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Course profile

Responsible AI (BSAN7210)

Study period
Sem 1 2025
Location
External
Attendance mode
Online

Course overview

Study period
Semester 1, 2025 (24/02/2025 - 21/06/2025)
Study level
Postgraduate Coursework
Location
External
Attendance mode
Online
Units
2
Administrative campus
St Lucia
Coordinating unit
Business School

Gathering, understanding, interpreting and making decisions based on collected data is an invaluable tool for business. However, concerns about privacy, consent, confidentiality, discrimination, ownership, commercialisation, intellectual property and the importance of fair benefit sharing need to be considered. Consideration is also required by those who access and make decisions about collected linked personal information. In this course students will critically analyse the ethical and legal foundations of data analytics governance that are relevant to data collection, storage, integration, exchange and access. Issues covered will include the ethical dimensions of data management, legal and regulatory frameworks in Australia and in relevant jurisdictions, data policy, data privacy, data ownership, legal liabilities regarding analytical decisions, and discrimination. The course will equip students to apply ethical and legal considerations to the core processes of business analytics.

Artificial intelligence has provided social and economic benefits to society as a whole by accelerating innovation for economic prosperity. But the accomplishments of artificial intelligence is fraught with varying ethical, legal, and societal issues that need to be considered when developing and deploying an algorithm to be used in business decision-making. This course consists ofᅠfour modules. You will be using anᅠethical, legal, and socialᅠframework to consider the implications ofᅠartificial intelligence and similar automated decision-systems.

  • Module 1 coversᅠthe basics of the ethical, legal, and social implications framework, introducing you to some of the holistic framework elements and underlyingᅠpremises.
  • Module 2 examinesᅠthe ethical, legal, and social componentsᅠindividually in greaterᅠdetail.
  • Module 3ᅠapplies the ethical, legal, and social implicationsᅠframework to identify key opportunities and gaps with weekly case studies to develop recommendations to mediate issues presented in the case. Sub-themes in this module include surveillance, fairness, bias, safety, misinformation, and well-being.
  • Module 4 wraps up the course with a course summary and Q&A session which is part of the final assessment.

Throughout the course there will be live sessions and discussion boards so that you can engage in robust discussions building your ability to use the framework and develop strong informed arguments.

Course requirements

Recommended prerequisites

We recommend completing the following courses before enrolling in this one:

BSAN7205

Restrictions

Restricted to students enrolled in the MBusAn program

Course contact

Course staff

Lecturer

Timetable

The timetable for this course is available on the UQ Public Timetable.

Additional timetable information

Please note: Teaching staff do not have access to the timetabling system to help with class allocation. Therefore, should you need help with your timetable and/or allocation of classes, please email business.mytimetable@uq.edu.au from your UQ student email account with the following details:

  • Full Name 
  • Student ID 
  • Course Code 

Aims and outcomes

This course aims to further students ability to make decisions related to the ethical, legal and social dimensions of artificial intelligence systems. In this course, the ELSI Framework is extensively used along with critically analysing issues in the light of the law, and ethical and social research related to AI. Students will participate in robust discussions to enable them to take into account diverse stakeholder perspectives and improve argument presentation. This should serve students well so that they can identify the issues, evaluate and propose solutions in their careers.

Learning outcomes

After successfully completing this course you should be able to:

LO1.

Identify and reflect on key ethical, legal and social aspects related to the AI life cycle from design to deployment.

LO2.

Research and critically analyse the opportunities and risks that AI presents in various scenarios and for diverse stakeholders.

LO3.

Propose and evaluate organisational and technological solutions in collaboration with others that respond to ethical, legal and social implications of the responsible use of AI in business and society.

Assessment

Assessment summary

Category Assessment task Weight Due date
Essay/ Critique Individual Reflective Essay 45%

31/03/2025 3:00 pm

Essay/ Critique, Presentation Final Group Presentation and Individual Reflection
  • Team or group-based
55% (35% Final Group Presentation; 20% Individual Reflection)

Presentation Content and Individual Reflection 26/05/2025 3:00 pm

Peer Assessment 30/05/2025 3:00 pm

Both the Presentation Content and Individual Reflection are due by the due date.

