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Risk consequence rating advice

Negatively affecting public accessibility or inclusivity of government services

Insignificant

Insignificant compromises to accessibility or inclusivity of services.
Minor technical issues causing brief inconvenience but no actual barriers to access or inclusion.
Issues rapidly resolved with minimal impact on user experience.

Minor

Limited, reversable compromises to accessibility or inclusivity of services.
Some people experience difficulties accessing services due to technical issues or design oversights.
Barriers are short-term and addressed once identified, with additional support provided to people affected.

Moderate

Many compromises are made to the accessibility or inclusivity of services.
Considerable access challenges for a modest number of users.
Resolving access issues requires substantial effort and resources.
Certain groups may be disproportionately impacted.
Affected users experience frustration and delays in receiving services.

Major

Extensive compromises are made to the accessibility or inclusivity of services, may include some essential services.
Ongoing delays that require external technical assistance to resolve.
Widespread inconvenience, frustration, public distress and potential legal implications.
Vulnerable user groups disproportionately impacted.

Severe

Widespread irreversible ongoing compromises are made to the accessibility or inclusivity of services, including some essential services.
Majority of users, especially vulnerable groups affected.
Essential services inaccessible for extended periods, causing significant public distress, legal implications, and a loss of trust in government efficiency.
Comprehensive and immediate actions are urgently needed to rectify the situation.

Unfair discrimination against individuals, communities or groups

Insignificant

Negligible instances of discrimination, with virtually no discernible effect on individuals, communities, or groups.
Issues are proactively identified and rapidly addressed before causing harm.

Minor

Limited instances of unfair discrimination occur, affecting a small number of individuals.
Relatively isolated cases, and corrective measures minimise their impact.

Moderate

Moderate levels of discrimination leading to noticeable harm to certain individuals, communities, or groups.
These incidents raise bias and fairness concerns and require targeted interventions.

Major

Significant discrimination results in major, tangible harm to individuals and multiple communities or groups.
Rebuilding trust requires substantial reforms and remediation efforts.

Severe

Pervasive and systemic discrimination causes severe harm across a broad spectrum of the population, particularly marginalised and vulnerable groups.
Public outrage, potential legal action, and a profound loss of trust in government.
Immediate, sweeping reforms and accountability measures are required.

Perpetuating stereotyping or demeaning representations of individuals, communities or groups

Insignificant

Inadvertently reinforce mild stereotypes, but these instances are quickly identified and rectified with no lasting harm or public concern.
Minor
Isolated cases of stereotyping, affecting limited members of community with some noticing and raising concerns.
Prompt action mitigates the issue, preventing broader impact.

Moderate

Moderate stereotyping by AI systems leads to noticeable public discomfort and criticism.
Disproportionally affecting certain communities or groups.
Requires targeted corrective measures to address and prevent recurrence.

Major

Significant and widespread reinforcement of harmful stereotypes and demeaning representations.
Causes public outcry and damages the relationship between communities and government entities.
Urgent, comprehensive strategies are needed to rectify these representations and restore trust.

Severe

Pervasive and damaging stereotyping severely harms multiple communities, leading to widespread distress.
Potential legal consequences, and a profound breach of trust in government use of technology.
Requires immediate, sweeping actions to address the harm, including system overhauls and public apologies.

Harm to individuals, communities, groups, businesses or the environment

Insignificant

Inconsequential glitches with no real harm to the public, business operations or ecosystems.
Easily managed through routine measures.

Minor

Isolated incidents mildly affecting the public.
Slight inconveniences or disruptions to businesses, leading to manageable financial costs.
Limited manageable environmental disturbances affecting local ecosystems or resource consumption.

Moderate

Noticeable negative effects on the public.
Businesses face operational challenges or financial losses, affecting their competitiveness.
Obvious environmental degradation, including pollution or habitat disruption, prompting public concern.

Major

Significant public harm causing distress and potentially lasting damage.
Significant harm to a wide range of businesses, resulting in substantial financial losses, layoffs, and long-term reputational damage.
Compromises ecosystem wellbeing causing substantial pollution, loss of biodiversity, and resource depletion.

