quality references made up of recommended texts and academic journal articles so as to demonstrate breadth and quality of research, including citation of the recommended texts
· The use of the APA in text referencing system to correctly cite academic sources.
- Apply ethical techniques in auditing data-driven processes in organisational contexts
- Demonstrate knowledge, skills and ideas related to ethical aspects of data, its collection, management and uses to a range of stakeholders.
- Knowledge
- Communication
- Research
- Self-management
- Leadership
3 Written Assessment
In Week 13 (9 June 2022) a case study will be distributed to all students but without any questions. You will have approximately 2 weeks to familiarise yourself with the case and the events and decisions described in the scenario.
During Exam Week on a specific date, you will be asked to answer a series of questions requiring you to apply your understanding of the unit content directly to the case. These questions will be made available 10 minutes prior to the opening of the submission.
This will be a time-limited submission. You will have 210 minutes (3.5 hours) to respond to a series of questions regarding the case that will require integrated answers utilising a variety of ethical concepts from the unit. You may use texts, journals, and notes in responding to the questions.
As this is an open book style response, accurate use of concepts, terms, and models is expected.
The focus of the assessment is on the correct application of data and ethics terms, concepts, theories, and analysis.
Referencing is not required for this assessment.
However, any submission forwarded after the scheduled submission due date will not be accepted. Please kindly be advised.
Exam Week Thursday (16 June 2022) 6:30 pm AEST
This is an individual assessment task.
Students will receive limited case feedback via Moodle on Certification of Grades day.
Please note as this is an open book style response, students are expected to demonstrate ethical concepts and their data application correctly as a basic standard. The key assessment criteria include:
· The correct use of key ethical concepts and theories as an analytical frame to identify ethical issues in the case
· The correct use of key data science concepts and theories as an analytical frame
· Provide options in the decision making
Students must note that advocating unethical or illegal practices as solutions or recommendations to case dilemmas is not acceptable.
- Critically reflect on the ethical dimensions of the data science, its purpose, methods and impact in data-driven organisations
- Apply ethical techniques in auditing data-driven processes in organisational contexts
- Analyse established ethical techniques and strategies, independently and within teams, to identify and minimize potential harm associated with data driven organisational processes
- Demonstrate knowledge, skills and ideas related to ethical aspects of data, its collection, management and uses to a range of stakeholders.
- Knowledge
- Communication
- Cognitive, technical and creative skills
- Research
- Self-management
- Ethical and Professional Responsibility
As a CQUniversity student you are expected to act honestly in all aspects of your academic work.
Any assessable work undertaken or submitted for review or assessment must be your own work. Assessable work is any type of work you do to meet the assessment requirements in the unit, including draft work submitted for review and feedback and final work to be assessed.
When you use the ideas, words or data of others in your assessment, you must thoroughly and clearly acknowledge the source of this information by using the correct referencing style for your unit. Using others’ work without proper acknowledgement may be considered a form of intellectual dishonesty.
Participating honestly, respectfully, responsibly, and fairly in your university study ensures the CQUniversity qualification you earn will be valued as a true indication of your individual academic achievement and will continue to receive the respect and recognition it deserves.
As a student, you are responsible for reading and following CQUniversity’s policies, including the Student Academic Integrity Policy and Procedure. This policy sets out CQUniversity’s expectations of you to act with integrity, examples of academic integrity breaches to avoid, the processes used to address alleged breaches of academic integrity, and potential penalties.
What is a breach of academic integrity?
A breach of academic integrity includes but is not limited to plagiarism, self-plagiarism, collusion, cheating, contract cheating, and academic misconduct. The Student Academic Integrity Policy and Procedure defines what these terms mean and gives examples.
Why is academic integrity important?
A breach of academic integrity may result in one or more penalties, including suspension or even expulsion from the University. It can also have negative implications for student visas and future enrolment at CQUniversity or elsewhere. Students who engage in contract cheating also risk being blackmailed by contract cheating services.
Where can I get assistance?
For academic advice and guidance, the Academic Learning Centre (ALC) can support you in becoming confident in completing assessments with integrity and of high standard.