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 2 - 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.
Feedback, Recommendations and Responses
Every unit is reviewed for enhancement each year. At the most recent review, the following staff and student feedback items were identified and recommendations were made.
Feedback from Self-reflection
Need more discussion among students
Implement more interactive workshop activities to encourage students to participate in discussions.
Feedback from Self-reflection
Need more collection of available 'case studies'
Case studies (e.g. Harvard style) on the unit theme would be a good source for reference as well as for the final exam or group assessment.
- 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 references will be provided in the unit profile, also in the unit introduction section and in the weekly plan on Moodle.
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
- Framing the learning process of the unit.
- Business and society relationships.
- The significant roles of data and ethics.
Chapter
- Ramsey, C. (2016). Introducing reflective learning. Open University.
- Carroll, Brown & Buchholz (2018). Ch. 1 & 3.
- Stanwick, P. A., & Stanwick, S. D. (2020). Ch. 2.
Events and Submissions/Topic
Module/Topic
- Definitions, fundamental principles, key concepts and perspectives of business ethics.
- The ethical responsibility of business to its stakeholders.
Chapter
- Stanwick & Stanwick (2020). Ch. 1.
- Carroll, Brown & Buchholz (2018). Ch. 7 & 10
- Schwartz, M. S. (2017). Part One and Part Two.
Events and Submissions/Topic
Students choose their individual cases for the Reflective Practice Assignment.
Module/Topic
- Open data, privacy and security, data asymmetries and algorithmic bias.
- Data philanthropy, informed consent, and data economies.
Chapter
- Richterich, A. (2018). Ch. 3.
- Sarangi, S. and Sharma, P. (2020). Introduction.
Events and Submissions/Topic
Module/Topic
- The “grey area” of data ethics in organizations.
-
Key principles, models and tools to manage grey areas to improve company’s performance.
Chapter
- Bruhn, J. G. (2009).
- National Academies of Sciences, Engineering, and Medicine. (2018). Ch. 2.
Events and Submissions/Topic
Module/Topic
- Key concepts and principles of organisational culture as a framework for understanding ethical practices in business.
- Building an ethical data management culture as the new strategic imperative.
Chapter
- Schwartz, M. S. (2017). Part 3.
Please refer to unit notes on the Moodle site for additional text and journal articles.
Events and Submissions/Topic
Module/Topic
Chapter
Events and Submissions/Topic
Module/Topic
- Data-driven business model.
- Building “trust” for consumers and stakeholders in the digital space of business.
Chapter
- Hasselbalch, G. & Tranberg, P. (2016). Ch. 2 & 4.
- Loukides, M., Mason, H., Patil, D., & Patil, DJ. (2018).
Events and Submissions/Topic
First individual assignment (Reflection Essay) due Thursday of this week.
Reflective Practice Assignment
Due: Thursday, Aug. 25, 2022 at 9:30 pm AEST (or 18:30 WIB)
Group formation and allocation of Case Analysis.
REFLECTIVE PRACTICE ASSIGNMENT Due: Week 6 Thursday (25 Aug 2022) 9:30 pm AEST
Module/Topic
- The trends of emerging technologies and big data.
- Key concepts and approaches to business innovation and investment, and the importance of data ethics.
Chapter
- Hasselbalch, G. & Tranberg, P. (2016). Ch. 7 & 8.
- National Academies of Sciences, Engineering, and Medicine. (2018). Ch. 3
- Davis, K. (2012).
Events and Submissions/Topic
Module/Topic
- The trend on urgency in dealing with the ethical and societal challenges raised by AI.
- Policy initiatives to provide normative guidance on ethical problems with AI.
Chapter
- Boddington, P. (2017). Ch. 1, 3.
- Coeckelbergh, M. (2020). Ch. 10, 11, and 12.
Events and Submissions/Topic
Module/Topic
- Controversies on trends of new requirements regarding businesses' treatment of data.
- Data privacy, data protection across jurisdictions and the political agenda.
Chapter
- Hasselbalch & Tranberg (2016). Ch. 5, 6, and 9.
Events and Submissions/Topic
Module/Topic
- The socio-technological phenomenon of big data.
- The roles of professional associations and expert groups.
- Data experts as the balancing power of big data ethics.
