Dr Michael Vincent
Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- Study level
- Postgraduate Coursework
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Historical & Philosophical Inq
Gathering, understanding, interpreting and making decisions based on collected data is an invaluable tool for science, business and governments. Concerns about privacy, consent, confidentiality, discrimination, ownership, commercialisation, intellectual property and the importance of fair benefit sharing are known. Being aware of conflicts of interest and the need to ensure equity, reciprocity and respect for cultural diversity are increasingly seen as important. What is less recognised is the nature of the roles of those who access and make decisions about collected linked personal information. The emerging global banked data that has become a key part of contemporary decision-making raises questions about the role of the data scientist. In this course students will critically analyse the ethical and legal foundations of data science governance that are relevant to the technical processes of data collection, storage, 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 identify the ethical and legislative requirements that underpin the technical processes of data science and to apply ethical and legal considerations to the core processes of data analytics. It will also introduce algorithms and technical approaches to minimise the risk of data identifiability and disclosure. A range of case studies will be used to explore these issues in applications of data science, including the use of government administrative data for informing social policy, to integrate ethical, legal and technical considerations.
This course is part of the Master of Data Scienceﾠintroduced by EAIT. This is a cross faculty PGCW initiative. The DATA7002ﾠcourse is one of the four “foundation” courses undertaken by all students enrolled in the program.ﾠ The course aims to provide thoseﾠworking with data sources with an understanding of the ethical and legal frameworks within which they will operate. This courseﾠequips graduates with an understanding of the ethical, legal and technical concerns they will face in their future careers.
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Course requirements
Assumed background
The DATA7002 course is one of the four “foundation” courses undertaken by all students enrolled in the MDataSc program, and is available as an elective to Master of Quantitative Biology students.
Restrictions
Restricted to Master of Data Science and Master of Quantitative Biology students only.
Course staff
Course coordinator
Lecturer
Dr Michael Vincent
Ms Sruthi Balaji
Dr Zhi Chen
Dr Hamish MacDonald
Tutor
Mr Nhat Pham
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
The course aims to provide data scientists working with data sources with an understanding of the ethical and legal frameworks within which they will operate. This course equips graduates with an understanding of the ethics, legal and technical concerns that they will face in their future careers.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Identify the key ethical, legal and technical considerations that are intrinsic to integrity in data science practice.
LO2.
Gain practical skills in predicting, identifying, assessing, evaluating and responding to the ethical conflicts and dilemmas that are likely to occur within data science.
LO3.
Explain the different legal and ethics approaches that underpin the application of data science.
LO4.
Apply relevant Australian and international law and ethics theory to contemporary data science problems.
LO5.
Evaluate the effectiveness of different legal and ethics approaches relevant to the application of data science.
LO6.
Ability to identify relevant ethics and legal aspects of data science and apply appropriate techniques and algorithms for data de-identification to ensure privacy and non-disclosure when required.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Quiz |
Quizzes to be held in Tutorials
|
20% |
2/08/2024 - 4/10/2024 |
| Paper/ Report/ Annotation | Plan for Major Assessment | 10% |
13/09/2024 5:00 pm |
| Presentation | Group Presentation | 30% |
10/10/2024 - 25/10/2024 |
| Essay/ Critique | Final Essay | 40% |
4/11/2024 |
Assessment details
Quizzes to be held in Tutorials
- In-person
- Online
- Mode
- Activity/ Performance
- Category
- Quiz
- Weight
- 20%
- Due date
2/08/2024 - 4/10/2024
- Other conditions
- Time limited.
- Learning outcomes
- L01, L02, L04, L06
Task description
There will be a small quiz in each of the first eight tutorials. It is important that you attend the tutorials and have a device on which to access Blackboard with you.
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) tools will not be permitted. Any attempted use of Generative AI may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Deferral or extension
You may be able to apply for an extension.