Severe

Widespread, profound harm and severe distress affecting broad segments of the public.
Profound damage across the business sector, leading to bankruptcies, major job losses, and a lasting negative impact on the economy.
Comprehensive environmental destruction, leading to critical loss of biodiversity, irreversible ecosystem damage, and severe resource scarcity.

Compromising privacy due to the sensitivity, amount or source of the data being used by an AI system

Insignificant

Insignificant data handling errors occur without compromising sensitive information.
Incidents are quickly rectified, maintaining public trust in data security.

Minor

Isolated exposure of limited sensitive data affects a small group of individuals.
Swift actions taken to secure the data and prevent further incidents.

Moderate

Breach of moderate amounts of sensitive data, leading to privacy concerns among the affected populace.
Some individuals experience inconvenience and distress.

Major

Serious misuse of sensitive private data affects a large segment of the population, leading to widespread privacy violations and a loss of public trust.
Comprehensive measures are urgently required to secure data and address the privacy breaches.

Severe

Significant potential to expose sensitive information of a vast number of individuals, causing severe harm, identity-theft risks; use of sensitive personal information in a way that is likely to draw public criticism with limited ability for individuals to choose how their information is used.
Significant potential to harm trust in government-information handling with potential for lasting consequences.

Raising security concerns due to the sensitivity or classification of the data being used by an AI system

Insignificant

Inconsequential security lapses occur without actual misuse of sensitive data.
Quickly identified and corrected with no real harm done.
These types of incidents may serve as prompts for reviewing security protocols.

Minor

A limited security breach involves unauthorised access to protected data affecting a small number of records with minimal impact.
Immediate actions secure the breach, and affected individuals are notified and supported.
Incident is catalyst for review of security protocols.

Moderate

Security incident leads to the compromise of a moderate volume of sensitive data, raising concerns over data protection and privacy.
The breach necessitates a thorough investigation, enhanced security measures.

Major

A significant security breach results in extensive unauthorised access to sensitive or protected data, causing considerable concern and distress among the public.
Urgent security upgrades and support measures for impacted individuals are implemented. to restore security and trust.

Severe

A massive security breach exposes a vast amount of sensitive and protected data, leading to severe implications for national security, public safety, and individual privacy.
This incident triggers an emergency response, including legal actions, a major overhaul of security systems, and long-term support for those affected.

Raising security concerns due to implementation, sourcing or characteristics of the AI system

Insignificant

Inconsequential security concerns arise due to characteristics of the AI system, such as software bugs, which are promptly identified and fixed with no adverse effects on overall security.
These issues may serve as lessons, leading to slight improvements in the system's security framework.

Minor

Certain characteristics of the AI system lead to vulnerabilities that are exploited in a limited manner, causing minor security breaches.
Immediate remediation measures are taken, and the system is updated to prevent similar issues.

Moderate

A moderate security risk is realised when intrinsic features of the AI system allow for unintended access or data leaks.
Incident affects a noticeable but contained component of the AI system.
Prompts a comprehensive security review of the AI system and the implementation of more robust safeguards.

Major

Significant security flaws in the AI system's design result in major breaches, compromising a large amount of data and severely affecting system integrity.
Incident leads to an urgent overhaul of security measures and protocols, alongside efforts to mitigate the damage.

Severe

Critical security vulnerabilities inherent to the AI system lead to widespread breaches, exposing vast quantities of sensitive data and jeopardising national security or public safety.
The incident results in severe consequences, necessitating emergency responses, extensive system redesigns, and long-term efforts to recover from the breach and prevent recurrence.

Influencing decision-making affects individuals, communities, groups, businesses or the environment

Insignificant

Decisions lead to negligible errors, swiftly identified and corrected with no harm to the public, business operations or the environment.
Incidents may serve as learning opportunity for system improvement.

Minor

Decisions result in minor inconveniences or errors affecting the public, business operations or finances or slight environmental impacts.
All impacts reversible with prompt action.

Moderate

Decisions cause moderate harm to the public, business operations or finances or noticeable environmental degradation.
Targeted interventions are required to mitigate these effects.

