Students

PHIL8400 – Rights, Responsibilities, and AI

2024 – Session 2, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Unit Convenor
Dr Regina Fabry
regina.fabry@mq.edu.au
Lecturer
A/Prof Abhaya Nayak
abhaya.nayak@mq.edu.au
Lecturer
Prof Niloufer Selvadurai
niloufer.selvadurai@mq.edu.au
Tutor
Siavosh Sahebi
siavosh.sahebi@mq.edu.au
Credit points Credit points
10
Prerequisites Prerequisites
COMP6400 or COMP2400
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 advanced understanding of the ethical and other socioeconomic implications of AI.
  • ULO3: Demonstrate an advanced understanding of what Responsible AI means, or will mean, in our current as well as future society.
  • ULO4: Effectively communicate your findings to different stakeholders

General Assessment Information

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.

Information about this unit's policy on the use of AI will be made available in the Assessment block in iLearn. Plase check that information and contact the convenor if you have any questions.

 

 

 

Assessment Tasks

Name Weighting Hurdle Due
Essay 40% No 03/11/2024 at 11:55 PM
Participation 15% No Weeks 2 to 11
Media presentation 15% No 09/10/2024 at 11:55 PM
Case Study 30% No 28/08/2024 at 11:55 PM

Essay

Assessment Type 1: Essay
Indicative Time on Task 2: 33 hours
Due: 03/11/2024 at 11:55 PM
Weighting: 40%

 

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 advanced understanding of the ethical and other socioeconomic implications of AI.
  • Demonstrate an advanced understanding of what Responsible AI means, or will mean, in our current as well as future society.

Participation

Assessment Type 1: Participatory task
Indicative Time on Task 2: 10 hours
Due: Weeks 2 to 11
Weighting: 15%

 

Active engagement in class discussions and associated activities

 
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 advanced understanding of the ethical and other socioeconomic implications of AI.
  • Demonstrate an advanced understanding of what Responsible AI means, or will mean, in our current as well as future society.
  • Effectively communicate your findings to different stakeholders

Media presentation

Assessment Type 1: Media presentation
Indicative Time on Task 2: 15 hours
Due: 09/10/2024 at 11:55 PM
Weighting: 15%

 

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 advanced understanding of the ethical and other socioeconomic implications of AI.
  • Demonstrate an advanced understanding of what Responsible AI means, or will mean, in our current as well as future society.
  • Effectively communicate your findings to different stakeholders

Case Study

Assessment Type 1: Case study/analysis
Indicative Time on Task 2: 25 hours
Due: 28/08/2024 at 11:55 PM
Weighting: 30%

 

Case study involving application of theoretical concepts to a practical context

 
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 advanced understanding of the ethical and other socioeconomic implications of AI.
  • Demonstrate an advanced understanding of what Responsible AI means, or will mean, in our current as well as future society.
  • Effectively communicate your findings to different stakeholders

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
  • the Writing Centre 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

Delivery and Resources

Delivery: All lectures are delivered live. The tutorial is held in person.

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

Unit Schedule

W1 – Introduction (Dr Regina Fabry) – 25 July 2024

  • No readings

  • No tutorial

 

W2 – Ethics and Robotics (A/Prof Abhaya Nayak) – 1 August 2024

  • Reading 1: 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

  • Reading 2: Vanderelst, D., & Winfield, A. (2018). An architecture for ethical robots inspired by the simulation theory of cognition. Cognitive Architectures for Artificial Minds, 48, 56–66. https://doi.org/10.1016/j.cogsys.2017.04.002

  • Tutorial 1

 

W3 – Algorithmic Decision Making (A/Prof Abhaya Nayak) – 8 August 2024

  • Reading 1: Gorwa, R., Binns, R., & Katzenbach, C. (2020). Algorithmic content moderation: Technical and political challenges in the automation of platform governance. Big Data & Society, 7(1), 2053951719897945. https://doi.org/10.1177/2053951719897945

  • Reading 2: Lim, H. S., & Taeihagh, A. (2019). Algorithmic decision-making in AVs: Understanding ethical and technical concerns for smart cities. Sustainability, 11(20). https://doi.org/10.3390/su11205791

  • Tutorial 2

 

W4 – AI and Safety (A/Prof Abhaya Nayak) – 15 August 2024

  • Reading 1: Limarga, R., Song, Y., Nayak, A., Rajaratnam, D., & Pagnucco, M. (forthcoming). Formalisation and evaluation of properties for consequentialist machine ethics. Proceedings of the 33rd International Joint Conference in Artificial Intelligence.

  • Reading 2: Burton, S., Habli, I., Lawton, T., McDermid, J., Morgan, P., & Porter, Z. (2020). Mind the gaps: Assuring the safety of autonomous systems from an engineering, ethical, and legal perspective. Artificial Intelligence, 279, 103201. https://doi.org/10.1016/j.artint.2019.103201

  • Tutorial 3

 

W5 – Moral Responsibility of AI Researchers (Dr Qiongkai Xu / Dr Xiaohan Yu) – 22 August 2024

  • Reading 1: Freedman, R., Borg, J. S., Sinnott-Armstrong, W., Dickerson, J. P., & Conitzer, V. (2020). Adapting a kidney exchange algorithm to align with human values. Artificial Intelligence, 283, 103261. https://doi.org/10.1016/j.artint.2020.103261

  • Reading 2: Schaich Borg, J. (2022). The AI field needs translational Ethical AI research. AI Magazine, 43(3), 294–307. https://doi.org/10.1002/aaai.12062

  • Tutorial 4

 

W6 – Ethical/Social AI Frameworks (Dr Regina Fabry) – 29 August 2024

  • 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

  • Tutorial 5

  • Assessment 1 (Case Study)

 

W7 – The Regulation of AI (Prof Niloufer Selvadurai) – 5 September 2024

  • 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

  • Tutorial 6

 

W8 – Power, Politics, and AI (Dr Regina Fabry) – 12 September 2024

  • Reading 1: Lazar, S. (2022). Power and AI: Nature and justification. In J. B. Bullock, Y.-C. Chen, J. Himmelreich, V. M. Hudson, A. Korinek, M. M. Young, & B. Zhang (Eds.), The Oxford Handbook of AI Governance (p. 0). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780197579329.013.12

  • Reading 2: Campolo, A., & Crawford, K. (2020). Enchanted determinism: Power without responsibility in artificial intelligence. Engaging Science, Technology, and Society, 6, 1–19. https://doi.org/10.17351/ests2020.277

  • Tutorial 7

 

W9 – What Is AI After All? The Turing Test Revisited (Dr Regina Fabry) – 3 October 2024

 

W10 – Explainable AI (Dr Regina Fabry) – 10 October 2024

 

W11 – Equitable AI (Dr Regina Fabry) – 17 October 2024

  • Reading 1: Cossette-Lefebvre, H., & Maclure, J. (2022). AI’s fairness problem: Understanding wrongful discrimination in the context of automated decision-making. AI and Ethics. 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

  • Tutorial 10

 

W12 – Trustworthy AI? The Case of Chatbots (Dr Regina Fabry) – 24 October 2023

  • 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

  • No Tutorial

 

W13 – Writing and Review

  • No Readings

  • No Lecture

  • No Tutorial

  • Assessment 3 (Essay)

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Unit information based on version 2024.04 of the Handbook