LLB250 Law, Privacy and Data Ethics
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | LLB250 |
|---|---|
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,892 |
| Pre-2021 CSP student contribution | $1,482 The pre-2021 commonwealth supported place (CSP) contribution amount only applies to students enrolled in a course prior to 2021. To learn more, visit our Understanding your fees page. |
| Domestic tuition unit fee | $2,976 |
| International unit fee | $3,852 |
Summer:
| Dates | Start 19 Nov 2023 End 22 Feb 2024 |
|---|---|
| Restrictions | Nil. Cross institutional and single unit students permitted with faculty approval. |
Unit Outline: Semester 1 2023, Gardens Point, Internal
| Unit code: | LLB250 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | LLB101 |
| Coordinator: | Brydon Wang | brydon.wang@qut.edu.au |
Overview
We live in an era where major advances in data-driven technologies are fundamentally changing many aspects of society. These technologies are not only becoming crucial to many businesses, which seek new avenues for creating competitive advantages and value, but also increasingly enmeshed in aspects of our everyday lives. This unit, therefore, explores the legal, ethical and social challenges raised by data-driven technologies in two main parts. The first centres on the information privacy law issues that arise from large-scale collection and aggregation of person information the second relates to the application of data analytics. Exploration of the challenges raised by different technologies across both parts of this unit are guided by broader considerations of fairness, accountability and transparency (FAT).
Learning Outcomes
On successful completion of this unit you will be able to:
- Apply the vocabulary of data analysis and machine learning to communicate about legal and social issues raised by technological innovation with technical and lay audiences (CLO 4.1)
- Evaluate the application of data analytics and analyse issues around bias, uncertainty, incomplete data, and valid and invalid inferences (CLO 3.1, 3.2)
- Apply the legal rules that govern the collection, storage, and use of data (CLO 1.1, 1.4)
- Critically evaluate the legal and ethical risks of new applications of data analysis, including privacy concerns, ethical risks, and public policy concerns (CLO 2.3, 3.1)
Content
The content of this unit includes:
- Introduction to the key drivers of automated decisionmaking and the vocabulary and core concepts of data analysis, machine learning
- Introduction to the ethical and legal issues raised by data analytics and developments in artificial intelligence
- Realising organizational opportunities of data analytics while protecting privacy regarding collection, storage, use of personal information
- The legal obligations governing information privacy
- Evaluating the legal and social risks of machine learning: understanding bias, discrimination, incomplete data, and mistaken inferences in the use of automated decisionmaking tools
Learning Approaches
This unit employs case study and inquiry based learning approaches to structure and conceptualise the unit content and to engage you in learning. The learning experiences provide you with the chance to think creatively, and produce material evidence of your preparedness for legal pathways in a rapidly evolving technology field.
This unit employs an active and collaborative approach to learning. It involves an online, self-led learning practice and live workshops where you will learn from experts and your peers. Prior to live workshops, you will be supported and engaged in this unit through the delivery of weekly material, interactive quizzes and formative learning activities, and collaborative discussions that will introduce a range of practical and theoretical perspectives.
Your participation in the unit will include:
- Engagement with online materials
- Discussions on online fora and in live workshops
- Collaboration with peers and experts to network and share your understandings
Feedback on Learning and Assessment
Students are provided with feedback to assist their learning throughout the semester. The feedback is provided through:
- the discussions in the weekly workshops
- feedback on workshop tasks
- online materials
- the individual feedback provided on assessment with the completed Criteria Referenced Assessment form
- generic feedback posted on the unit's Canvas for each item of assessment
- the option of consultation with a member of the teaching team.
Assessment
Overview
In this unit students are graded on a scale of one to seven.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Memo
Acting as a legal consultant, you will write a memorandum of advice to a client who is considering new applications of data analysis. Your advice will analyse the opportunities and potential legal and ethical risks, including privacy concerns and provide recommendations.
This is an assignment for the purposes of an extension.
