Outline

Overview

The rapid evolution and widespread adoption of digital technologies are transforming societies, economies, and the environment. These transformations bring significant benefits but also pose serious risks of harm, particularly for vulnerable and marginalised stakeholders, requiring a balanced approach that genuinely serves the common good. This unit unpacks the ethical dilemmas of sociotechnical change, exploring how digital technologies like artificial intelligence, blockchain, and social media impact privacy, equity, democracy, dignity, business, the environment, and more. Guided by compassion as a foundational principle, students will critically examine the ambiguous and pluralistic nature of digital innovations, developing the skills to manage ethical dilemmas and promote responsible digital innovation for the common good. The course integrates ethical theories with recent practical case studies, equipping students to address contemporary ethical dilemmas in today's digital world.

Unit details and rules

Academic unit	Business Information Systems
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	INFS1000 or INFS1020 or INFO1000 or INFO1003 or INFO1903
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Raffaele Ciriello, raffaele.ciriello@sydney.edu.au

Assessment

The census date for this unit availability is 31 August 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written work	GenAI Strategy Plan and critically reflect on using GenAI and how it affects learning.	5%	Week 03 Due date: 19 Aug 2026 at 23:59 Closing date: 13 Sep 2026	600 words	AI allowed
Outcomes assessed:
Written work	Individual assignment Written report	30%	Week 06 Due date: 09 Sep 2026 at 23:59 Closing date: 30 Sep 2026	1500 words	AI allowed
Outcomes assessed:
Interactive oral	Group Assignment / Business Presentation Slides and Oral presentation	5%	Week 12 Due date: 30 Oct 2026 at 23:59 Closing date: 30 Nov 2026	3 slides / 5 minutes	AI limited - refer to Canvas
Outcomes assessed:
Portfolio or journal	Reflective Journal Individual reflective learning journal, stimulated by prompts.	30%	Week 13 Due date: 06 Nov 2026 at 23:59 Closing date: 30 Nov 2026	1500 words	AI allowed
Outcomes assessed:
Written work	Group Assignment / Business Report Written report	30%	Week 13 Due date: 06 Nov 2026 at 23:59 Closing date: 30 Nov 2026	3000 words	AI allowed
Outcomes assessed:
= group assignment ? = early feedback task ?

Early feedback task

This unit includes an early feedback task, designed to give you feedback prior to the census date for this unit. Details are provided in the Canvas site and your result will be recorded in your Marks page. It is important that you actively engage with this task so that the University can support you to be successful in this unit.

Assessment summary

Individual assignment: This assignment aims to assist students’ development as an independent learner of the knowledge and skills acquired in this unit. Students are to select, analyse and submit a brief business report on a news item for a specific company relevant to this unit.
Group project report: The group project is an applied project aimed at assessing the knowledge acquired in this unit of study. Students will be required to demonstrate their critical thinking and analytical skills to analyse and recommend improvements to real-world businesses.
Group project presentation: The group project presentations are aimed at assessing the level of knowledge acquired in this unit of study. All group members must actively participate in the presentation.
Reflective journal: Individual continuous, reflective journal about the student’s authentic learning experience gleaned from the group project and the in-class activities. While the submission is due in W13, students are encouraged to reflect on weekly prompts by the teaching team, and can discuss their reflections in the weekly consultations. The final deliverable is a written reflective journal of 1500 words (± 10%).
Generative AI Strategy: By developing a clear strategy for using GenAI tools in assignment work, students will enhance their ability to leverage GenAI for tasks such as identifying literature, structuring ideas, refining text, and fact-checking while ensuring ethical and effective practices. The goal is not just to ensure academic integrity but also to reflect on how GenAI tools shape analytical and critical thinking skills. The deliverable is a 600 word (± 10%) document.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100	Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Distinction	75 - 84	Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Credit	65 - 74	Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Pass	50 - 64	Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Late penalties will apply in accordance with the Business School policy (unless formal Special Consideration is granted).

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Unit introduction	Lecture (1.5 hr)
Week 01	Warm-Up & Individual Assignment Prep	Tutorial (1.5 hr)
Week 02	The Big Picture: Compassionate Digital Innovation	Lecture (1.5 hr)
Week 02	Selecting Evidence	Tutorial (1.5 hr)
Week 03	Digital Harm Analysis	Lecture (1.5 hr)
Week 03	Analysing Evidence	Tutorial (1.5 hr)
Week 04	Ambivalent Affordances of Digital Technologies	Lecture (1.5 hr)
Week 04	Privacy vs Security	Tutorial (1.5 hr)
Week 05	Ethical Paradoxes of Sociotechnical Change	Lecture (1.5 hr)
Week 05	Group Assignment Prep	Tutorial (1.5 hr)
Week 06	Compassion: A Timely and Timeless Moral Guide	Lecture (1.5 hr)
Week 06	Bust Your Own Bias	Tutorial (1.5 hr)
Week 07	Case Study: Responsible Social Media Use	Lecture (1.5 hr)
Week 07	Designing Responsibility Nudges	Tutorial (1.5 hr)
Week 08	Case Study: Compassionate Web3 Governance	Lecture (1.5 hr)
Week 08	Presenting Evidence	Tutorial (1.5 hr)
Week 09	Case Study: Human-AI Companionship	Lecture (1.5 hr)
Week 09	Sensitive Issues in Sociotechnical Systems	Tutorial (1.5 hr)
Week 10	Developing Employability	Lecture (1.5 hr)
Week 10	Developing Your Employability	Tutorial (1.5 hr)
Week 11	Open Topic / Guest Lecture	Lecture (1.5 hr)
Week 11	Reflection on common issues in assignments	Tutorial (1.5 hr)
Week 12	Unit Review and Debrief	Lecture (1.5 hr)
Week 12	Group Project Presentations	Tutorial (1.5 hr)
Week 13	Open Topic / Guest Lecture	Lecture (1.5 hr)
Week 13	Reflection Workshop	Tutorial (1.5 hr)

Attendance and class requirements

Lecture recordings: All lectures are recorded and will be available on Canvas for student use. Please note the Business School does not own the system and cannot guarantee that the system will operate or that every class will be recorded. Students should ensure they attend and participate in all classes.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Analyse the ethical challenges and paradoxes associated with digital innovations.
LO2. Critically evaluate the societal, economic, and environmental impacts of digital technologies, considering both benefits and harms.
LO3. Apply ethical frameworks and theories to navigate and resolve ethical dilemmas in digital innovation.
LO4. Communicate and collaborate effectively on ethical issues in diverse professional contexts.
LO5. Make informed decisions that balance technological progress with societal well-being and ethical integrity.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

Sessional changes have been made based on the received student feedback. Assignment handouts have been updated to provide clearer and more comprehensive guidance. Marking rubrics have been updated. Final exam has been replaced with an individual, continuous, reflective journal to achieve higher pedagogical effectiveness in line with authentic assessment principles. Participation mark has been replaced with an early-feedback task (GenAI strategy)

Additional information

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

INFS2010: Digital Ethics and Compassionate Innovation

Semester 2, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links