MATHEMATICS AND STATISTUC

1. Course Roadmap
6
Lecture
Data, Analytics
and
Organisations
Data Exploration
and Visualisation
I
Data Exploration
and Visualisation
II
Predictive
Analytics I
Predictive
Analytics II
Flexibility Week DataEthics
Research Design
&
Experimentation
I
Research Design
&
Experimentation
II
Data
Communication
Data Analytics
Case Study & R
Data
visualization
using R
The S&P 500 Regression Classification Flexibility Week Data Ethics
Evaluating and
Designing
Experiments I
Evaluating and
Designing
Experiments II
Persuasive Data
Viz
3
2 4 5 8 9
7 10
1
Workshop

2. Learning Objectives
• Moral dilemmas and ethical theories in the context of Data ethics
• Ethical decision-making framework
• Ethical issues involving different stages of business analytics
• Key principles of data ethics
• Technical approaches to prevent and mitigate ethical issues
• Moral and legal aspects of data ethics
• Risk management for data ethics

3. Ethics: moral principles that govern a person's
behaviour or the conducting of an activity1
Morals: standards of behaviour; principles of
right and wrong
Collective
(inter-subjective assessment)
Individual
(subjective assessment)
Morals and Ethics

4. What is a moral/ethical dilemma?
https://www.moralmachine.net
Is it “easy” to be ethical?

5. Ethical theories: A Tale of Two Schools
Deontology Utilitarianism
Origins Coined from Greek “Deon”
meaning duty and care
Founder: Emmanuel Kant
Founder: Jeremy Bentham
Main Focus Moral duties, irrespective of
consequences.
Do our actions maximise the
positive outcome (utility) for most
people?
Keywords Duty for duty’s sake, Virtue is its
own reward, Rule-based
approach
Societal perspective, Public
happiness, Minimum Pain,
Consequentialism, Greatest Good
Examples?

7. A framework for making ethical decisions

8. Data Ethics:
“moral obligations of gathering, protecting,
and using personally identifiable information
and how it affects individuals”2.
Data ethics takes the view that we have
moral obligations and a duty of care towards
our customers/users, as custodians of their
data
Tensions between what is good for the
company vs. what is good for the user
Data ethics is also “a new branch of ethics that studies and evaluates moral problems
related to data, algorithms, and corresponding practices”3.

9. But…why must organisations care about
data ethics?
https://www.slido.com COMM1190_T3_202
2

10. Becoming a new source of competitive advantage
• Responsible business practices – using data for
good
• Maintain trust between companies and customers
and business partners
• Comply with government and industry regulations
• Enhance business reputation
• Reduce cost
…

11. Unpacking the Data Ethics Phenomenon in
organisations
People
(awareness, obligations)
Process
(principles, policies, legislation)
Technology
(infrastructure, solutions)
Data
Ethics

12. Define business
objectives
Collect data
Prepare and
explore data
Create training
and test datasets
Build and improve
the model
Deploy the model
Data Ethics – An analytics lifecycle perspective

13. Ethics – An analytics lifecycle perspective
Collect
Data
Store
Data
Analyze
Data
Communicate
Insights
Privacy X X
Security X
Bias X X
Transparency X X X
There is an obligation to keep uphold a user’s privacy, keep their data secure, analyse
the
data without bias and be transparent about what data we collect, how we use and store
it.

14. Data Privacy

15. Data Privacy
“the claim of individuals, groups and
institutions to determine for
themselves, when, how and to what
extent information about them is
communicated to others”1

16. Data Privacy - Key Principles
• Notice: inform users about privacy policy, privacy protection procedures e.g.
who will be collecting data, how data will be collected, who owns data
• Choice and consent: consent from individuals about the collection, use,
disclosure, and retention of their information
• Use and retention: data should be retained and protected according to law
or business practices required e.g. the length of data retention; avoid
secondary use of data for other purposes
https://www.oaic.gov.au/privacy/australian-privacy-principles/read-the-australian-privacy-
principles

17. Data Privacy - Key Principles
• Access: provide access to individuals with the access to review, update, and
modify the data about their personal information
• Protection: data is used only for the purpose stated; de-identifiable of
sensitive information; users have the right to opt out for the use of their data
• Enforcement and Redress: provide channels for individuals to report,
provide feedback, or complain

18. Australian Privacy Principles 1
1. open and transparent management of personal information
2. anonymity and pseudonymity
3. collection of solicited personal information
4. dealing with unsolicited personal information
5. notification of the collection of personal information
6. use or disclosure of personal information
7. direct marketing
8. cross-border disclosure of personal information
9. adoption, use or disclosure of government related identifiers
10. quality of personal information
11. security of personal information
12. access to personal information
13. correction of personal information

19. Data Types under Protection
• Identity data – name, address, personal number
• Demographic data – gender, age, education, religion, marital
status
• Analysis data –data attributes for which analysis is
conducted such as diseases, habits1

20. Think and Share: Are you OK with this?
“The amendments will enable
telecommunications companies to
temporarily share approved
government identifier information
(such as driver’s licence, Medicare
and passport numbers of affected
customers) with regulated financial
services entities to allow them to
implement enhanced monitoring and
safeguards for customers affected by
the data breach.”

21. Data Protection Mechanisms
There are ways to protect data…
• Anonymity – a user may use a resource or service without disclosing their identity
• Pseudonymity - a user acting under a pseudonym may use a resource or service
without disclosing their identity
• Unobservability - a user may use a resource or service without others being able
to observe that the resource or service is being used
• Unlinkability - sender and recipient cannot be identified as communicating with
each other1

22. Identity Protector (IP)
Software that helps keep identities secure: individual/enterprise use
• Reports and controls instances when identity is revealed
• Generates pseudo-identities
• Translates pseudo-identities into identities and vice-versa
• Converts pseudo-identities into other pseudo-identities
• Combats fraud and misuse of the system1

23. Application of IP at the database level

24. Data Security

25. Data Security
protection of the data against accidental or intentional loss,
destruction, or misuse from internal, external, and natural sources1.