Assessment details

Individual Reflective Essay

Mode
Written
Category
Essay/ Critique
Weight
45%
Due date

31/03/2025 3:00 pm

Learning outcomes
L01

Task description

Goal: The goal for this writing task is to apply the ethical, legal and social implications (ELSI) framework covered in Modules 1 and 2 to create a short essay responding to a series of reflective questions.

Tasks: Choose any AI technology or AI innovation or AI system. You will be provided with three short prompts for reflection, with respect to artificial intelligence, and asked to consider and respond to prompts relating to all material in Modules 1 and 2, including questions relating to:

  1. What are key ethics dimensions related to ethics in the development and deployment of your chosen AI technology, innovation or system. (Module 2.1)
  2. What legal and regulatory issues are associated with your technology innovation or system? Relate your answer to AI systems throughout their lifecycle (Module 2.2)
  3. What are the broader societal implications of your chosen AI technology if it were deployed at scale? Include the impacts of your chosen technology on various stakeholders, including vulnerable groups and stakeholders from diverse walks of life. (Module 2.3). 

Based on this you will develop a three-part individual essay. Allocate equal effort to each area.

The essay should be between 1,500 – 2,300 words that responds to each of the Ethical, Legal, and Social Implications prompts (i.e. three essays of 500 – 800 words each). The individual reflective essay will need to be submitted via Blackboard.

Essay

You will want to address the following in your Individual Reflective Essay:

  • A critical analysis responding to each of the prompting questions, applying the Ethical, Legal, and Social Implications framework.
  • At least three supporting references for each of the three sections of the essay.

Criteria & Marking for Individual Reflective Essay:

The focus of the individual reflective essay should be on demonstrating an ability to critically analyse and reflect upon the prompting questions and identifying, describing and applying relevant ethical, legal and social issues.

Individual reflective essay Assessment Criteria

Your individual reflective essay will be assessed according to the following criteria:

  1. Depth and breadth of knowledge/content of the distinct aspects of the Ethical, Legal, and Social Implications of Responsible AI
  2. Critical Judgement: Critical analysis and problem solving; including evaluation of distinct aspects of the Ethical, Legal, and Social Implications framework
  3. Ethical, Legal and Social Understanding: An ability to discuss Responsible AI as a ethical, legal or social undertaking
  4. Communication: The ability to select and use the appropriate level, style and means of communication

To achieve a grade of 7 (85-100%) you will have completed a very persuasive, well-written, and thoughtful essay that clarifies and explains your responses to the prompting questions with a very clear and effective structure, with a sufficient amount of relevant supporting materials that are appropriately referenced. You will have utilised supplied and original materials in an excellent discussion that complies precisely with the stated word limit.

To achieve a grade of 6 (75-84%) you will have completed a persuasive and well-written essay that clarifies and explains your responses to the prompting questions with an effective structure, with a sufficient amount of relevant supporting materials that are appropriately referenced. You will have utilised supplied and original materials in a very good discussion that complies with the stated word limit.

To achieve a grade of 5 (65-74%) you will have completed a well-written essay that clarifies and explains your responses to the prompting questions with an adequate structure, with a sufficient amount of relevant supporting materials that are appropriately referenced. You will have utilised supplied and original materials in a solid discussion within the stated word limit.

To achieve a grade of 4 (50-64%) you will have presented an adequate talk that addresses aspects of your topic but with flaws in approach, structure and/or delivery, and/or with an insufficient amount and/or inappropriately referenced supporting materials. There may have been limitations to your use of supporting materials, and/or difficulties in meeting set word limit constraints.

Grades of 1-3 (less than 50%) will be awarded to essays that are not adequately prepared or written, that do not clarify or address aspects of your responses to the prompting questions with an adequate structure and/or lack an effective structure. Major deficiencies are also present in utilising supporting materials and/or meeting word requirements.