Major

Significant harm to the public, substantial business financial losses or operational disruptions, or significant environmental damage.
Loss of confidence in government, operations, service delivery and partnerships.
Significant harm to a wide range of businesses, resulting in substantial financial losses, layoffs, and long-term reputational damage.
Compromises ecosystem wellbeing causing substantial pollution, loss of biodiversity, and resource depletion.

Severe

AI's influence on critical decision-making processes leads to severe and widespread harm to public, business operations or finances or the environment.
Potentially endangering lives or significantly impacting public safety, rights and trust.
Causes massive job losses, undermining business economic stability and viability.
Catastrophic loss of ecosystems, endangered species, and long-term ecological imbalance or severe resources depletion.

Posing a reputational risk or undermining public confidence in the government

Insignificant

Isolated reputational issues arise, quickly addressed and explained.
Causes negligible damage to public trust in government capabilities.

Minor

Small-scale AI mishaps lead to brief public concern, slightly denting the government's reputation.
Prompt clarification and corrective measures minimize long-term impact on public confidence
Seen by the government as poor management.

Moderate

Misapplications result in moderate public dissatisfaction and questioning of government oversight.
Requires remedial actions to mend trust and address concerns.
Seen by government and opposition as failed management.

Major

Widespread public scepticism and criticism, majorly affecting the government's image.
Requires substantial efforts to rebuild public confidence through transparency, accountability, and improvement of AI governance.
High profile negative stories, seen by government and opposition as significant failed management.

Severe

Severe misuse or failure of AI systems leads to profound public distrust and criticism.
Significantly undermining confidence in government effectiveness and integrity.
Requires comprehensive, long-term strategies for rehabilitation of public trust, including systemic changes and ongoing engagement.
Seen by government and opposition as catastrophic failure of management.
Minister expresses loss of confidence or trust in agency.

Risk likelihood table

Likelihood	Probability	Description
Almost certain	91% and above	The risk is almost certain to eventuate within the foreseeable future.
Likely	61–90%	The risk will probably eventuate within the foreseeable future.
Possible	31–60%	The risk may eventuate within the foreseeable future.
Unlikely	5–30%	The risk may eventuate at some time but is not likely to occur in the foreseeable future.
Rare	Less than 5%	The risk will only eventuate in exceptional circumstances or as a result of a combination of unusual events.

2. Purpose and expected benefits

1. Basic information

1.1 AI use case profile

Complete the information below:
•    Name of AI use case.
•    Reference number.
•    Lead agency.
•    Assessment contact officer (name and email).
•    Executive sponsor (name and email).

1.2 AI use case description

In plain language, briefly explain how you are using or intend to use AI. 200 words or less.

1.3 Type of AI technology

Briefly explain what type of AI technology you are using or intend to use. 100 words or less.

1.4 Lifecycle stage

These stages can take place in an iterative manner and are not necessarily sequential. They are adapted from the OECD’s definition of the AI system lifecycle. Refer to guidance for further information. Select only one.

Which of the following lifecycle stages best describes the current stage of your AI use case?

Early experimentation (note: assessment not required).
Design, data and models
Verification and validation
Deployment
Operation and monitoring
Retirement

1.5 Review date

Assessments must be reviewed when use cases either move to a different stage of their lifecycle or significant changes occur to the scope, function or operational context of the use case. Consult the Guidance and, if in doubt, consult the DTA.

Indicate next date/milestone that will trigger the next review of the AI use case.

1.6 Assessment review history

Record the review history for this assessment. Include the review dates and brief summaries of changes arising from reviews (50 words or less).

3. Threshold assessment

3.1 Risk assessment

Using the risk matrix, determine the severity of each of the risks in the table below, accounting for any risk mitigations and treatments. Provide a rationale and an explanation of relevant risk controls that are planned or in place. The guidance document contains consequence and likelihood descriptors and other information to support the risk assessment.

The risk assessment should reflect the intended scope, function and risk controls of the AI use case. Keep the rationale for each risk rating clear and concise, aiming for no more than 200 words per risk.

Risk matrix
Likelihood/Consequence	Insignificant	Minor	Moderate	Major	Severe
Almost certain	Medium	Medium	High	High	High
Likely	Medium	Medium	Medium	High	High
Possible	Low	Medium	Medium	High	High
Unlikely	Low	Low	Medium	Medium	High
Rare	Low	Low	Low	Medium	Medium

What is the risk (low, medium or high) of the use of AI:

Negatively affecting public accessibility or inclusivity of government services?
Unfairly discriminating against individuals, communities or groups?
Perpetuating stereotyping or demeaning representations of individuals, communities or groups?
Harming individuals, communities, groups, organisations or the environment?
Raising privacy concerns due to the sensitivity, amount or source of the data being used by an AI system?
Raising security concerns due to the sensitivity or classification of the data being used by an AI system?
Raising security concerns due to the implementation, sourcing or characteristics of the AI system?
Influencing decision-making that affects individuals, communities, groups, organisations or the environment?
Posing a reputational risk or undermining public confidence in the government?

3.2 Assessment contact officer recommendation

If the assessment contact officer is satisfied that all risks in the threshold assessment are low, then they may recommend that a full assessment is not needed and that the agency accept the low risk.

If one or more risks are medium or above, then a full assessment must be completed, unless you amend the AI use scope, function or risk controls such that the assessment contact officer is satisfied that all risks in the threshold assessment are low.

You may decide not to accept the risk and not proceed with the AI use case.

The assessment contact officer recommendation should include:

the statement ‘a full assessment is/is not necessary for this use case’
comments (optional)
name and position
date.

3.3 Executive sponsor endorsement

The executive sponsor endorsement should include:

the statement ‘I have reviewed the recommendation, am satisfied by the supporting analysis and agree that a full assessment is/is not necessary for this use case’
comments (optional)
name and position
date.

4. Fairness

For each of the following questions, indicate either yes, no or N/A, and explain your answer.

4.1 Defining fairness

Do you have a clear definition of what constitutes a fair outcome in the context of your use of AI?

Where appropriate, you should consult relevant domain experts, affected parties and stakeholders to determine how to contextualise fairness for your use of AI. Consider inclusion and accessibility. Consult the guidance document for prompts and resources to assist you.

4.2 Measuring fairness

Do you have a way of measuring (quantitatively or qualitatively) the fairness of system outcomes?

Measuring fairness is an important step in identifying and mitigating fairness risks. A wide range of metrics are available to address various concepts of fairness. Consult the guidance document for resources to assist you.

5. Reliability and safety

For each of the following questions, indicate either yes, no or N/A, and explain your answer.

5.1 Data suitability

If your AI system requires the input of data to operate, or you are training or evaluating an AI model, can you explain why the chosen data is suitable for your use case?

Consider data quality and factors such as accuracy, timeliness, completeness, consistency, lineage, provenance and volume.

5.2 Indigenous data

If your AI system uses Indigenous data, including where any outputs relate to Indigenous people, have you ensured that your AI use case is consistent with the Framework for Governance of Indigenous Data?

Consider whether your use of Indigenous data and AI outputs is consistent with the expectations of Indigenous people, and the Framework for Governance of Indigenous Data (GID). See definition of Indigenous data in guidance material.

5.3 Suitability of procured AI model

If you are procuring an AI model, can you explain its suitability for your use case?

May include multiple models or a class of models. Includes using open-source models, application programming interfaces (APIs) or otherwise sourcing or adapting models. Factors to consider are outlined in guidance.

5.4 Testing

Outline any areas of concern in results from testing. If testing is yet to occur, outline elements to be considered in testing plan (for example, the model’s accuracy).

5.5 Pilot

Have you conducted, or will you conduct, a pilot of your use case before deploying?

If answering ‘yes’, explain what you have learned or hope to learn in relation to reliability and safety and, if applicable, outline how you adjusted the use of AI.

5.6 Monitoring

Have you established a plan to monitor and evaluate the performance of your AI system?

If answering ‘yes’, explain how you will monitor and evaluate performance.

5.7 Preparedness to intervene or disengage

Have you established clear processes for human intervention or safely disengaging the AI system where necessary (for example, if stakeholders raise valid concerns with insights or decisions or an unresolvable issue is identified)?

See guidance document for resources to assist you in establishing appropriate processes.

6. Privacy protection and security

For each of the following questions, indicate either yes, no or N/A, and explain your answer.

6.1 Minimise and protect personal information

Are you satisfied that any collection, use or disclosure of personal information is necessary, reasonable and proportionate for your AI use case?
See guidance on data minimisation and privacy enhancing technologies.

6.2 Privacy assessment

Has the AI use case undergone a Privacy Threshold Assessment or Privacy Impact Assessment?

6.3 Authority to operate

Has the AI system been authorised or does it fall within an existing authority to operate in your environment, in accordance with Protective Security Policy Framework (PSPF) Policy 11: Robust ICT systems?

Engage with your agency’s IT Security Adviser and consider the latest security guidance and strategies for AI use (such as Engaging with AI from the Australian Signals Directorate).

7. Transparency and explainability

For each of the following questions, indicate either yes, no or N/A, and explain your answer.

7.1 Consultation

Have you consulted stakeholders representing all relevant communities or groups that may be significantly affected throughout the lifecycle of the AI use case?

Refer to the list of stakeholders identified in section 2. Seek out community representatives with the appropriate skills, knowledge or experience to engage with AI ethics issues. Consult the guidance document for prompts and resources to assist you.

7.2 Public visibility

Will appropriate information (such as the scope and goals) about the use of AI be made publicly available?

See guidance document for advice on appropriate transparency mechanisms, information to include and factors to consider in deciding to publish or not publish AI use information.

7.3 Maintain appropriate documentation and records

Have you ensured that appropriate documentation and records will be maintained throughout the lifecycle of the AI use case?

Ensure you comply with requirements for maintaining reliable records of decisions, testing and the information and data assets used in an AI system. This is important to enable internal and external scrutiny, continuity of knowledge and accountability.

7.4 Disclosing AI interactions and outputs

Will people directly interacting with the AI system or relying on its outputs be made aware of the interaction or that they are relying on AI generated output? How?

Consider members of the public or government officials that may interact with the system or decision makers that may rely on its outputs.

7.5 Offer appropriate explanations

If your AI system will materially influence administrative action or decision making by or about individuals, groups, organisations or communities, will your AI system allow for appropriate explanation of the factors leading to AI generated decisions, recommendations or insights?

8. Contestability

For each of the following questions, indicate either yes, no or N/A, and explain your answer.

8.1 Notification of AI affecting rights

Will individuals, groups, organisations or communities be notified if an administrative action with a legal or similarly significant effect on them was materially influenced by the AI system?

See guidance document for help interpreting ‘administrative action’, ‘materially influenced’ and ‘legal or similarly significant effect’ as well as recommendations for notification content.

8.2 Challenging administrative actions influenced by AI

Is there a timely and accessible process to challenge the administrative actions discussed at 8.1?

Administrative law is the body of law that regulates government administrative action. Access to review of government administrative action is a key component of access to justice. Consistent with best practice in administrative action, ensure that no person could lose a right, privilege or entitlement without access to a review process or an effective way to challenge an AI generated or informed decision.

9. Accountability

9.1 Establishing responsibilities

Identify who will be responsible for:

use of AI insights and decisions
monitoring the performance of the AI system
data governance.

Where feasible, it is recommended that the same person does not hold all 3 of these roles. The responsible officers should be appropriately senior, skilled, and qualified.

9.2 Training of AI system operators

For question 9.2, indicate either yes, no or N/A, and explain your answer.

Is there a process in place to ensure operators of the AI system are sufficiently skilled and trained?

With all automated systems, there is always the risk of overreliance on results. It is important that the operators of the system, including any person who exercises judgment over the use of insights, or responses to alerts, are appropriately trained on the use of the AI system. Training should be sufficient to understand how to appropriately use the AI system, and to monitor and critically evaluate outcomes.