Plan for Major Assessment
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 10%
- Due date
13/09/2024 5:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
This is a 'scaffolded writing assignment,' which requires students to complete written sections of a fillable Word document that will be provided on Blackboard. Each section of the fillable Word document will represent a single component of what would traditionally be included in an essay, such as a thesis statement, an introduction, main paragraphs, and a bibliography. Each section will provide instructions concerning how to complete it, including the word limits for responses.
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 will be through a Turnitin portal.
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.
Group Presentation
- Mode
- Activity/ Performance
- Category
- Presentation
- Weight
- 30%
- Due date
10/10/2024 - 25/10/2024
- Learning outcomes
- L01, L02, L03, L04, L05, L06
Task description
Students are allocated to groups. Each group will be required to identify key ethical, legal and technical considerations relating to a real-life data science problem. Each group will choose their own problem. Each group will create a presentation for 15 minutes followed by a 5 minute question time. The presentation should be comprised of a brief introduction to the real-life problem, the identification of key ethical, legal and technical concerns, and a conclusion.
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) tools will not be permitted. Any attempted use of Generative AI may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Each group will present in weeks 11 or 12 (or 13 if necessary) and submit the presentation summary and ppt via a link on the e-learning site.
Deferral or extension
You may be able to apply for an extension.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
It is urgent that you seek an official extension if you will not be able to present at your assigned time.
Final Essay
- Mode
- Written
- Category
- Essay/ Critique
- Weight
- 40%
- Due date
4/11/2024
- Learning outcomes
- L01, L02, L03, L04, L05, L06
Task description
You will be required to identify and apply key ethical, legal and technical issues relating to a real-life data science case study, in dialogue with either another student or a large language model. A template showing how this is to be done will be provided on Blackboard. Normally students will focus on a different topic than that which was covered in the Presentations.
Each student is expected to consider (a) what ethical, legal and technical solutions could have been implemented to ameliorate/stop the problem and (b) reflect on the importance of ethics, law and technical perspectives, in light of what they have learned about the case study/real life problem and their learning on the course. The focus of the analysis should be on demonstrating an ability to identify, describe relevant issues and apply course learnings.
Texts and references should be cited according to the University guidelines. In line with common writing practices in philosophy, law and ethics, students may write in first or third person.
Word Count: Students will be limited to submitting 2000 words each maximum, as well as word limits specified on the task sheet.
This task has been designed to be challenging, authentic and complex. Whilst students may use AI technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance. A failure to reference generative AI use may constitute student misconduct under the Student Code of Conduct. To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI tools.
Submission guidelines
You must submit an electronic copy only through Turnitin. All students must ensure they receive their Turnitin receipt on submission of any assessments. A valid Turnitin receipt will be the only evidence accepted if assessments are missing. In the event of Turnitin technical difficulties, immediately send a copy of your essay to course staff so they can see that it was completed on time.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
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 | Description |
|---|---|
| 1 (Low Fail) |
Absence of evidence of achievement of course learning outcomes. Course grade description: Grade 1, Low Fail (0-24%), is generally awarded in cases where some assessment has been submitted, but it is of wholly unsatisfactory standard or quantity. In work submitted, however, there is no demonstrated evidence of understanding of the concepts of the field of study or basic requirements of the course. |
| 2 (Fail) |
Minimal evidence of achievement of course learning outcomes. Course grade description: Grade 2, Fail (25-44%), is generally awarded to work that exhibits deficiencies in understanding and applying the fundamental concepts of the course and field of study, and as such, does not satisfy the basic requirements of the course. Often, one or more major items of assessment will not have been completed. |
| 3 (Marginal Fail) |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: Grade 3, Marginal Fail (45-49%), is generally awarded if a student has submitted work that attempts to meet the knowledge and skill requirements of the course, but is only able to demonstrate a superficial understanding of the fundamental concepts of the course. Students will usually have attempted all major pieces of assessment and show that they have an identifiable, emerging ability to apply basic knowledge and skills. |
| 4 (Pass) |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: Grade 4, Pass (50-64%), is generally awarded where all major items of assessment have been submitted. An adequate knowledge of the fundamental concepts of the course and field of study should be demonstrated and a functional skill level achieved. |
| 5 (Credit) |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: Grade 5, Credit (65-74%), is generally awarded where all items of assessment have been completed and a substantial understanding of the fundamental concepts of the course and field of study have been demonstrated. |
| 6 (Distinction) |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: Grade 6, Distinction (75-84%), is generally awarded where all items of assessment have been completed and substantial knowledge of the deeper and more complex aspects of the course and field of study have been demonstrated. |
| 7 (High Distinction) |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: Grade 7, High Distinction (85-100%), is generally awarded where all items of assessment have been completed and there is evidence that the deeper and more complex aspects of the course and field of study have been mastered. |
Additional course grading information
A word count that is within ±10% of the set length (word limit) is acceptable. Word count does not include footnotes; however, discursive footnotes are included in the word count. A word count that is outside these 10% will receive a proportionate penalty and will be graded against the grading criteria.