AI Statement:

This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.

Submission guidelines

Submission is via the Turnitin submission point on Blackboard.

Deferral or extension

You may be able to apply for an extension.

Late submission

A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.

Final Group Presentation and Individual Reflection

  • Team or group-based
Mode
Activity/ Performance, Oral, Product/ Artefact/ Multimedia, Written
Category
Essay/ Critique, Presentation
Weight
55% (35% Final Group Presentation; 20% Individual Reflection)
Due date

Presentation Content and Individual Reflection 26/05/2025 3:00 pm

Peer Assessment 30/05/2025 3:00 pm

Both the Presentation Content and Individual Reflection are due by the due date.

Other conditions
Student specific, Peer assessment factor.

See the conditions definitions

Learning outcomes
L01, L02, L03

Task description

Goal: The goal for this task is to work with a group of your peers to apply the Ethical, Legal, and Social Implications framework to a case study of your own choosing (with the approval of the course coordinator). You will then present your analysis in two forms: 

  1. Via a recorded group presentation lasting ten minutes; and
  2. An individual written reflection presented as an Executive Summary tailored to specific issues and for a targeted stakeholder audience.

Tasks: You will be randomly allocated to groups via Blackboard by Week 5. You will need to contact other group members and coordinate with one another to complete the assessment. 

Select a case study for analysis: Each group will select a real-life scenario relevant to the field of business analytics where the responsible use of AI is up for debate. You will treat this scenario as a case study to which you will apply the Ethical, Legal, and Social Implications framework to identify issues and problems, as well as potential solutions.

Based on this you will develop the two outputs described above:

  1. Group Presentation: A recorded group presentation of your selected case study. The presentation should be no more than 15 minutes in length and be uploaded to Blackboard submission area Monday of Week 13. Each group member should have a role in the development and/or delivery of the presentation. You will be expected to be ready to discuss your presentations in a Q&A session in the Week 13 Seminar Live Session.
  2. Individual Reflection: You will submit an individually written Reflection of (length, description) and submit this on Monday of Week 13 via Blackboard.

Presentations

Presentation Content: The presentation should comprise a brief introduction to the scenario, the identification of key ethical, legal and social concerns, and a conclusion. You will be expected to produce slides for the presentation.

Peer-evaluation of presentation: You will be provided with a form that allows you to describe your contribution to the group presentation, as well as evaluate the contributions of your colleagues. This needs to be submitted in Week 13, via Blackboard.

Individual Reflection

Each individual team member will be asked to write their own Reflection on the case selected. The Reflection should be a maximum of 3 pages, single-spaced, and in 12-point Time New Roman font. References should be included. Please use APA7 referencing style. You may want to include appendices supporting your summary and these will not be included in the page count.

Given space constraints you will most probably need to select a sub-topic or narrow your analysis in the Reflection, and this should be made explicit. You will want to address the following in your Reflection:

  • A brief description of the case relevant for the focus or angle you will be analysing.
  • A critical analysis of the topic and application of the Ethical, Legal, Social Implications framework.
  • Recommendations for the stakeholder audience you are addressing in your summary. 

Peer Evaluations (BuddyCheck)

All group work requires a peer-evaluation: You will be provided with a link that will allow you to describe your contribution to the group presentation, as well as evaluate the contributions of your colleagues. 

The grade on your team's project determines a substantial portion of your total grade. Your instructor cannot directly observe the relative contributions of each team member. You must therefore submit confidential quantitative peer evaluations of each other member in your team. Evidence is required. For example, minutes of meetings. You are required to assess the contribution of your team mates to this project. You will award them a mark between 1 & 5 for each of 4 criteria as follows: 

1) Organisation/project management 

Allocate a mark to each team member for their role in establishing the process needed to complete the assignment. Consider their approach to setting up meeting times and work submission deadlines as well as their willingness to listen to other team members' opinions. A mark of one indicates little to no contribution, and a mark of five indicates the maximum contribution possible. 