Assessment: Essay
You will write an essay that critically examines the legal risks, public policy impacts and/or potential ethical concerns that could arise from a data analytics case study. You will identify and examine potential risks, impacts and/or concerns based on FAccT (e.g. fairness, accountability and transparency) considerations.
This is an assignment for the purposes of an extension.
Academic Integrity
Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.
Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.
Resources
Risk Assessment Statement
There are no unusual risks in this unit.
Unit Outline: Semester 1 2023, Online
| Unit code: | LLB250 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | LLB101 |
Overview
We live in an era where major advances in data-driven technologies are fundamentally changing many aspects of society. These technologies are not only becoming crucial to many businesses, which seek new avenues for creating competitive advantages and value, but also increasingly enmeshed in aspects of our everyday lives. This unit, therefore, explores the legal, ethical and social challenges raised by data-driven technologies in two main parts. The first centres on the information privacy law issues that arise from large-scale collection and aggregation of person information the second relates to the application of data analytics. Exploration of the challenges raised by different technologies across both parts of this unit are guided by broader considerations of fairness, accountability and transparency (FAT).
Learning Outcomes
On successful completion of this unit you will be able to:
- Apply the vocabulary of data analysis and machine learning to communicate about legal and social issues raised by technological innovation with technical and lay audiences (CLO 4.1)
- Evaluate the application of data analytics and analyse issues around bias, uncertainty, incomplete data, and valid and invalid inferences (CLO 3.1, 3.2)
- Apply the legal rules that govern the collection, storage, and use of data (CLO 1.1, 1.4)
- Critically evaluate the legal and ethical risks of new applications of data analysis, including privacy concerns, ethical risks, and public policy concerns (CLO 2.3, 3.1)
Content
The content of this unit includes:
- Introduction to the key drivers of automated decisionmaking and the vocabulary and core concepts of data analysis, machine learning
- Introduction to the ethical and legal issues raised by data analytics and developments in artificial intelligence
- Realising organizational opportunities of data analytics while protecting privacy regarding collection, storage, use of personal information
- The legal obligations governing information privacy
- Evaluating the legal and social risks of machine learning: understanding bias, discrimination, incomplete data, and mistaken inferences in the use of automated decisionmaking tools
Learning Approaches
This unit employs case study and inquiry based learning approaches to structure and conceptualise the unit content and to engage you in learning. The learning experiences provide you with the chance to think creatively, and produce material evidence of your preparedness for legal pathways in a rapidly evolving technology field.
This unit employs an active and collaborative approach to learning. It involves an online, self-led learning practice and live workshops where you will learn from experts and your peers. Prior to live workshops, you will be supported and engaged in this unit through the delivery of weekly material, interactive quizzes and formative learning activities, and collaborative discussions that will introduce a range of practical and theoretical perspectives.
Your participation in the unit will include:
- Engagement with online materials
- Discussions on online fora and in live workshops
- Collaboration with peers and experts to network and share your understandings
Feedback on Learning and Assessment
Students are provided with feedback to assist their learning throughout the semester. The feedback is provided through:
- the discussions in the weekly workshops
- feedback on workshop tasks
- online materials
- the individual feedback provided on assessment with the completed Criteria Referenced Assessment form
- generic feedback posted on the unit's Canvas for each item of assessment
- the option of consultation with a member of the teaching team.
Assessment
Overview
In this unit students are graded on a scale of one to seven.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Memo
Acting as a legal consultant, you will write a memorandum of advice to a client who is considering new applications of data analysis. Your advice will analyse the opportunities and potential legal and ethical risks, including privacy concerns and provide recommendations.
This is an assignment for the purposes of an extension.
Assessment: Essay
You will write an essay that critically examines the legal risks, public policy impacts and/or potential ethical concerns that could arise from a data analytics case study. You will identify and examine potential risks, impacts and/or concerns based on FAccT (e.g. fairness, accountability and transparency) considerations.
