Students

PHIX3400 – Rights, Responsibilities, and AI

2026 – Session 2, Online-flexible

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Unit Convenor
Prof Paul Formosa
paul.formosa@mq.edu.au
Lecturer
Dr Regina Fabry
regina.fabry@mq.edu.au
Lecturer
Prof Niloufer Selvadurai
niloufer.selvadurai@mq.edu.au
Lecturer & Tutor
Dr Ines Hipolito
ines.hipolito@mq.edu.au
Credit points Credit points
10
Prerequisites Prerequisites
130cp at 1000 level or above
Corequisites Corequisites
Co-badged status Co-badged status
Unit description Unit description

With increasing entrenchment of AI in human affairs, its scientific, moral, political, economic, and other social aspects are becoming a significant issue. For instance, there is a significant concern that machine learning algorithms contribute to the discrimination against members of oppressed groups (e.g., women, people of colour). This unit, co-designed and co-taught by relevant experts in Computing, Philosophy, and cognate disciplines, will present and discuss key theoretical, ethical, and empirical questions about the conditions of explainable, safe, fair, and responsible AI. Furthermore, it will explore scientific, ethical,  political, economic, and other social implications of topical issues such as algorithmic decision making, applications of deep learning models, and robot rights. Students will be exposed to ideas such as balancing risks and responsibilities, both in the scientific and moral sense, in the context of the evolving AI technologies.

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates

Learning Outcomes

On successful completion of this unit, you will be able to:

  • ULO1: Explain the fundamental principles underlying AI, and the normative constraints that it needs to satisfy.
  • ULO2: Demonstrate an understanding of the ethical and other socioeconomic implications of AI.
  • ULO3: Demonstrate an understanding of what Responsible AI means, or will mean, in our current as well future society.
  • ULO4: Critically reflect on the use of AI in relevant fields.

General Assessment Information

Late Penalty

Unless a Special Consideration request has been submitted and approved, a 5% penalty (of the total possible mark) will be applied each day a written assessment is not submitted, up until the 7th day (including weekends). After the 7th day, a mark of ‘0’ (zero) will be awarded even if the assessment is submitted. Submission time for all written assessments is set at 11.55pm. A 1-hour grace period is provided to students who experience a technical issue. This late penalty will apply to written reports and recordings only. Late submission of time sensitive tasks (such as tests/exams, performance assessments/presentations, scheduled practical assessments/labs) will be addressed by the unit convenor in a Special consideration application.

 

Generative AI (ChatGPT etc)
In this unit, unless notified otherwise in writing by the Unit Convenor, it is expected that the work you submit demonstrates your own understanding, analysis, research, reflection, critical thinking, and writing, and you will be marked on the basis of your demonstrated understanding of and engagement with the unit content and activities. This applies to all assessments, including online forums. If your teachers have concerns about whether a submission is your own work in a meaningful sense, you may be required to attend an interview with the Unit Convenor or other academic staff to discuss your work and demonstrate your attainment of unit and task learning outcomes.  Further information and advice about policies and expectations around the use of Generative AI will be given in iLearn. 
 
Special Considerations
For details around special consideration, including applying for short extensions, see https://students.mq.edu.au/study/assessment-exams/special-consideration 

Assessment Tasks

Name Weighting Hurdle Due Groupwork/Individual Short Extension AI Approach
Media presentation 20% No 11/10/2026 Individual Yes Open
Reflective task 35% No 30/08/2026 Individual Yes Open
Research essay 45% No 08/11/2026 Individual Yes Open

Media presentation

Assessment Type 1: Presentation task
Indicative Time on Task 2: 18 hours
Due: 11/10/2026
Weighting: 20%
Groupwork/Individual: Individual
Short extension 3: Yes
AI Approach: Open

Media presentation
On successful completion you will be able to:
  • Explain the fundamental principles underlying AI, and the normative constraints that it needs to satisfy.
  • Demonstrate an understanding of the ethical and other socioeconomic implications of AI.
  • Demonstrate an understanding of what Responsible AI means, or will mean, in our current as well future society.

