Ethical AI Decision Guide¶
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Purpose¶
This guide provides a practical framework for making ethical decisions when designing, developing, and deploying AI systems in government. It translates Australia's AI Ethics Principles into actionable decision-making tools.
- Does this respect human rights and dignity?
- Could this harm individuals or communities?
- Is this fair and non-discriminatory?
- Can we explain how decisions are made?
- Are we being transparent about AI use?
- Who is accountable if something goes wrong?
- Have we considered diverse perspectives?
1. Australia's AI Ethics Principles¶
1.1 The Eight Principles¶
| Principle | Core Question | Key Considerations |
|---|---|---|
| 1. Human, societal and environmental wellbeing | Does this benefit people and society? | Long-term impacts, sustainability, public good |
| 2. Human-centred values | Does this respect rights and dignity? | Autonomy, privacy, cultural values |
| 3. Fairness | Does this treat everyone equitably? | Bias, discrimination, equal access |
| 4. Privacy protection and security | Is personal information protected? | Data minimization, consent, security |
| 5. Reliability and safety | Does this work as intended safely? | Testing, fail-safes, risk management |
| 6. Transparency and explainability | Can we explain how it works? | Documentation, interpretability, disclosure |
| 7. Contestability | Can decisions be challenged? | Appeal processes, human review |
| 8. Accountability | Who is responsible? | Governance, oversight, liability |
1.2 Principle Interactions¶
Some decisions involve trade-offs between principles:
| Trade-off | Example | Resolution Approach |
|---|---|---|
| Fairness vs Privacy | Collecting demographic data for bias testing | Anonymize data; use statistical proxies |
| Explainability vs Accuracy | Complex models perform better but are opaque | Use interpretable models for high-stakes decisions |
| Safety vs Innovation | New AI could help but carries risks | Staged rollout with monitoring |
| Transparency vs Security | Disclosing AI details could enable gaming | Share high-level information; protect specifics |
2. Ethical Decision Framework¶
2.1 The ETHICS Decision Process¶
flowchart LR
E["<strong>E</strong><br/>Examine<br/>the situation"] --> T["<strong>T</strong><br/>Think through<br/>stakeholders"]
T --> H["<strong>H</strong><br/>Highlight<br/>risks & harms"]
H --> I["<strong>I</strong><br/>Identify<br/>options"]
I --> C["<strong>C</strong><br/>Choose &<br/>justify"]
C --> S["<strong>S</strong><br/>Safeguard &<br/>monitor"]
style E fill:#e3f2fd,stroke:#1976d2,stroke-width:2px
style T fill:#e8f5e9,stroke:#388e3c,stroke-width:2px
style H fill:#ffcc80,stroke:#ef6c00,stroke-width:2px
style I fill:#fff3e0,stroke:#f57c00,stroke-width:2px
style C fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px
style S fill:#e0f2f1,stroke:#00796b,stroke-width:2px 2.2 Step 1: Examine the Situation¶
Questions to answer:
| Question | Your Response |
|---|---|
| What is the AI being asked to do? | |
| What decisions will it make or support? | |
| What data will it use? | |
| Who will be affected by the AI? | |
| What is the context of use? | |
| What are the stakes involved? |
Risk Tier Assessment:
| Tier | Characteristics | Example Uses | Ethics Scrutiny |
|---|---|---|---|
| Tier 1: Minimal | No individual impact, internal only | Document summarization, internal analytics | Standard review |
| Tier 2: Limited | Some individual impact, human oversight | Search ranking, recommendation | Ethics checklist |
| Tier 3: Significant | Affects access to services or rights | Benefit eligibility triage, risk scoring | Full ethics review |
| Tier 4: High | Major impact on fundamental rights | Enforcement targeting, automated decisions | Ethics board approval |
2.3 Step 2: Think Through Stakeholders¶
Stakeholder Analysis:
| Stakeholder Group | How Affected | Concerns | Voice in Process |
|---|---|---|---|
| Primary users (staff) | |||
| Affected individuals | |||
| Vulnerable groups | |||
| Oversight bodies | |||
| General public |
Key Questions: - Have we consulted affected communities? - Are vulnerable groups disproportionately affected? - Have we included diverse perspectives in design? - Are there cultural considerations we need to address?
2.4 Step 3: Highlight Risks and Harms¶
Harm Categories:
| Harm Type | Description | Examples | Likelihood | Severity |
|---|---|---|---|---|
| Physical | Bodily harm or safety risks | Medical AI errors, infrastructure failures | ||
| Psychological | Mental distress or trauma | Insensitive interactions, anxiety from AI decisions | ||
| Financial | Economic loss or disadvantage | Incorrect benefit denials, unfair pricing | ||
| Reputational | Damage to standing or dignity | Privacy violations, profiling | ||
| Discriminatory | Unfair treatment of groups | Biased hiring, discriminatory access | ||
| Privacy | Exposure of personal information | Data breaches, inference attacks | ||
| Democratic | Impact on civic participation | Manipulation, suppression | ||
| Environmental | Ecological impact | Energy consumption, e-waste |
Harm Assessment Questions:
| Question | Response |
|---|---|
| What could go wrong? | |
| Who would be harmed and how? | |
| How likely is harm to occur? | |
| How severe would the harm be? | |
| Can harms be reversed or remedied? | |
| Are some groups more at risk than others? |
2.5 Step 4: Identify Options¶
Option Generation:
Consider alternatives including: 1. Proceed as planned - Accept current design 2. Modify the approach - Add safeguards or constraints 3. Alternative technology - Non-AI or different AI approach 4. Enhanced oversight - Add human review 5. Phased approach - Start limited, expand gradually 6. Delay or defer - Wait for better solutions 7. Do not proceed - Reject the use case
Options Evaluation Matrix:
| Option | Ethical Concerns Addressed | Practical Feasibility | Residual Risks |
|---|---|---|---|
| Option 1 | |||
| Option 2 | |||
| Option 3 |
2.6 Step 5: Choose and Justify¶
Decision Documentation:
| Element | Documentation |
|---|---|
| Decision made | |
| Rationale | |
| Principles prioritized | |
| Trade-offs accepted | |
| Safeguards required | |
| Conditions attached | |
| Dissenting views | |
| Approval authority |
Justification Test: - Would I be comfortable if this decision was made public? - Would I be comfortable if I were the person affected? - Can I explain this decision to a non-expert? - Does this decision align with our stated values?
2.7 Step 6: Safeguard and Monitor¶
Safeguards Checklist:
| Safeguard | Implementation | Owner | Status |
|---|---|---|---|
| Human oversight | |||
| Appeal/review process | |||
| Bias monitoring | |||
| Performance monitoring | |||
| Regular ethics review | |||
| Sunset clause |
Ongoing Monitoring: - Define ethics metrics to track - Schedule regular ethics reviews - Establish feedback mechanisms - Document issues and responses
3. Common Ethical Scenarios¶
3.1 Scenario: Using AI to Prioritize Service Requests¶
Situation: An agency wants to use AI to triage and prioritize citizen service requests.
Ethical Analysis:
| Principle | Consideration | Recommendation |
|---|---|---|
| Fairness | Risk of systematically deprioritizing certain groups | Test for disparate impact; ensure diverse training data |
| Transparency | Citizens should know AI is involved | Disclose AI use in service charter |
| Contestability | People should be able to challenge prioritization | Provide easy escalation path |
| Accountability | Clear ownership of prioritization outcomes | Name responsible officer |
Decision Framework: - LOW risk if used to assist humans who make final decisions - MEDIUM risk if it significantly affects service timing - HIGH risk if it effectively determines service access
3.2 Scenario: Predictive Risk Scoring¶
Situation: Using AI to identify high-risk cases for intervention (fraud, child safety, health).
Ethical Analysis:
| Principle | Consideration | Recommendation |
|---|---|---|
| Fairness | High risk of encoding historical biases | Extensive bias testing; avoid proxies for protected attributes |
| Privacy | Requires significant personal data | Data minimization; purpose limitation |
| Human-centred | Risk of treating people as statistics | Ensure human review of all flagged cases |
| Contestability | High-stakes decisions need challenge rights | Robust review process |
Red Lines: - No fully automated decisions for significant outcomes - No use of protected attributes as direct inputs - Must be able to explain why a case was flagged
3.3 Scenario: Chatbot for Public Services¶
Situation: Deploying an AI chatbot to handle citizen inquiries.
Ethical Analysis:
| Principle | Consideration | Recommendation |
|---|---|---|
| Transparency | Users should know they're talking to AI | Clear disclosure; don't impersonate humans |
| Reliability | Must provide accurate information | Regular quality checks; clear limits |
| Accessibility | Must work for diverse users | Multiple channels; escalation to humans |
| Privacy | May collect sensitive information | Minimize data collection; clear notices |
Guidelines: - Always identify as AI - Provide easy escalation to human - Don't handle high-stakes decisions - Don't collect more information than needed
3.4 Scenario: Using External AI Services (e.g., Large Language Models)¶
Situation: Using third-party AI services for government functions.
Ethical Analysis:
| Principle | Consideration | Recommendation |
|---|---|---|
| Privacy | Data may leave government control | No personal information without appropriate agreements |
| Accountability | Shared responsibility with vendor | Clear contractual terms |
| Reliability | May produce inaccurate information | Human verification required |
| Transparency | Third-party "black box" | Understand and document limitations |
Usage Guidelines: - Never input classified or sensitive information without appropriate approvals - Never use outputs for decisions without verification - Document limitations and failure modes - Ensure appropriate data agreements with vendors
4. Ethics Review Process¶
4.1 When Ethics Review is Required¶
| Trigger | Review Type |
|---|---|
| New AI system development | Full ethics assessment |
| Significant change to existing AI | Change impact review |
| New data source for AI | Data ethics review |
| AI affecting vulnerable groups | Enhanced review |
| AI with enforcement/compliance role | Mandatory ethics board review |
| Any Tier 3-4 AI use | Ethics committee approval |
4.2 Ethics Assessment Template¶
Section 1: AI Description | Field | Response | |-------|----------| | AI system name | | | Purpose | | | Type of AI | | | Decision type | | | Affected groups | |
Section 2: Principle Assessment
| Principle | How Addressed | Evidence | Gaps | Mitigations |
|---|---|---|---|---|
| Human wellbeing | ||||
| Human-centred values | ||||
| Fairness | ||||
| Privacy and security | ||||
| Reliability and safety | ||||
| Transparency and explainability | ||||
| Contestability | ||||
| Accountability |
Section 3: Risk Assessment | Risk | Likelihood | Severity | Mitigation | Residual Risk | |------|------------|----------|------------|---------------| | | | | | |
Section 4: Recommendation | Recommendation | Conditions | Review Date | |----------------|------------|-------------| | Approve / Approve with conditions / Reject | | |
4.3 Ethics Governance Structure¶
flowchart TB
EXEC["<strong>EXECUTIVE SPONSOR</strong><br/>Accountable for AI ethics"] --> EB
EXEC --> EL
EXEC --> PT
subgraph EB["<strong>ETHICS BOARD</strong>"]
EB1[Tier 3-4 approvals]
end
subgraph EL["<strong>ETHICS LEAD</strong>"]
EL1[Guidance & advice]
end
subgraph PT["<strong>PROJECT TEAMS</strong>"]
PT1[Ethics-by-design]
end
style EXEC fill:#e3f2fd,stroke:#1976d2,stroke-width:2px
style EB fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px
style EL fill:#fff3e0,stroke:#f57c00,stroke-width:2px
style PT fill:#e8f5e9,stroke:#388e3c,stroke-width:2px 5. Ethical Red Lines¶
5.1 Prohibited Uses¶
The following AI uses are generally prohibited in Australian government:
| Prohibited Use | Rationale | Exception Process |
|---|---|---|
| Mass surveillance of citizens | Privacy, human rights | None |
| Social scoring of citizens | Autonomy, dignity | None |
| Manipulation of democratic processes | Democratic values | None |
| Lethal autonomous weapons | Human control of force | Defence policy |
| Discrimination based on protected attributes | Anti-discrimination law | None |
5.2 High-Risk Uses Requiring Special Approval¶
| Use Case | Required Approval | Additional Safeguards |
|---|---|---|
| AI in criminal justice | Minister + Ethics Board | Independent oversight |
| AI affecting children | Ethics Board + Child safety | Guardian notification |
| AI in health decisions | Ethics Board + Clinical | Clinician oversight |
| AI denying benefits/services | Ethics Board + Legal | Full appeal rights |
| AI in national security | Security Committee | Classified oversight |
5.3 Ethical Boundaries Checklist¶
Before proceeding, confirm: - [ ] This is not a prohibited use - [ ] Appropriate approval obtained for high-risk use - [ ] Human oversight proportionate to risk - [ ] Appeal/contestability mechanism in place - [ ] Affected individuals informed of AI use - [ ] Data use is lawful and proportionate - [ ] No discrimination against protected groups
6. Handling Ethical Dilemmas¶
6.1 When Principles Conflict¶
Resolution Hierarchy:
- Fundamental rights take priority (no discrimination, privacy)
- Safety and wellbeing next (prevent harm)
- Procedural fairness follows (transparency, contestability)
- Operational considerations last (efficiency, cost)
6.2 Escalation Process¶
If you encounter an ethical dilemma you cannot resolve:
- Document the dilemma clearly
- Consult Ethics Lead or designated ethics advisor
- If unresolved, escalate to Ethics Board
- If still unresolved, escalate to Executive Sponsor
- Document final decision and rationale
6.3 Whistleblowing and Raising Concerns¶
If you believe an AI system raises serious ethical concerns:
- Raise with your manager in first instance
- If unresolved, contact Ethics Lead
- If still unresolved, use formal complaint channels
- Public Interest Disclosure provisions apply
Protection: Staff who raise ethical concerns in good faith are protected under PID legislation.
7. Embedding Ethics in AI Lifecycle¶
7.1 Ethics at Each Stage¶
| Stage | Ethics Activities | Deliverable |
|---|---|---|
| Ideation | Initial ethics screening | Ethics tier classification |
| Discovery | Stakeholder ethics concerns | Ethics requirements |
| Design | Ethics-by-design review | Ethics design document |
| Development | Bias testing, fairness checks | Fairness report |
| Testing | Ethics testing scenarios | Ethics test results |
| Deployment | Ethics go/no-go decision | Ethics clearance |
| Operations | Ongoing ethics monitoring | Ethics dashboard |
| Retirement | Ethics review of outcomes | Lessons learned |
7.2 Ethics in Agile/Sprint Processes¶
| Sprint Activity | Ethics Integration |
|---|---|
| Backlog grooming | Flag items with ethics implications |
| Sprint planning | Include ethics tasks in sprint |
| Daily standups | Raise ethics concerns early |
| Sprint review | Demo ethics features (explainability, etc.) |
| Retrospective | Discuss ethics lessons learned |
8. Tools and Checklists¶
8.1 Quick Ethics Checklist¶
Before any AI decision or milestone, confirm:
Fairness: - [ ] Tested for bias across demographic groups - [ ] No use of protected attributes as direct inputs - [ ] Diverse training data
Transparency: - [ ] AI use disclosed to affected parties - [ ] Decision logic documented - [ ] Limitations documented
Accountability: - [ ] Clear ownership assigned - [ ] Escalation path defined - [ ] Monitoring in place
Contestability: - [ ] Appeal process available - [ ] Human review option exists - [ ] Review timeframes appropriate
Privacy: - [ ] Data collection minimized - [ ] Consent obtained where required - [ ] Security controls implemented
8.2 Ethics Conversation Starters¶
Use these questions in team discussions:
- "What's the worst that could happen with this AI?"
- "Who might be harmed by this, and how?"
- "If this decision was in the news, how would we feel?"
- "Would we be comfortable if this happened to us?"
- "Have we heard from the people affected?"
- "What are we assuming that might not be true?"
- "Can we explain this to a non-technical person?"
- "What would we do if this goes wrong?"
8.3 Ethics Decision Tree¶
flowchart TB
START([Is AI being used?]) --> Q1{Does it affect<br/>individuals?}
Q1 -->|No| STD[Standard process]
Q1 -->|Yes| Q2{Are effects<br/>significant?}
Q2 -->|No| CHK[Ethics checklist]
Q2 -->|Yes| Q3{Does it affect<br/>rights/access?}
Q3 -->|No| ENH[Enhanced review]
Q3 -->|Yes| FULL[Full ethics<br/>board review]
style START fill:#e3f2fd,stroke:#1976d2,stroke-width:2px
style STD fill:#c8e6c9,stroke:#388e3c,stroke-width:2px
style CHK fill:#fff9c4,stroke:#f9a825,stroke-width:2px
style ENH fill:#ffcc80,stroke:#ef6c00,stroke-width:2px
style FULL fill:#ef9a9a,stroke:#c62828,stroke-width:2px 9. Resources and Support¶
9.1 Internal Resources¶
| Resource | Purpose | Contact |
|---|---|---|
| Ethics Lead | Guidance and advice | [Contact] |
| Ethics Board | Formal approvals | [Contact] |
| Privacy Officer | Privacy guidance | [Contact] |
| Legal Team | Legal compliance | [Contact] |
9.2 External Resources¶
| Resource | Description |
|---|---|
| Australia's AI Ethics Framework | National ethical framework |
| OAIC AI Guidance | Privacy and AI |
| OECD AI Principles | International framework |
9.3 Training and Development¶
| Training | Audience | Frequency |
|---|---|---|
| AI Ethics Fundamentals | All staff | Annual |
| Ethics in AI Development | Technical staff | At onboarding |
| Ethics Decision Making | Project leads | Annual |
| Ethics Board Orientation | Board members | At appointment |
10. Glossary¶
| Term | Definition |
|---|---|
| Algorithmic bias | Systematic errors in AI that create unfair outcomes |
| Contestability | Ability to challenge AI-influenced decisions |
| Explainability | Ability to describe how AI reaches conclusions |
| Fairness | Absence of discrimination or unfair treatment |
| Human-in-the-loop | Human review and approval in AI decisions |
| Human-on-the-loop | Human oversight of AI operations |
| Human-out-of-the-loop | Fully autonomous AI decision-making |
| Proxy discrimination | Using neutral factors that correlate with protected attributes |
| Transparency | Openness about AI use and functioning |