2) Knowledge 

Allocate a mark to each team member for their contribution of knowledge, and skills to the project. Consider how much research and preparation time this person put in, as well as how much impact their contribution had. Did their input demonstrate a serious commitment to the assessment objectives, or a superficial approach? A mark of one indicates little to no contribution, and a mark of five indicates the maximum contribution possible. 

3) Communication 

Allocate a mark to each team member for how well they collaborated and communicated with others in the team and fulfilled their individual roles within the team. To determine your mark consider if each team member: -was present at all meetings, as long as feasibly possible (either in person, or via phone/online); -played an active role in facilitating group agreement and resolving conflict and; -provided timely responses to all digital communications and queries. A mark of one indicates little to no contribution, and a mark of five indicates the maximum contribution possible. 

4) Responsibility 

Allocate a mark to each team member for how well they took responsibility for the teams outcome, and consistently acted in a manner that demonstrated they were keen for the whole group to perform well. Consider whether this person kept to the agreed deadlines for work. A mark of one indicates little to no contribution, and a mark of five indicates the maximum contribution possible. 

A mark of 1 indicates little to no contribution in this area, and a mark of 5 indicates the maximum contribution possible. Provide a brief justification for point assignments, e.g., 5 points: e.g. Student A attended the team meetings and/or discussion boards and their valuable contributions were high quality. 

Oral Presentation Assessment Criteria 

Your oral presentation will be assessed according to the following criteria: 

  1. Content and argument of the topic of the presentation. 
  2. Engagement with academic sources and evidence.  
  3. Compliance with the stated time limit. 
  4. Evidence of preparation including PowerPoint if required.  
  5. Fluency, ease and persuasiveness of the presentation. 

AI Statement:

This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.

Submission guidelines

Submission information will be posted on Blackboard.

Deferral or extension

You may be able to apply for an extension.

Late submission

A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.

Course grading

Full criteria for each grade is available in the Assessment Procedure.

Grade Cut off Percent Description
1 (Low Fail) 0 - 29

Absence of evidence of achievement of course learning outcomes.

2 (Fail) 30 - 46

Minimal evidence of achievement of course learning outcomes.

3 (Marginal Fail) 47 - 49

Demonstrated evidence of developing achievement of course learning outcomes

4 (Pass) 50 - 64

Demonstrated evidence of functional achievement of course learning outcomes.

5 (Credit) 65 - 74

Demonstrated evidence of proficient achievement of course learning outcomes.

6 (Distinction) 75 - 84

Demonstrated evidence of advanced achievement of course learning outcomes.

7 (High Distinction) 85 - 100

Demonstrated evidence of exceptional achievement of course learning outcomes.

Additional course grading information

Grades will be allocated according to University-wide standards of criterion-based assessment.

Supplementary assessment

Supplementary assessment is available for this course.

Learning resources

You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.

Library resources

Find the required and recommended resources for this course on the UQ Library website.

Learning activities

The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.

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Learning period Activity type Topic
Week 1
Seminar

Introduction to Responsible AI

In Module 1, you will be introduced to the ELSI Framework, which stands for the ethical, legal, and social implications for artificial intelligence.

In the Seminar we will have welcome and course introduction.

Learning outcomes: L01

Week 2
Seminar

Ethics and AI

In Module 2.1, you will examine the key philosophy and ethical dimensions of AI, and focusing on business analytics.

In our Seminar we will be Aapplying the ELSI framework: Ethics and AI. Please prepare prior to attendance.

Learning outcomes: L01

Week 3
Seminar

Law and AI

In Module 2.2, you will develop your theoretical understanding of the relationship between AI and law, different models for explaining legal, technological, and social change, and a practical understanding of different areas of law and how these might impact on your future work.

In the Seminar we will be applying the framework focusing on the law. Please prepare prior to attendance.