This is an assignment for the purposes of an extension.
Academic Integrity
Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.
Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.
Resources
Risk Assessment Statement
There are no unusual risks in this unit.
Unit Outline: Summer 2023, Gardens Point, Internal
| Unit code: | LLB250 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | LLB101 |
| Coordinators: | Henry Fraser | h5.fraser@qut.edu.au |
Overview
We live in an era where major advances in data-driven technologies are fundamentally changing many aspects of society. These technologies are not only becoming crucial to many businesses, which seek new avenues for creating competitive advantages and value, but also increasingly enmeshed in aspects of our everyday lives. This unit, therefore, explores the legal, ethical and social challenges raised by data-driven technologies in two main parts. The first centres on the information privacy law issues that arise from large-scale collection and aggregation of person information the second relates to the application of data analytics. Exploration of the challenges raised by different technologies across both parts of this unit are guided by broader considerations of fairness, accountability, transparency and explainability (FATE).
Learning Outcomes
On successful completion of this unit you will be able to:
- Apply the vocabulary of data analysis and machine learning to communicate about legal and social issues raised by technological innovation with technical and lay audiences (CLO 4.1)
- Evaluate the application of data analytics and analyse issues around bias, uncertainty, incomplete data, and valid and invalid inferences (CLO 3.1, 3.2)
- Apply the legal rules that govern the collection, storage, and use of data (CLO 1.1, 1.4)
- Critically evaluate the legal and ethical risks of new applications of data analysis, including privacy concerns, ethical risks, and public policy concerns (CLO 2.3, 3.1)
Content
The content of this unit includes:
- Introduction to the key drivers of automated decisionmaking and the vocabulary and core concepts of data analysis, machine learning
- Introduction to the ethical and legal issues raised by data analytics and developments in artificial intelligence
- Realising organizational opportunities of data analytics while protecting privacy regarding collection, storage, use of personal information
- The legal obligations governing information privacy
- Evaluating the legal and social risks of machine learning: understanding bias, discrimination, incomplete data, and mistaken inferences in the use of automated decisionmaking tools
Learning Approaches
This unit employs case study and inquiry based learning approaches to structure and conceptualise the unit content and to engage you in learning. The learning experiences provide you with the chance to think creatively, and produce material evidence of your preparedness for legal pathways in a rapidly evolving technology field.
This unit employs an active and collaborative approach to learning. It involves an online, self-led learning practice and live workshops where you will learn from experts and your peers. Prior to live workshops, you will be supported and engaged in this unit through the delivery of weekly material, interactive quizzes and formative learning activities, and collaborative discussions that will introduce a range of practical and theoretical perspectives.
Your participation in the unit will include:
- Engagement with online materials
- 2 semi-intensive blocks of face-to-face workshops held over four days in total. Each block will consist of 5-6 workshops held over two days. The first block will be held during w/c 20 November 2023 and the second during w/c 15 January 2024. Workshops are recorded and attendance is strongly encouraged.
- Discussions on online fora.
- Collaboration with peers and experts to network and share your understandings
Feedback on Learning and Assessment
Students are provided with feedback to assist their learning throughout the semester. The feedback is provided through:
- the discussions in the workshops
- feedback on workshop tasks
- online materials
- the individual feedback provided on assessment with the completed Criteria Referenced Assessment form
- generic feedback posted on the unit's Canvas for each item of assessment
- the option of consultation with a member of the teaching team.
Assessment
Overview
In this unit students are graded on a scale of one to seven.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Memo
Acting as a legal consultant, you will write a memorandum of advice to a client who is considering new applications of data analysis. Your advice will analyse the opportunities and potential legal and ethical risks, including privacy concerns and provide recommendations.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Essay
You will write an essay that critically examines the legal risks, public policy impacts and/or potential ethical concerns that could arise from a data analytics case study. You will identify and examine potential risks, impacts and/or concerns based on FATE (e.g. fairness, accountability, transparency and explainability) considerations.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Academic Integrity
Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.
Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.
Resources
Risk Assessment Statement
There are no unusual risks in this unit.
Unit Outline: Summer 2023, Online
| Unit code: | LLB250 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | LLB101 |
Overview
We live in an era where major advances in data-driven technologies are fundamentally changing many aspects of society. These technologies are not only becoming crucial to many businesses, which seek new avenues for creating competitive advantages and value, but also increasingly enmeshed in aspects of our everyday lives. This unit, therefore, explores the legal, ethical and social challenges raised by data-driven technologies in two main parts. The first centres on the information privacy law issues that arise from large-scale collection and aggregation of person information the second relates to the application of data analytics. Exploration of the challenges raised by different technologies across both parts of this unit are guided by broader considerations of fairness, accountability, transparency and explainability (FATE).
Learning Outcomes
On successful completion of this unit you will be able to:
- Apply the vocabulary of data analysis and machine learning to communicate about legal and social issues raised by technological innovation with technical and lay audiences (CLO 4.1)
- Evaluate the application of data analytics and analyse issues around bias, uncertainty, incomplete data, and valid and invalid inferences (CLO 3.1, 3.2)
- Apply the legal rules that govern the collection, storage, and use of data (CLO 1.1, 1.4)
- Critically evaluate the legal and ethical risks of new applications of data analysis, including privacy concerns, ethical risks, and public policy concerns (CLO 2.3, 3.1)
Content
The content of this unit includes:
- Introduction to the key drivers of automated decisionmaking and the vocabulary and core concepts of data analysis, machine learning
- Introduction to the ethical and legal issues raised by data analytics and developments in artificial intelligence
- Realising organizational opportunities of data analytics while protecting privacy regarding collection, storage, use of personal information
- The legal obligations governing information privacy
- Evaluating the legal and social risks of machine learning: understanding bias, discrimination, incomplete data, and mistaken inferences in the use of automated decisionmaking tools
Learning Approaches
This unit employs case study and inquiry based learning approaches to structure and conceptualise the unit content and to engage you in learning. The learning experiences provide you with the chance to think creatively, and produce material evidence of your preparedness for legal pathways in a rapidly evolving technology field.
This unit employs an active and collaborative approach to learning. It involves an online, self-led learning practice and live workshops where you will learn from experts and your peers. Prior to live workshops, you will be supported and engaged in this unit through the delivery of weekly material, interactive quizzes and formative learning activities, and collaborative discussions that will introduce a range of practical and theoretical perspectives.
Your participation in the unit will include:
- Engagement with online materials
- 2 semi-intensive blocks of face-to-face workshops held over four days in total. Each block will consist of 5-6 workshops held over two days. The first block will be held during w/c 20 November 2023 and the second during w/c 15 January 2024. Workshops are recorded and attendance is strongly encouraged.
- Discussions on online fora.
- Collaboration with peers and experts to network and share your understandings
Feedback on Learning and Assessment
Students are provided with feedback to assist their learning throughout the semester. The feedback is provided through:
- the discussions in the workshops
- feedback on workshop tasks
- online materials
- the individual feedback provided on assessment with the completed Criteria Referenced Assessment form
- generic feedback posted on the unit's Canvas for each item of assessment
- the option of consultation with a member of the teaching team.
Assessment
Overview
In this unit students are graded on a scale of one to seven.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Memo
Acting as a legal consultant, you will write a memorandum of advice to a client who is considering new applications of data analysis. Your advice will analyse the opportunities and potential legal and ethical risks, including privacy concerns and provide recommendations.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Essay
You will write an essay that critically examines the legal risks, public policy impacts and/or potential ethical concerns that could arise from a data analytics case study. You will identify and examine potential risks, impacts and/or concerns based on FATE (e.g. fairness, accountability, transparency and explainability) considerations.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Academic Integrity
Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.
Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.
Resources
Risk Assessment Statement
There are no unusual risks in this unit.