Chapter
- Boddington, P. (2017). Ch. 4, 5, 6.
Please refer to unit notes on the Moodle site for additional text and journal articles.
Events and Submissions/Topic
Module/Topic
- The trend and impacts of data science and AI applications on the global economy and world affairs.
- Ethical and legal issues of big data across cultures and inter-/transgovernmental settings.
Chapter
- Ferrell, Fraedrich & Ferrell, (2018), Ch. 8, 9, 10.
- Hasselbalch, G. & Tranberg, P. (2016). Ch. 10 & 11.
Events and Submissions/Topic
Module/Topic
Chapter
Events and Submissions/Topic
Group presentation and written report due: Thursday, Oct. 6, 2022 at 09:30 pm AEST (or 18:300 WIB).
- 20-30 minutes presentation per group.
- PowerPoints and written reports to be provided to the unit coordinator prior to the presentation.
GROUP PRESENTATION AND WRITTEN ASSESSMENT Due: Week 12 Thursday (6 Oct 2022) 9:30 pm AEST
Module/Topic
Unit review
Chapter
All previous weeks’ unit materials and references
Events and Submissions/Topic
- Distribution of take-home exam case materials.
- Information on take-home exams and expectations.
Module/Topic
Take-Home Exam
Chapter
Events and Submissions/Topic
Take-Home Exam Due:
Friday, Oct. 21, 2022 at 01.00 am AEST (or Thursday, Oct. 20, 200 at 22.00 WIB)
TAKE HOME EXAM Due: Exam Week Friday (21 Oct 2022) 1:00 am AEST
Overview
The emerging field of data science encompasses "big data" and "data analytics". In this unit you will analyse ethical considerations specific to the design of data-driven business models, data collection methods, data analysis and the appropriate dissemination and application of findings. 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 both commerce and common societal goals with respect to ethical and responsible business operations, new business investment models, and ICT and AI innovations.
Details
Career Level: Postgraduate
Unit Level: Level 9
Credit Points: 6
Student Contribution Band: 10
Fraction of Full-Time Student Load: 0.125
Pre-requisites or Co-requisites
Students enrolling in this unit must be undertaking the CL84 Master of Business Administration (International). 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.
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 2 - 2022
Jakarta
Attendance Requirements
All on-campus students are expected to attend scheduled classes – in some units, these classes are identified as mandatory (pass/fail) components and attendance is compulsory.
For 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).
Website
This unit has a website, within the Moodle system, which is available two weeks before the start of term. It is important that you visit your Moodle site throughout the term. Please visit Moodle for more information.
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 real-life experiences in their respective organisations or business setting. Students are to provide an understanding and perceived assumptions about data and ethics, and make conclusions based on the ethical principles and theories discussed in class.
Week 6 Thursday (25 Aug 2022) 9:30 pm AEST
This is an individual assessment task.
Week 9 Wednesday (14 Sept 2022)
Feedback is provided via Moodle.
- 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 the term. These groups will be non-negotiable.
Students will undertake an 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 literature.
Each group will be afforded 20-50 minutes to present, and must submit a written report of approximately 1500-2000 words with a detailed reference list. A copy of the PowerPoint 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 and ethics and/or area of professional practices.
- Demonstrate the ability to apply knowledge and principles of data ethics and other methods applicable to the case presented.
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 (6 Oct 2022) 9:30 pm AEST
This is a group task but only 1 person representing each group submits the assignment
Exam Week Friday (21 Oct 2022)
Feedback will be provided at the time of the presentation and on the written executive report via Moodle.
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.
- Provide a clear, thorough and holistic analysis of data and ethics.
- Provide a set of options or decision 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 1500-2000 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 10-16 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 of the term, a case study will be distributed to all students but without any questions. You will have approximately a week 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 data and 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 but please cite appropriately according to the academic standard.
However, any submission forwarded after the scheduled submission due date will not be accepted. Please kindly be advised.
Exam Week Friday (21 Oct 2022) 1:00 am AEST
This is an individual assessment task.
Exam Week Friday (21 Oct 2022)
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 data and ethical concepts and theories as an analytical frame to identify ethical issues in the case
- Clear, thorough, and holistic analysis of the issues.
- Provide options in the decision making and recommendations.
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.