Privacy Impact Assessment (PIA) for AI Systems - FAQ¶

FAQ

Quick Answers

When required: Any AI handling personal information or making automated decisions
When to start: Early in planning, before design is finalized
Who conducts: Privacy Officer or someone with privacy expertise
Key framework: Privacy Act 1988, Australian Privacy Principles

Overview¶

This FAQ provides practical guidance for conducting Privacy Impact Assessments for AI systems in the Australian Public Service.

General Questions¶

What is a Privacy Impact Assessment (PIA)?¶

A PIA is a systematic assessment of a project or initiative that identifies the impact that the project might have on the privacy of individuals. It helps identify privacy risks and provides solutions to manage, minimize, or eliminate these risks.

When is a PIA required for AI projects?¶

A PIA is required when: - The AI system will handle personal information - There's a change to how personal information is collected, used, or disclosed - The system uses new technology that impacts privacy - The project involves data matching or linking - There's profiling or automated decision-making about individuals

For AI specifically, a PIA is recommended for: - All AI systems handling any personal information - Systems making automated decisions affecting individuals - Any AI using machine learning on datasets containing personal information

Who should conduct the PIA?¶

PIAs should be conducted by someone with: - Understanding of privacy principles and the Privacy Act 1988 - Knowledge of the project/AI system - Independence from the project team (ideally)

Most agencies have Privacy Officers who can: - Conduct the PIA - Provide guidance and templates - Review and approve PIAs

When should I start the PIA process?¶

Early - ideally during project planning, before: - Finalizing system design - Procuring technology - Collecting or processing data - Going live

Starting early allows privacy to be built into design (privacy by design) rather than retrofitted.

AI-Specific Privacy Questions¶

What makes AI different from a privacy perspective?¶

AI systems raise unique privacy considerations:

Data Volume: AI often requires large datasets, increasing privacy risk
Automated Decisions: AI makes decisions without human review
Opacity: Complex AI models can be difficult to explain
Purpose Limitation: Models trained for one purpose might be used for another
Data Quality: AI can perpetuate or amplify biases in training data
Re-identification Risk: AI might identify individuals from de-identified data

How do I assess privacy risk for training data?¶

Consider:

Data Collection: - Is personal information necessary for training? - Can you use synthetic or anonymized data instead? - Is consent required? If so, is it informed and specific? - Are you collecting minimum necessary data?

Data Quality: - Is the data accurate and up-to-date? - Could biased data lead to discriminatory outcomes?

Data Security: - How is training data stored and protected? - Who has access? - How long is it retained?

De-identification: - If using de-identified data, could AI re-identify individuals? - Have you assessed re-identification risk?

What about privacy in AI model deployment?¶

For deployed models, assess:

Inputs: - What personal information do users provide? - Is collection notice provided? - Is consent obtained where required?

Processing: - How is personal information processed by the model? - Are decisions automated or human-in-the-loop? - Can individuals understand how decisions are made?

Outputs: - What personal information is in model outputs? - Who has access to outputs? - How are outputs used and disclosed?

Model Updates: - Is personal information retained to retrain models? - Are individuals informed about continuous learning?

How do I handle automated decision-making?¶

Australian Privacy Principle (APP) 1.3 requires taking reasonable steps to implement practices that ensure compliance when using or disclosing personal information for automated decision-making.

Best practices:

Human oversight: Have humans review significant decisions
Transparency: Explain that decisions are automated
Explainability: Provide meaningful information about decision logic
Contestability: Allow individuals to challenge decisions
Accuracy: Ensure data and models are accurate and current
Fairness: Test for and mitigate discriminatory outcomes

Document in PIA: - What decisions are automated - Significance of decisions (e.g., eligibility, services, rights) - Human involvement in decision-making - How individuals can challenge decisions

PIA Process Questions¶

What should I include in an AI PIA?¶

Key sections:

1. Project Description: - AI system purpose and functionality - How AI makes decisions - Types of personal information involved - Data flows (collection, use, disclosure, storage)

2. Privacy Impact Analysis: - Compliance with Australian Privacy Principles (APPs) - Specific privacy risks for each APP - Severity and likelihood of risks

3. Risk Mitigation: - Controls to address each risk - Privacy-enhancing technologies - Governance and oversight mechanisms

4. Consultation: - Stakeholders consulted (including privacy officer) - Feedback received and how addressed

5. Recommendations: - Actions required before deployment - Ongoing privacy measures

What are the Australian Privacy Principles (APPs)?¶

The Privacy Act 1988 includes 13 APPs that govern how personal information is handled:

Most relevant for AI:

APP 1: Open and transparent management of personal information
APP 3: Collection of solicited personal information (collection must be necessary)
APP 5: Notification of collection (privacy notices)
APP 6: Use or disclosure of personal information (purpose limitation)
APP 8: Cross-border disclosure (if using offshore AI services)
APP 10: Quality of personal information (accuracy)
APP 11: Security of personal information
APP 12: Access to personal information
APP 13: Correction of personal information

How do I assess privacy risk severity?¶

Use a risk matrix combining likelihood and consequence:

Likelihood: - Rare: May occur in exceptional circumstances - Unlikely: Could occur at some time - Possible: Might occur at some time - Likely: Will probably occur - Almost Certain: Expected to occur

Consequence (privacy harm): - Insignificant: Minimal impact; no remediation needed - Minor: Short-term inconvenience or embarrassment - Moderate: Distress, time/effort to remediate - Major: Significant distress, financial loss, harm - Severe: Serious harm, irreversible consequences

Risk Level = Likelihood × Consequence

High risks require strong mitigation measures before proceeding.