Course Grading Criteria:
1. Essay Assessment Criteria
Your essay will be assessed according to the following criteria:
1. Ability to define the topic or philosophical issue under debate. (Topic definition.)
2. Ability to construct a well-reasoned argument for a certain point of view. (Argument and Structure.)
3. Depth and breadth of understanding of the various positions in a philosophical debate. (Scope and Depth of Treatment)
4. Insight and/or originality in interpreting texts or constructing a point of view or argument. (Originality)
5. Capacity to produce a polished, well-written and appropriately referenced essay. (Presentation)
To achieve a grade of 7 (High Distinction, 85-100%), your essay should reflect an exceptional level of achievement. It should demonstrate that you have undertaken extensive, high-level research, that you are able to form a very rigorous, well-organised argument, and that your discussion is original and creative. It should also demonstrate that you are able to evaluate and organise data and/or evidence in a critical manner and that you have a sophisticated and insightful understanding of problems and issues. Your essay will be very well written, clear and concise, pay strict attention to discipline conventions and have minimal, if any errors in referencing, expression, grammar, spelling and punctuation: This grade is assigned for a total (sum of all the assessment components) in the range 85-100%. See assessment components for specific grading criteria.
To achieve a grade of 6 (Distinction, 75-84%), your essay should reflect an advanced level of achievement. It should demonstrate that you have undertaken wide research, that you are able to form a rigorous, well-organised argument, and that your discussion is coherent and convincing. It should also demonstrate that you are able to evaluate data and/or evidence in a perceptive manner, and that your understanding of problems and issues is perceptive and insightful. Your essay will be well written, clear and concise, follow discipline conventions and have few errors in referencing, expression, grammar, spelling and punctuation: This grade is assigned for a total (sum of all the assessment components) in the range 75-84%. See assessment components for specific grading criteria.
To achieve a grade of 5 (Credit, 65 – 74%) your essay should reflect a proficient level of achievement. It should demonstrate that you have undertaken the expected level of research, that you are able to develop or adapt convincing arguments and justify them adequately, that you are able to evaluate data and/or evidence in a proficient manner, and that you have a good understanding of problems and issues. The presentation and referencing of your essay will largely follow discipline conventions, perhaps have some errors in grammar, spelling and punctuation, and demonstrate your ability to communicate effectively: This grade is assigned for a total (sum of all the assessment components) in the range 65-74%. See assessment components for specific grading criteria.
To achieve a grade of 4 (Pass, 50 – 64%) your essay should reflect functional achievement. It should demonstrate that you are able to apply fundamental concepts and skills, that you have undertaken a basic level of research and have the basic ability to evaluate data and/or evidence, to identify problems and issues, to offer insights and to develop routine arguments. Your organisation, writing, referencing, spelling and grammar will be adequate and use some of the discipline conventions to communicate appropriately: This grade is assigned for a total (sum of all the assessment components) in the range 50-64%. See assessment components for specific grading criteria.
