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ANU COMP2420 / COMP6420 - Introduction to Data Management, Analysis and Security
Topic Outline

Roger Clarke **

Released Version of 13 April 2020

© Xamax Consultancy Pty Ltd, 2020

This document is at http://www.rogerclarke.com/DV/DEDP-20.html

Introduction

This 2-hour segment addresses important issues arising from data analysis. It comprises two lectures, delivered at two different stages of the semester-unit:

• Lecture 1 - IT and Data Ethics  -  Week 6, Fri 24 Apr 09:30-11:00
  • Required Reading
  • Further Reading
  • Discussion Questions
• Lecture 2 - Data Protection & Data Privacy  -  Week 10, Fri 22 May 09:30-11:00
  • Required Reading
  • Further Reading
  • Discussion Questions

The Examinable Materials

The examinable materials comprise the following:

• the Required Readings listed in the Lecture Outlines above
• the Slide-Sets linked to from the Lecture Outlines above
• Any additional material provided during Lectures

The Further Reading is not examinable. It's provided in order to enable you to 'drill down' on topics you're particularly interested in, or need to understand more about.

Lecture Outlines
Lecture 1: IT and Data Ethics

Week 6 - Fri 24 Apr 09:30-11:00 - Coombs Theatre

The slides are available in PDF for viewing, and PDF-4up for printing

The video-presentation is available in mp4 format

TOPIC OUTLINE

1. Introduction

• The power of IT and of its impact and implications
• The general concept of ethics
• Codes of Ethics of the ACS and other professional societies

2. Ethics in Data Science / Data Analysis

• Ethical Issues, and the Harm Arising
• Case Studies of Ethical Issues:
  1. Robo-Debt
  2. Data Scrubbing / Cleaning / Cleansing
  3. The Transparency of Inferences and Decisions
  4. The Digital Surveillance Economy

3. Applied Ethics

• Codes
• Guidelines for Data Analytics
• A Business Process with Embedded QA

REQUIRED READING

The Power of IT

s.4.1 Information Infrastructure in 2018, at http://www.rogerclarke.com/II/IIC18.html#II18

Ethics

The Introduction section to 'Ethics', International Encyclopaedia of Philosophy, at https://www.iep.utm.edu/ethics/

Codes of Ethics

Australian Computer Society (ACS), at http://www.acs.org.au/content/dam/acs/rules-and-regulations/Code-of-Professional-Conduct_v2.1.pdf

Ethical Issues in Data Science

s.4.2 Data Harm, at http://www.rogerclarke.com/II/DRC.html#GH

Case Studies

Centrelink's Big Data 'Robo-Debt' Fiasco of 2016-20, at http://www.rogerclarke.com/DV/CRD17.html

s.2.5 Data Scrubbing, at http://www.rogerclarke.com/EC/BDQAS.html#ChDS

s.2.7 Decision Transparency (2016), at http://www.rogerclarke.com/EC/BDQAS.html#ChDS

Guidelines for Data Analytics

Guidelines for the responsible application of data analytics (2018), at http://www.rogerclarke.com/EC/GDA-Tab2E.pdf

FURTHER READING

The Power of IT

What Drones Inherit from Their Ancestors, at http://www.rogerclarke.com/SOS/Drones-I.html

Cyborgs Today (2017), at http://www.rogerclarke.com/SOS/Cyborgs17.html

Ethics

The remainder of 'Ethics', International Encyclopaedia of Philosophy, at https://www.iep.utm.edu/ethics/

Kinch N. (2019) 'Data Ethics in tech; Here's why it's so hard' Medium, 4 Jan 2019, at https://medium.com/greater-than-experience-design/data-ethics-in-tech-heres-why-it-s-so-hard-b3e0d8f0a108

Persons-at-Risk

s.4.3 Market Segmentation for Privacy-Enhancing Technologies (PETs), at http://www.rogerclarke.com/DV/UPETs-1405.html#MS

s.4.4 Risk Assessment for Whistleblowers, at http://www.rogerclarke.com/DV/UPETs-1405.html#RA