Common AI Privacy Risks & Mitigations¶

Risk: Re-identification from de-identified training data¶

Mitigation: - Use formal anonymization techniques (k-anonymity, differential privacy) - Assess re-identification risk with modern techniques - Consider synthetic data for training - Limit access to training data - Contractual protections with AI vendors

Risk: Automated decisions without transparency¶

Mitigation: - Provide clear notice that decisions are automated - Explain decision factors in plain language - Offer human review for significant decisions - Document decision logic - Use explainable AI techniques where possible

Risk: Purpose creep (using data for unintended purposes)¶

Mitigation: - Clearly define and limit AI system purpose - Obtain specific consent for new purposes - Implement technical controls preventing unauthorized use - Regular audits of data usage - Data governance policies

Risk: Offshore data transfer (using cloud AI services)¶

Mitigation: - Assess if offshore transfer is necessary - Consider onshore hosting options - Ensure vendor has appropriate privacy protections - Include privacy clauses in contracts - Assess foreign jurisdiction privacy laws - Document cross-border disclosure in PIA

Risk: Bias and discrimination¶

Mitigation: - Audit training data for bias - Test model outputs for discriminatory patterns - Use diverse, representative training data - Implement fairness constraints in models - Regular monitoring post-deployment - Human oversight for sensitive decisions

Risk: Data breaches and unauthorized access¶

Mitigation: - Encryption of data at rest and in transit - Access controls and authentication - Regular security assessments - Vendor security certification - Incident response plan - Data minimization (collect only what's needed)

Vendor and Procurement¶

What should I ask AI vendors about privacy?¶

Key questions:

Data handling:
Where is data stored and processed (geography)?
Who has access to our data?
Is our data used to train vendor models?
How long is data retained?
Security:
What security certifications do you have (ISO 27001, etc.)?
How is data encrypted?
What is your incident response process?
Privacy compliance:
Do you comply with Australian privacy law?
Can you provide a privacy impact assessment?
How do you handle data subject rights (access, correction)?
Transparency:
How do your models make decisions?
Can you provide explainability?
What bias testing have you conducted?
Governance:
Can we audit your privacy practices?
What privacy terms are in the contract?
Who owns the data and model outputs?

Should vendor PIAs be included in our PIA?¶

Yes. If using a vendor AI service: - Request the vendor's PIA - Review it for adequacy - Reference it in your PIA - Identify any gaps - Document additional agency-specific risks and controls

Your PIA should cover: - How the vendor service is used in your context - Your data flows to/from the vendor - Your governance and oversight of the vendor - Contractual privacy protections

Post-PIA¶

What happens after the PIA is completed?¶

Review: Privacy officer reviews and approves PIA
Implementation: Implement recommended controls and mitigations
Documentation: Store PIA with project records
Monitoring: Ongoing monitoring of privacy controls
Review: Regular PIA updates (annually or when changes occur)

When should I update the PIA?¶

Update the PIA when there are material changes: - New data sources or types of personal information - Changes to AI model or decision logic - New uses or disclosures of personal information - Security incidents - Changes to vendors or hosting - New legal or policy requirements

Best practice: Annual PIA review even without major changes.

Do I need to publish the PIA?¶

Generally, PIAs are internal documents. However: - Consider publishing a summary for transparency - Office of the Australian Information Commissioner (OAIC) recommends transparency - Some agencies publish PIAs as part of responsible AI commitments

If publishing: - Redact sensitive security information - Summarize key findings and mitigations - Make accessible on agency website

Resources and Support¶

Where can I get PIA templates?¶

OAIC: Guide to undertaking privacy impact assessments
Your agency privacy team: Most agencies have PIA templates
DTA: Digital Service Standard guidance
GovSafeAI Toolkit: Privacy assessment checklist (see templates folder)

Who can I contact for help?¶

Your agency Privacy Officer (first point of contact)
Office of the Australian Information Commissioner (OAIC): Guidance and advice
Attorney-General's Department: Privacy policy questions
GovSafeAI Team: AI-specific privacy questions

Where can I learn more?¶

Privacy Act and APPs: - Privacy Act 1988 - Australian Privacy Principles Guidelines

AI and Privacy: - OAIC guidance on AI and privacy - Australian Government AI Ethics Framework

General Privacy Guidance: - OAIC website - Privacy Awareness Week resources

Quick Reference Checklist¶

Use this checklist when starting a PIA for an AI project: