Data and Artificial Intelligence (AI) Ethics Navigator

2/16 Principle 1: Human Centricity

Understanding public attitudes and ethical expectations We understand the community social attitudes, expectations and concerns in relation to trusting data analytics and AI solutions and reflect these considerations in our business practices.

3/16 Principle 2: Shared prosperity

Societal prosperity Data analytics and AI solutions are designed – where possible – to deliver benefits to the public (shared prosperity) in order to solve societal problems.

4/16 Principle 2: Shared prosperity

Monitoring practices We regularly monitor the impact of our AI/data analytics solutions on institutions, society, democracy and people to assess the benefits (as well as risks) of our data analytics and AI solutions.

5/16 Principle 3: Understanding, addressing and balancing competing needs

People’s needs and data proportionality When collecting data for the purposes of developing data analytics and AI solutions, we understand the people’s needs, and we evaluate the relevance and proportionality of the data being collected against those needs.

6/16 Principle 3: Understanding, addressing and balancing competing needs

Balancing consumer and employees’ needs We assess the impact of the data analytics and AI solutions on our employees. These impacts are taken into considerations to find the right balance between employees, consumers and business needs.

7/16 Principle 4: Human autonomy, empowerment and oversight

Human autonomy We have controls in place to prevent data and AI solutions from being used to subordinate, coerce, deceive, manipulate condition or nudge humans.

8/16 Principle 4: Human autonomy, empowerment and oversight

Human empowerment - explainability Decisions (automated, semi-automated, and human) produced or informed by AI and data analytics solutions are documented and articulated in ways that are understandable by internal (e.g. internal auditors, employees) and external stakeholders (e.g. consumers, regulators) irrespective of their technical background.

9/16 Principle 5: Do not harm (fairness, safety, cybersecurity and system performance)

Do not harm Our data analytics and AI solutions are designed and operate in a manner that respects, serves and protects humans’ physical and mental integrity, personal and cultural sense of identity, and satisfaction of their essential needs.

10/16 Principle 5: Do not harm (fairness, safety, cybersecurity and system performance)

Data and AI ethics - risk appetite We have defined the data and AI practices and use-cases which are deemed to be unethical, and therefore, prohibited. These include use-cases that represent a threat to human rights where interference with human rights is not deemed reasonable, necessary and proportionate (please refer to your local Human Rights Charter/Legislation).

11/16 Principle 6: Adopting ethical data privacy best practices

Data privacy - ethical best practices vs regulatory compliance Our data privacy practices are benchmarked against national and international best practices to address ethical expectations and concerns raised by the community, individuals and regulators. Examples of best practices include: data practices outlined in the ACCC inquiry on digital platforms; Germany Data Ethics Commission Recommendations issued in 2019; Recommendations issues by the UK Data Ethics and Innovation Commission following analysis of specific use-cases; U.S. Mind your Business Act.

12/16 Principle 6: Adopting ethical data privacy best practices

Data purpose We clearly articulate to the individual (consumer and employees) the purpose of collecting and processing data.

13/16 Principle 7: Data quality and integrity

Data quality We have procedures in place to assess the quality of the data being used to develop data analytics and AI solutions and address any data quality issues.

14/16 Principle 7: Data quality and integrity

Data anonymisation We assess and minimize the risk of re-identification.

15/16 Principle 8: Ethical data monetization

Third party information sharing with the public Consumers and employees are provided with a list of third parties we could share their data with; individuals are provided with the option to opt-out from having their de-identified data shared with specific third parties.

16/16 Principle 8: Ethical data monetization

Data sharing within the organisation Consumers and employees’ data is shared within the organisation only with authorised people who use this data solely for purposes aligned with the original consent provided by the consumer or employee.