Codes of Ethics

Association for Computing Machinery (ACM), at https://www.acm.org/code-of-ethics

IEEE, at https://www.ieee.org/about/corporate/governance/p7-8.html

Engineers Australia, at https://www.engineersaustralia.org.au/sites/default/files/resource-files/2020-02/828145%20Code%20of%20Ethics%202020%20D.pdf

Ethical Issues in Data Science

Data Values harmed by Data Analytics (2016), at http://www.rogerclarke.com/EC/PBAR.html#Tab4

Big Data, Big Risks (2016), at http://www.rogerclarke.com/EC/BDBR.html

Case Studies

Doctorow C. (2017) 'Australia put an algorithm in charge of its benefits fraud detection and plunged the nation into chaos' BoingBoing, 1 Feb 2018, at https://boingboing.net/2018/02/01/dole-bludgers-under-beds.html

s.2.2 The Rationale Underlying a Decision (2014), at http://www.rogerclarke.com/SOS/Drones-I.html#CRD

s.4 The Digital Surveillance Economy, at http://www.rogerclarke.com/EC/DSE.html#DSE

Codes for Data Analytics

'Data Science Association Code Of Professional Conduct' (2016), at http://www.datascienceassn.org/sites/default/files/datasciencecodeofprofessionalconduct.pdf

'American Statistical Association Ethical Guidelines for Statistical Practice' (2016), at https://www.amstat.org/ASA/Your-Career/Ethical-Guidelines-for-Statistical-Practice.aspx

Guidelines for Data Analytics

Article on 'Guidelines for the Responsible Application of Data Analytics' (2018), at http://www.rogerclarke.com/EC/GDA.html

Business Processes

'Towards Responsible Data Analytics: A Process Approach' (2019), at http://www.rogerclarke.com/EC/BDBP.html

Anonymisation / De-Identification

s.3, at http://www.rogerclarke.com/DV/RFED.html#DId

Lecture 1 - DISCUSSION QUESTIONS

Slide 7: Some Ethical Issues

How does IT generally, and Data Analytics in particular, contribute to:

1. climate change
2. the political impacts of location and tracking
3. unfair discrimination against lower economic demographics
4. continuous disruption of organisations and occupations
5. the casualisation of labour

Slide 12: Ethical Issues in Data Science

What are your expectations about the results you are awarded by the ANU for the courses you do? In particular, what do you think about the ideas of:

1. transparency of decision-rationale
2. automated decision-making
3. due process / procedural fairness

Slide 15: Case Study #1 - Robodebt

What ethical issues arise from the Robo-Debt case?

Surely Centrelink should have discovered in advance that the design of the new system was highly unreasonable. But how precisely should they gave discovered that?

Slides 20-25: Case Study #4 - The Digital Surveillance Economy

Companies that you deal with on the Web do the following things, in order to maximise their revenue or profit. As a shareholder, are you happy that they do these things? As a consumer, are you happy that they do these things?

1. they gather a lot of data about you
2. they share that data with many other companies
3. they use that data to categorise you
4. they apply ad targeting, i.e. select ads to display to you that appear most likely to influence your purchasing decisions
5. they make offers to you at the highest price your profile suggests you're prepared to pay

Lecture 2: Data Protection & Data Privacy

Week 10 - Fri 22 May 09:30-11:00 - Coombs Theatre

The slides are avalable in PDF for viewing, and PDF-4up for printing

The video-presentation is available in mp4 format

TOPIC OUTLINE

1. Introduction
   • Data, Information
   • Data Sensitivities
2. Data Security
   • The Conventional Security Model
   • Locations of Security Risks
3. Privacy
   • The Concept
   • The Reasons
   • The Dimensions of Privacy
4. Data Privacy
   • Threats across the Data Life-Cycle
5. Safeguards
   • Organisational Safeguards
   • Legal Safeguards
   • Technical Safeguards (PETs)