To achieve a grade of 3 (Marginal Fail, 45 – 49%), your essay should reflect developing levels of achievement. It should demonstrate that you have a superficial knowledge of fundamental concepts and skills, that you have undertaken a basic level of research, made some attempt to evaluate data and/or evidence, to identify problems and issues, and to offer insights. Your arguments, while underdeveloped, show your emerging ability to apply knowledge and skills. Your organisation, writing, spelling and grammar will be adequate, perhaps poor, and your referencing and use of discipline conventions poor/and or inconsistent: This grade is assigned for a total (sum of all the assessment components) in the range 45-49%. See assessment components for specific grading criteria.
To achieve a grade of 2 (Fail, 25-44%) your essay will reflect deficiencies in skill acquisition and in your understanding of the fundamental concepts of the course. It will demonstrate that you have not undertaken adequate research, that you are unable to evaluate data and/or evidence, to identify problems and issues, or to offer insights adequately. Your arguments will be unsupported and/or inappropriate, your organisation and writing will be poor and/or inappropriate, and referencing and use of discipline conventions poor/and or inconsistent: This grade is assigned for a total (sum of all the assessment components) in the range 25-44%. See assessment components for specific grading criteria
To achieve a grade of 1 (Low Fail, 0-24%), your essay will reflect minimal evidence of achievement, and exhibit deficiencies in skill acquisition and in your understanding of the fundamental concepts of the course. It will demonstrate that you have not undertaken adequate research, that you are unable to evaluate data and/or evidence, to identify problems and issues, and/or to offer insights adequately. Your arguments will be unsupported and/or inappropriate, your organisation and writing will be poor and/or inappropriate, and referencing and use of discipline conventions poor/and or inconsistent: This grade is assigned for a total (sum of all the assessment components) in the range 1-24%. See assessment components for specific grading criteria.
Grade X: No assessable work received.
Essay Proposals and Plans Assessment Criteria
Your project/essay proposal or plan will be assessed according to the following criteria:
1. Topic definition and rationale for project.
2. Concise statement of your research question or problem.
3. Outline of research approach, strategy and time plan.
4. Identification of key issues and/or challenges.
5. Evaluation of academic sources and evidence.
6. Presentation and written expression.
To achieve a grade of 7 (High Distinction, 85-100%), your proposal should demonstrate exceptional consideration of issues related to topic definition and rationale, and provide a nuanced and sophisticated statement of your research question and strategic approach. Project tasks should be identified very clearly, and the evaluation of relevant sources will be very insightful. Your proposal will be very well written, clear and concise, pay strict attention to discipline conventions and have minimal, if any errors in referencing, expression, grammar, spelling and punctuation.
To achieve a grade of 6 (Distinction, 75-84%), your proposal should demonstrate an advanced level of consideration of issues related to topic definition and rationale, and provide a very effective statement of your research question and strategic approach. Project tasks will be clearly identified, and the evaluation of relevant sources will be insightful. Your proposal will be well written, clear and concise, follow discipline conventions and have few errors in referencing, expression, grammar, spelling and punctuation.
To achieve a grade of 5 (Credit, 65 – 74%), your proposal should demonstrate proficient consideration of issues related to topic definition and rationale, and provide an effective statement of your research question and strategic approach. Project tasks will be well identified, with good evaluation of relevant sources. The presentation and referencing of your essay will follow/largely follow discipline conventions, have few/some errors in grammar, spelling and punctuation, and demonstrate your ability to communicate effectively.
To achieve a grade of 4 (Pass, 50 – 64%), your proposal should adequately consider some issues related to topic definition and rationale, and provide a functional or workable statement of your research question and strategic approach. There will be adequate identification of some project tasks, and a basic evaluation of some relevant sources. Your organisation, writing, referencing, spelling and grammar will be adequate and use some of the discipline conventions to communicate appropriately.