26. Ethical Aspects of Data Security
• Attracting, training, and retaining
quality personnel to address ethical
issues.
• A perceived potential conflict of
interest also exists relative to ethical
behaviours and technical knowledge

27. Australian Security Principles 1
Protect customers against…
1. Misuse
2. Interference
3. Loss
4. Unauthorised access
5. Unauthorised modification
6. Unauthorised disclosure
https://www.oaic.gov.au/privacy/australian-privacy-principles-guidelines/chapter-11-
app-11-security-of-personal-information

28. Data Security Mechanisms
• Triggers: a system defined rule to handle unexpected events
• Authentication: identify persons attempting to gain access to data
• Authorization: identify users and restrict the actions they may take
against data
• Audit trial: maintain the audit and the backup of data changes

29. Data Security Mechanisms (Cont.)
• Triggers
 Prohibit inappropriate actions (e.g. changing salary records outside
normal business days)
 Cause special handling procedures to be executed (e.g., penalty
applied if payment received after a certain due date)
 Cause a log file to echo important information to review sensitive data
(e.g., reminding users to double check where sensitive information
change initiated)

30. • Authentication
 Password or personal identification number
 A smart card or a token
 Unique personal characteristics, such as fingerprint or retinal scan
• Authorization
 Identify users and restrict the actions (e.g., read, update, modify) they
may take against a data
Data Security Mechanisms (Cont.)

31. Audit Trail Example
Database
Management System
Database
(current)
Transaction
log
Database
change
log
Database
(backup)
Transactions Recovery action
Effect of transaction or
recovery action
Copy of database
affected by
transaction
Copy of transaction

32. Data Bias
https://www.visualcapitalist.com/50-cognitive-biases-in-
the-modern-world/
COMM1190_T3_202
2

33. Data Bias - Types and Mitigation Strategies
• Confirmation Bias
 People perform data analysis to prove predetermined assumptions
 Examples?
• How to avoid
 Record your beliefs and assumptions before starting your analysis
 Resist the temptation to generate hypotheses or gather additional
information to confirm your beliefs.
 Revisit your recorded beliefs and assumptions at the conclusion of
your analysis

34. Confirmation Bias Example
https://www.shortform.com/blog/black-swan-fallacy/

35. Data Bias - Types and Mitigation Strategies (Cont.)
• Outlier Bias
 Uncomfortable truths are hidden behind a good-enough average
 Outliers can be useful to detect fraud or risks
 Examples?
• How to avoid
 Examine the distribution of the sample
 Use median instead of average
 Identify and analyze outliers

36. Data Bias - Types and Mitigation Strategies (Cont.)
• Selection Bias
 Sample is not representative of the population
 Examples
• How to avoid
 Randomization
 Make sure sampling techniques are appropriate

37. Selection Bias Example
https://www.theguardian.com/technology/2021/oct/05/ex-uber-
driver-takes-legal-action-over-racist-face-recognition-software
Studies of several facial
recognition software
packages have shown that error
rates when recognising people
with darker skin have been
higher than among lighter-
skinned people, although
Microsoft and others have been
improving performance.

38. Data Bias - Types and Mitigation Strategies (Cont.)
• Survivorship Bias
 Focus on one side of a story e.g. focus on
positives only
 How ‘survivorship bias’ can cause you to make
mistakes
 Survivorship bias influences us to focus on the
characteristic of winners, due to a lack of visibility
of other samples—confusing our ability to
discern correlation and causation.
 Examples?
• How to avoid
 Develop a thorough understanding of the
phenomenon before data collection

39. Data Bias - Types and Mitigation Strategies (Cont.)
• Historical Bias
 Socio-cultural prejudices and beliefs are
mirrored into analytics process
 Examples?
• How to avoid
 Identify biases in historical sources
 Develop inclusive data governance
frameworks
https://www.inquirer.com/business/technology/apple-card-algorithm-sparks-
gender-bias-allegations-against-goldman-sachs-20191111.html

40. Data Transparency

41. Do you know what you’re sharing?
COMM1190_T3_202
2 https://hbr.org/2015/05/customer-data-designing-for-transparency-and-trust

42. Data Transparency
The principle of enabling the public to gain information about the
operations and structures of a given entity (Heald 2006)
• Understanding how data was selected, recorded, analyzed, and used
• Being able to access, update, and modify the information

43. The Guardian – Responsible business practices

44. Data Explainability – avoid black-boxed process
https://towardsdatascience.com/why-model-explainability-is-
the-next-data-science-superpower-b11b6102a5e0

45. How far (with our obligations) should we go
– Moral vs Legal?
ETHICS LAW
Meaning Ethics is a branch of moral
philosophy that guides people about
the basic human conduct.
The law refers to a systematic body of rules that
governs the whole society and the actions of its
individual members.
Objective Ethics are made to help people to
decide what is right or wrong and
how to act.
Law is created with an intent to maintain social order
and peace in the society and provide protection to all
the citizens.
Governed By Individual, Legal and Professional
norms
Government
Violation There is no punishment for violation
of ethics.
Violation of law is not permissible which may result in
punishment like imprisonment or fine or both.
Binding Ethics do not have a binding nature. Law has a legal binding.

46. • Identify risk
• Assess the vulnerability of critical assets
to specific threats
• Determine the expected likelihood and
consequences of specific types of
outcomes on specific assets
• Identify ways to reduce those risks
• Prioritise risk reduction measures
Risk Management for Data Ethics