Reflective task

Assessment Type 1: Written Submission
Indicative Time on Task 2: 30 hours
Due: 30/08/2026
Weighting: 35%
Groupwork/Individual: Individual
Short extension 3: Yes
AI Approach: Open

Present arguments and defend your own view on a topic from the unit.
On successful completion you will be able to:
  • Explain the fundamental principles underlying AI, and the normative constraints that it needs to satisfy.
  • Demonstrate an understanding of the ethical and other socioeconomic implications of AI.
  • Demonstrate an understanding of what Responsible AI means, or will mean, in our current as well future society.
  • Critically reflect on the use of AI in relevant fields.

Research essay

Assessment Type 1: Written Submission
Indicative Time on Task 2: 35 hours
Due: 08/11/2026
Weighting: 45%
Groupwork/Individual: Individual
Short extension 3: Yes
AI Approach: Open

Research essay on a topic from the unit
On successful completion you will be able to:
  • Explain the fundamental principles underlying AI, and the normative constraints that it needs to satisfy.
  • Demonstrate an understanding of the ethical and other socioeconomic implications of AI.
  • Demonstrate an understanding of what Responsible AI means, or will mean, in our current as well future society.
  • Critically reflect on the use of AI in relevant fields.

1 If you need help with your assignment, please contact:

  • the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
  • Academic Success for academic skills support.

2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation.

3 An automatic short extension is available for some assessments. Apply through the Service Connect Portal.

Delivery and Resources

Delivery: All lectures are delivered live; echo recordings are available via iLearn. Online forums are available in iLearn.

Resources: All required readings are provided in Leganto. You must read the required readings before class.

Unit Schedule

Unit Schedule

W1 – Introduction: Rights, Responsibilities, and AI (Prof Paul Formosa) – 28 July 2026

•     No readings

•     In person tutorial 1 OR Online forum 1

W2 – Ethical AI Frameworks (Prof Paul Formosa) – 4 August 2026

•     Reading 1: Hagendorff, T. (2020). The ethics of AI ethics: An evaluation of guidelines. Minds and Machines, 30(1), 99–120. https://doi.org/10.1007/s11023-020-09517-8

•     Reading 2: Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5

•     In person tutorial 2 OR Online forum 2

W3 – Robot Rights & Moral Status (Prof Paul Formosa) – 11 August 2026

•     Reading 1: Schwitzgebel, E., & Garza, M. (2015). A defense of the rights of artificial intelligences. Midwest Studies in Philosophy, 39(1), 98–119. https://doi.org/10.1111/misp.12032

•     Reading 2: Birhane, A., & van Dijk, J. (2020). Robot rights? Let’s talk about human welfare instead. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 207–213. https://doi.org/10.1145/3375627.3375855

•     In person tutorial 3 OR Online forum 3

W4 – Explainable AI (Dr Regina Fabry) – 18 August 2026

•     Reading 1: Zednik, C. (2021). Solving the black box problem: A normative framework for explainable artificial intelligence. Philosophy & Technology, 34(2), 265–288. https://doi.org/10.1007/s13347-019-00382-7

•     Reading 2: Russo, F., Schliesser, E., & Wagemans, J. (2024). Connecting ethics and epistemology of AI. AI & Society, 39(4), 1585–1603. https://doi.org/10.1007/s00146-022-01617-6

•     In person tutorial 4 OR Online forum 4

W5 – Equitable AI (Dr Regina Fabry) – 25 August 2026

•     Reading 1: Cossette-Lefebvre, H., & Maclure, J. (2023). AI’s fairness problem: Understanding wrongful discrimination in the context of automated decision-making. AI and Ethics, 3(4), 1255–1269. https://doi.org/10.1007/s43681-022-00233-w

•     Reading 2: Kasirzadeh, A. (2022). Algorithmic fairness and structural injustice: Insights from feminist political philosophy. Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, 349–356. https://doi.org/10.1145/3514094.3534188

•     In person tutorial 5 OR Online forum 5

•     Assignment 1 (Reflective task)

W6 – Trustworthy AI: Chatbots (Dr Regina Fabry) – 1 September 2026

•     Reading 1: Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. https://doi.org/10.1145/3442188.3445922

•     Reading 2: Heersmink, R., de Rooij, B., Clavel Vázquez, M. J., & Colombo, M. (2024). A phenomenology and epistemology of large language models: Transparency, trust, and trustworthiness. Ethics and Information Technology, 26(3), 41. https://doi.org/10.1007/s10676-024-09777-3