To achieve a grade of 3 (Marginal Fail, 45 – 49%), your proposal will consider issues related to topic definition and rationale only superficially, and will provide an attempt that demonstrates developing proficiency but ultimately does not result in a workable statement of your research question and/or strategic approach. Identification of project tasks will be superficial, and relevant sources will be evaluated inadequately. Your organisation, writing, spelling and grammar will be adequate/poor and your referencing and use of discipline conventions poor/and or inconsistent.
To achieve a grade of 2 (Fail, 25-44%), your proposal will demonstrate minimal consideration of issues related to topic definition and rationale, will fail to provide a workable statement of your research question and strategic approach. There will be poor or insufficient identification of project tasks, and inadequate evaluation of relevant sources. Your organisation and writing will be poor and/or inappropriate, and referencing and use of discipline conventions poor/and or inconsistent.
To achieve a grade of 1 (Low Fail, 0-24%), your proposal will fail to consider issues related to topic definition and rationale, and will not present a workable statement of your research question and strategic approach. There will be no or insufficient identification of project tasks, and no or insufficient evaluation of relevant sources. There will be an unsatisfactory standard of presentation and/or written expression. Use of discipline conventions will be absent, poor or inappropriate.
Quiz Assessment Criteria
Your quiz assessment task will be assessed according to the following criteria:
1. Ability to produce a succinct response to a directed question.
2. Knowledge of key themes, ideas or content.
3. Ability to employ relevant terminology.
4. Ability to write in clear English (where applicable).
Unlike formative assessment undertaken during your course (such as book reviews and essays), quiz answers are typically marked on right or wrong basis for multiple choice, single word and short answer questions. The overall grade achieved for a quiz is arrived at by totalling the marks for its constituent elements.
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.
6. Fostering of discussion and engagement of audience, as per course requirements.
To achieve a grade of 7 (High Distinction, 85-100%), you will have presented a very fluent and exceptionally well-prepared talk that clarifies and explains your topic with a very clear and effective structure. You will have utilised both supplied and original materials to achieve a highly affective discussion that complies precisely with the stated time limit.
To achieve a grade of 6 (Distinction, 75-84%) you will have presented a fluent and very well-prepared talk that clarifies and explains your topic with a clear and effective structure. You will have utilised supplied and original materials to achieve a very effective discussion that complies with the stated time limit.
To achieve a grade of 5 (Credit, 65 – 74%), you will have presented a well-prepared, proficient talk that clarifies and explains your topic with an adequate and functional structure. You will have utilised supplied and original materials to achieve an effective discussion within the stated time limit.
To achieve a grade of 4 (Pass, 50 – 64%), you will have presented a basic talk that addresses aspects of your topic but with some flaws in approach, structure and/or delivery. There may have been limitations in your use of supporting materials, and/or difficulties in meeting set time constraints.
To achieve a grade of 3 (Marginal Fail, 45 – 49%), you will have presented a talk that has clear potential, but has only superficially addressed aspects of your topic. There will have been several flaws in your approach, structure and/or delivery. There will have been limitations in your use of supporting materials, and/or difficulties in meeting set time constraints.
To achieve a grade of 2 (Fail, 25-44%), you will have presented a talk that was not adequately prepared or presented. Minimal effort will have been put into clarifying your topic, and your approach, structure and/or delivery will have been deficient or flawed. Major deficiencies will also have been present in utilising supporting materials and/or meeting time requirements.
To achieve a grade of 1 (Low Fail, 0-24%), you will have presented a talk that was not well prepared or presented. You will have not clarified your topic and your talk will have lacked an effective structure. You will not have utilised supporting materials and/or met time requirements.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
ﾠ ﾠFailure to submit all major assessment items (those worth 15% and above) will result in a maximum grade of 2 (Fail).
ﾠﾠﾠﾠﾠ For information on assessment remarks see: https://my.uq.edu.au/information-and-services/manage-my-program/exams-and-assessment/querying-result
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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.
Additional learning resources information
NA
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 (22 Jul - 28 Jul) |
Lecture |
Introduction and course foundations Week one is an introduction to the course, including expectations regarding assessment. We will then consider how ethics is relevant to data science. An introduction to practical ethics and the nature of moral inquiry and philosophical analysis will follow. Learning outcomes: L01, L02, L03, L04 |
Week 2 (29 Jul - 04 Aug) |
Lecture |
The theoretical tools of philosophical analysis In the lecture this week we will consider ethical reasoning, some key approaches to philosophical ethics and then practical decision-making and problem solving. Over the next few weeks we will progress from personal ethics, to professional ethics, to large-scale challenges in our social system. Learning outcomes: L01, L02, L03, L04, L05 |
Week 3 (05 Aug - 11 Aug) |
Lecture |
Assessment Preparation and Case Studies; Responsibility How the major assessment pieces will function. Some examples of disputes in data science ethics, and how we might resolve them. The place of professionals in the social ecosystem. Learning outcomes: L01, L02, L03, L04, L05 |
Week 4 (12 Aug - 18 Aug) |
Lecture |
The Big Picture: socio-political factors The politics of Big Data. Learning outcomes: L01, L02, L03, L04, L05 |
Week 5 (19 Aug - 25 Aug) |
Lecture |
Introduction to legal issues in Data Science Introduction to law and legal issues relevant to data science Introduction to law and data science What is law?, Jurisdiction, sources of law, and legal reasoning; law and technological change. Learning outcomes: L01, L03, L04, L05 |
Week 6 (26 Aug - 01 Sep) |
Lecture |
Intellectual Property and Contract Law Intellectual property and contract law Copyright, patents, and trademarks Law of contracts Open access, open source, and open data Learning outcomes: L01, L03, L04, L05 |
Week 7 (02 Sep - 08 Sep) |
Lecture |
Privacy and Cybersecurity Law The concept of privacy Information privacy law Law and cybersecurity Learning outcomes: L01, L03, L04, L05 |
Week 8 (09 Sep - 15 Sep) |
Lecture |
Technical: Responsible statistical practice I Students will learn to recognize some common misuses of statistics through case studies involving various statistical techniques ranging from statistical graphics, hypothesis testing to regression models. Learning outcomes: L01, L04, L05, L06 |
Week 9 (16 Sep - 22 Sep) |
Lecture |
Technical Strategies for Responsible DataSc: Responsible Statistical Practice Part II and Machine Learning Part I The statistical framework Responsible statistical practice Part II: Students will learn general guidelines on responsible uses of statistics and how to do this. Responsible Machine Learning Part I: In this part of the course we will introduce the ethical considerations in machine learning research and practice. Learning outcomes: L01, L04, L05, L06 |
Week 10 (30 Sep - 06 Oct) |
Lecture |
Responsible Machine Learning Part II In this part of the course we will explore various biases and discrimination issues in machine learning and introduce actionable strategies to mitigate these biases and realize fairness-aware machine learning. Cognitive Biases, Human Biases in Machine Learning, Actionable Strategies to Mitigate Biases. Learning outcomes: L01, L04, L05, L06 |
Week 11 (07 Oct - 13 Oct) |
Seminar |
Integrated seminar presentations Presentations will be assessed in the lecture and tutorial times of this week. Learning outcomes: L01, L02, L03, L04, L05, L06 |
Week 12 (14 Oct - 20 Oct) |
Seminar |
Integrated seminar presentations Presentations will be assessed in the lecture and tutorial times of this week. Learning outcomes: L01, L02, L03, L04, L05, L06 |
Week 13 (21 Oct - 27 Oct) |
Lecture |
Future Directions and Course Summary Discussion for final assessment, addressing what we have learned through the semester. Presentations may be assessed in the tutorial times of this week. Learning outcomes: L01, L02, L03, L04, L05 |
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:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.
Course guidelines
Please note that in order to pass this course all assessment items worth over 15% must be attempted.