Learning outcomes: L01

Week 4
Seminar

Society and AI

In Module 2.3, you will examine the broader societal implications that are important to consider with AI and automated decision-making systems.

In the Seminar we will be applying the framework focusing on the social and societal implications of AI systems. Please prepare prior to attendance.

Learning outcomes: L01

Week 5
Seminar

Privacy, Consent & Surveillance - Part 1

Consent and privacy challenges are important considerations with AI systems and data. As we begin Module 3.1, we will cover issues associated with Privacy, Consent and Surveillance as these arise in the artificial intelligence landscape.

We will explore these issues through two different case studies.

The Seminar provides a discussion on the (fictional) case of 'Optimizing Schools' (Princeton Case Study) and the privacy, surveillance and consent issues raised when a high school uses student data to improve student retention rates. Please prepare prior to attendance.

Learning outcomes: L02, L03

Week 6
Seminar

Privacy, Consent & Surveillance 2, w/Team Session

In the second part of Module 3.1, we will cover additional issues associated with Privacy, Consent and Surveillance as these arise in the artificial intelligence landscape. In Week 6 teams gather to select a topic for the Final Group Presentations (Assessment #2, due in Week 13). Students team up in groups of 3 - 4 (or other amount to be determined by instructor depending on course enrollment). Teams will select an organisational case relating to responsible AI and prepare a PPT presentation as a video recording on their findings. A set of guiding questions will be provided in week 5.

Learning outcomes: L02, L03

Week 7
Seminar

Diversity, Inclusiveness, and Fairness

In Module 3.2, you will examine how the concepts of fairness, inclusivity, and non-discrimination relate to AI.

We will explore these issues through two different case studies.

This week's Seminar provides a discussion on the (fictional) case of 'Hiring by Machine', which raises relevant real-world questions about fairness, equity, unfair discrimination and others. Please prepare prior to attendance.

Learning outcomes: L02, L03

Week 8
Seminar

Trustworthy systems

This week, in Module 3.3, we will cover Transparency, Contestability, Explainability, Understandability, and Safety in the artificial intelligence landscape. We will explore these issues through two different case studies.

The Seminar provides a discussion on the (fictional) case of 'Cogito Ergo Sum', which raises issues relating to disclosure when a person is interacting with an AI system. Please prepare prior to attendance.

Learning outcomes: L02, L03

Mid-sem break
No student involvement (Breaks, information)

In-semester break

No classes

Week 9
Seminar

Human, Societal & Environmental Well-Being

This week, in Module 3.4A, we will cover Human, Societal and Environmental Well-Being in the artificial intelligence landscape. We will explore these issues through two different case studies.

This Seminar provides a discussion on the (fictional) case of 'Healthcare App', which describes the development of an app which uses AI to make diabetic care easier and more accessible, but raises issues about the equity of benefits and utility. Please prepare prior to attendance.

Learning outcomes: L02, L03

Week 10
Seminar

Assessment #2 Working Week

Assessment #2 Working Week

Learning outcomes: L01, L02, L03

Week 11
Seminar

Generative AI

This week, in Module 3.5, we will cover Generative AI in the artificial intelligence landscape. We will explore these issues through two different case studies.

The Seminar provides a discussion on a case to be provided in Week 12 relating to generative and their potential ethical, legal and societal implications. Please prepare prior to attendance.

Learning outcomes: L02, L03

Week 12
Seminar

AI Misinformation

In Module 3.5 we will cover Misinformation in the artificial intelligence landscape. We will explore these issues through two different case studies.

This session provides a discussion on the (fictional) case relating to Deep Fakes and their potential ethical, legal and societal implications. Please prepare prior to attendance.

Learning outcomes: L02, L03

Week 13
Seminar

Summary

In this concluding module [Module 4], we will summarize and reflect on the course and you will complete the final assessment.

Q&A Session. Please see details on how this will be run on Blackboard.

Learning outcomes: L01, L02, L03

Policies and procedures

University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:

Learn more about UQ policies on my.UQ and the Policy and Procedure Library.