•     In person tutorial 6 OR Online forum 6

W7 – The Regulation of AI (Prof Niloufer Selvadurai) – 8 September 2026

•     Reading 1: Gacutan, J., & Selvadurai, N. (2020). A statutory right to explanation for decisions generated using artificial intelligence. International Journal of Law and Information Technology, 28(3), 193–216. https://doi.org/10.1093/ijlit/eaaa016

•     Reading 2: Smuha, N. A. (2021). From a ‘race to AI’ to a ‘race to AI regulation’: Regulatory competition for artificial intelligence. Law, Innovation and Technology, 13(1), 57–84. https://doi.org/10.1080/17579961.2021.1898300

•     In person tutorial 7 OR Online forum 7

W8 – AI, Automation & the Future of Work (Prof Paul Formosa) – 15 September 2026

•     Reading 1: Bankins, S., & Formosa, P. (2023). The ethical implications of artificial intelligence (AI) for meaningful work. Journal of Business Ethics, 185(4), 725–740. https://doi.org/10.1007/s10551-023-05339-7

•     Reading 2: Kim, T. W., & Scheller-Wolf, A. (2019). Technological unemployment, meaning in life, purpose of business, and the future of stakeholders. Journal of Business Ethics, 160(2), 319–337. https://doi.org/10.1007/s10551-019-04205-9

•     In person tutorial 8 OR Online forum 8

RECESS FROM 21 SEPTEMBER TO 2 OCTOBER 2026

W9 – Power, Politics & AI: Censorship & Content Moderation (Dr Ines Hipolito) – 6 October 2026

•     Reading 1: Lazar, S. (2022). Power and AI: Nature and justification. In J. B. Bullock et al. (Eds.), The Oxford Handbook of AI Governance. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780197579329.013.12

•     Reading 2: Kashyap, B., & Formosa, P. (2026). From tweets to tyranny: Exploring the symmetry between state and social media censorship of speech. Journal of Social Philosophy. https://doi.org/10.1111/josp.70052

•     In person tutorial 9 OR Online forum 9

•     Assignment 2 (Media presentation)

W10 – Moral Responsibility & the Responsibility Gap (Dr Ines Hipolito) – 13 October 2026

•     Reading 1: Gunkel, D. J. (2020). Mind the gap: Responsible robotics and the problem of responsibility. Ethics and Information Technology, 22(4), 307–320. https://doi.org/10.1007/s10676-017-9428-2

•     Reading 2: Coeckelbergh, M. (2020). Artificial intelligence, responsibility attribution, and a relational justification of explainability. Science and Engineering Ethics, 26(4), 2051–2068. https://doi.org/10.1007/s11948-019-00146-8

•     In person tutorial 10 OR Online forum 10

W11 – The Singularity & AI Safety (Dr Ines Hipolito) – 20 October 2026

•     Reading 1: Chalmers, D. J. (2010). The singularity: A philosophical analysis. Journal of Consciousness Studies, 17(9–10), 7–65. [Pages 7–40 ONLY]

•     Reading 2: Millière, R. (2025). Normative conflicts and shallow AI alignment. Philosophical Studies, 182(7), 2035–2078. https://doi.org/10.1007/s11098-025-02347-3 [Pages 2035–2062 ONLY]

•     In person tutorial 11 OR Online forum 11

W12 – What Is AI? Intelligence & the Turing Test (Dr Ines Hipolito) – 27 October 2026

•     Reading 1: Proudfoot, D. (2013). Rethinking Turing’s test. The Journal of Philosophy, 110(7), 391–411. https://doi.org/10.5840/jphil2013110722

•     Reading 2: Wheeler, M. (2020). Deceptive appearances: The Turing test, response-dependence, and intelligence as an emotional concept. Minds and Machines, 30(4), 513–532. https://doi.org/10.1007/s11023-020-09533-8

•     In person tutorial 12 OR Online forum 12

W13 – AI, Virtue & Human Flourishing (Dr Ines Hipolito) – 3 November 2026

•     Reading 1: Vallor, S. (2015). Moral deskilling and upskilling in a new machine age: Reflections on the ambiguous future of character. Philosophy & Technology, 28(1), 107–124. https://doi.org/10.1007/s13347-014-0156-9

•     Reading 2: Smith, N., & Vickers, D. (2024). Living well with AI: Virtue, education, and artificial intelligence. Theory and Research in Education, 22(1), 19–44. https://doi.org/10.1177/14778785241231561

•     Assignment 3 (Research essay)

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Unit information based on version 2026.05 of the Handbook