REQUIRED READING

Data and Information

Fundamentals of Information Systems, at http://www.rogerclarke.com/SOS/ISFundas.html

Knowledge, at http://www.rogerclarke.com/SOS/Know.html

Data Quality and Information Quality Factors, at http://www.rogerclarke.com/EC/BDBR.html#Tab1

The Conventional Security Model

Appendix 1, at http://www.rogerclarke.com/EC/SSACS.html#App1

Privacy

Introduction to Privacy, at http://www.rogerclarke.com/DV/Intro.html

Persons-at-Risk

Categories of Persons-at-Risk, at http://www.rogerclarke.com/DV/UPETs-1405.html#MS

The Life-Cycle of Personal Data

'Data Management for Data Protection (GDPR)' (Legner C. & Labadie C., 2019), at https://www.cc-cdq.ch/data-management-for-data-protection-gdpr

Privacy Law

The Australian Privacy Principles (APPs), at https://www.oaic.gov.au/privacy/australian-privacy-principles/australian-privacy-principles-quick-reference/

The Notifiable Data Breaches scheme, at https://www.oaic.gov.au/privacy/notifiable-data-breaches/about-the-notifiable-data-breaches-scheme/

Privacy-Enhancing Technologies

Categories of PETs, at http://www.rogerclarke.com/DV/Biel15-DuD.html#P

'Privacy in a pandemic: Keep calm, and remember first principles' (Johnston, 2020), at https://www.salingerprivacy.com.au/2020/03/31/privacy-in-a-pandemic/

FURTHER READING

The Conventional Security Model

User-Friendly Security Solutions, ss.3 and 3.1 and Table 1, at http://www.rogerclarke.com/EC/SSACS.html#SS

Appendix 2: Baseline Security Features, at http://www.rogerclarke.com/EC/SSACS.html#App2

Data Values harmed by Data Analytics (2016), at http://www.rogerclarke.com/EC/PBAR.html#Tab4

Privacy

'What's Privacy?', at http://www.rogerclarke.com/DV/Privacy.html

The EU's General Data Protection Regulation

'What is the GDPR?', at https://gdpr.eu/what-is-gdpr/

Privacy-Enhancing Technologies

'Introducing PITs and PETs: Technologies Affecting Privacy', at http://www.rogerclarke.com/DV/PITsPETs.html

Lecture 2 - DISCUSSION QUESTIONS

Slides 3-4: Data and Information

Think about what you're learning in this unit, COMP2420/6420. Now give some examples from this unit of data, information, knowledge and wisdom.

Slides 5-6: Data and Information Quality

The ANU gathers data about your performance in this unit of study. It makes most of it available to you. It publishes some of it on your transcript. Various people will use that data for various purposes.

Consider each of the 7 data quality and 6 information quality factors, and give examples of good quality and bad quality data and information about your performance.

Slides 8-15: The Conventional Security Model

Apply the concepts of threat, vulnerability, security incident, harm, asset, stakeholder and safeguard to the personal data that the ANU holds about you.

Slides 17-19: Privacy Needs, Harm, and Risk Categories

Identify a couple of examples of privacy concerns that you have, or that your friends or relatives have.

Which needs, harms and risk categories are relevant to those privacy concerns?

What circumstances might arise that would increase the levels of concern that people feel about those particular privacy issues?

Slides 27-28: Privacy Protections

In relation to the privacy concerns you've just discussed, to what extent do you think that the law provides adequate protections?

Slides 29-38: Privacy-Enhancing Technologies

What PETs do you use?

What extra PETs should people use who face more serious risks than you do?

Author Affiliations

Roger Clarke is Principal of Xamax Consultancy Pty Ltd, Canberra. He is also a Visiting Professor in the Research School of Computer Science at the Australian National University,, and in the Cyberspace Law & Policy Centre at the University of N.S.W.

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Created: 9 March 2020 - Last Amended: 13 April 2020 by Roger Clarke - Site Last Verified: 15 February 2009
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