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[null,"AI RISK GUIDANCE"]
[]
["This guide to AI risk is designed to support NHS organisations in understanding and managing the complex landscape of AI risks, helping to ensure the responsible and secure deployment of AI technologies. It explains the different types of AI risks and poses key questions at each stage of development to guide users in assessing and addressing risks within their specific use cases. This document was written for the NHS.net Connect service in relation to Copilot Extensibility, any use beyond this intended purpose is at organisation discretion."]
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["AI Risk Guidance"]
["The AI Risk Guidance sections are designed to help assess your AI use case and identify potential risks within a given solution. They aim to ask the right questions at the right stages and provide guidance on the steps organisations should do to manage risks associated with AI.\n\nIt is split into 3 sections :\n1. Initiation\n2. Development\n3. Post-Monitoring\n\nEach section contains relevant questions to ask at  their respective stage of a project. You can filter the areas that risks fall under, as well as whether a question is relevant to your use case. For example, some questions apply only to custom AI solutions where training data is used and fine tuning is involved."]
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["AI Risk List"]
["The list provides a comprehensive view of the various types of risk you may encounter when developing AI systems, along with the broader categories and areas that they fall under.Each risk is explained in detail to help users gain a deeper understanding and assess whether it is relevant to their specific use case.\n\nRisk teams can also use this section to map AI risks to any existing risks and controls they may already have in place."]

{"sheet":"Risk Guidance - All Questions","index":1,"n_rows":68,"n_cols":8,"rows_kept":68,"cut":false,"hidden":false,"merged":[[0,0,0,5]]}
["AI Risk Guidance"]
["Q No","Risk","Area","Applies to","AI Lifecycle Stage\n","Question"]
[1,"Impact on Cultural Diversity","Inclusivity","All","Post-monitoring","Have you evaluated the output of the AI System when it is asked questions based on religion and race?"]
[2,"Environmental Impact","Inclusivity","Custom AI","Development","Have you assessed the environmental impact and sustainability of AI model training and management activities? "]
[3,"Lack of Accountability","Accountability","All","Post-monitoring","Who will be responsible for addressing any system performance issues identified through continuous risk monitoring?"]
[4,"Lack of AI Expertise","Accountability","All","Initiation","Do the team who are responsible for deploying and using the solution have the correct competencies and expertise to assess, develop, deploy and use the solution appropriately?"]
[5,"Level of Automation - Loss of Oversight","Accountability","All","Development","What is the processes for human oversight?"]
[6,"Lack of Maintainability","Accountability","Custom AI","Development","Have you checked that the solution is resilient to changes in environment, including external stimuli, data drift, or internal modifications?"]
[7,"Complexity of Environment","Accountability","Custom AI","Development","Have you designed and developed the solution such that it is resilient to complex external environments datasets cover all relevant contexts in which the solution might be deployed?"]
[8,"Confidential Information in Data","Compliance","Custom AI","Development","Has confidential information been included as part of the training, testing or validation datasets for the solution?"]
[9,"Data Usage Rights Restrictions","Compliance","All","Development","Have you checked if any applicable terms of service, license compliance, or other IP issues which may restrict the ability to use certain data for developing this solution?"]
[10,"IP Information in Prompt","Compliance","All","Development","Have you checked that no IP information is being included in the prompt or database for the GenAI model?"]
[11,"Confidential Data in Prompt","Security","All","Development","Have you checked that the prompt does not include any confidential information about the organisation, its employees or other stakeholders (ex: patients, suppliers)?"]
[12,"Revealing Confidential Information","Security","All","Development","Have you checked that the system does not reveal any confidential information about the organisation, its employees or other stakeholders (ex: patients, suppliers)?"]
[13,"Copyright Infringement","Compliance","Custom AI","Development","Have you checked that the training data does not include any material that has been obtained without an appropriate license?"]
[14,"Generating Content Ownership & IP","Compliance","All","Development","Have you considered, if applicable, that the Gen AI solution could generate content that is similar or identical to copyright material?"]
[15,"Data Usage, Acquisition, transfer Restrictions","Compliance","All","Development","Have you checked to see if the solution complies with relevant laws or other restrictions, that limit or prohibit the use, collection or transfer of some data for specific AI use cases?"]
[16,"Data Privacy Rights Alignment","Compliance","All","Development","Have you checked to see if the solution complies with relevant laws or other restrictions, that include provisions for data subject rights such as opt-out, right to access, and right to be forgotten?"]
[17,"Third Party Relationships","Accountability","All","Development","If your AI System relies on partners and/or external code libraries have you checked if the current versions in use have any known vulnerabilities?"]
[18,"Third Party Relationships","Accountability","All","Development","Are you aware whether or not your inputs / outputs will be used:\n1. To improve any models from the foundation model provider?\n2. To improve any 3rd party products or services from the foundation model provider?"]
[19,"Third Party Relationships","Accountability","All","Development","Have you checked that the known limitations of the model, as listed in the vendor documentation, do not adversely affect the final solution?"]
[20,"Hallucination","Transparency","All","Development","Have you tested the AI system for hallucination propensity, considering the potential to generate factually inaccurate or untruthful content?",null,"`"]
[21,"Prompt Leaking","Security","Custom AI","Development","Have you assessed, and if applicable, addressed, the possibility of an attack attempting to extract the model's system prompt?"]
[22,"Prompt Injection Attack","Security","Custom AI","Development","Have you assessed, and if applicable, addressed, the possibility of an attack which forces a generative model that takes a prompt as input to produce unexpected output by manipulating the structure, instructions, or information contained in its prompt?"]
[23,"Evasion Attack","Security","Custom AI","Development","Have you applied any techniques to control evasion attacks such as input perturbation, feature-space and model inversion attacks?"]
[24,"Poor Model Accuracy","Transparency","All","Development","Have you tested the solution for accuracy using metrics relevant to its purpose?"]
[25,"System Hardware Issues","Security","Custom AI","Post-monitoring","Have you considered an incident management plan in case hardware issues disrupt the availability of, or service provided by, the solution? "]
[26,"System Lifecycle Issues","Transparency","All","Post-monitoring","How are known and emerging risks are managed across the lifecycle of the solution?"]
[27,"Technology Readiness","Accountability","All","Post-monitoring","How are technical updates to the software, library or 3rd party platform incorporated when they occur?"]
[28,"Security: General","Security","All","Post-monitoring","\nHow are security risks evaluated and monitored?"]
[29,"Data Poisoning","Security","Custom AI","Development","Have you considered guardrails within the solution, such as access controls or maintaining data lineage, to block data poisoning efforts?"]
[30,"Jailbreaking","Security","All","Development","Have you considered incorporating content filters / prompt instructions to ensure that the solution cannot be jailbroken and made to give responses outside its intended scope?"]
[31,"Prompt Priming","Security","All","Development","Have you considered incorporating content filters / prompt instructions to ensure that the solution does not reveal sensitive information?"]
[32,"Incorrect Risk Testing","Security","All","Post-monitoring","Have you checked that any techniques and metrics for measuring risk are appropriate to the management of the related risk?"]
[33,"Lack of Robustness","Security","Custom AI","Development","Have you considered testing the system to ensure that it will still produce a reliable output even in the presence of invalid inputs or stressful environmental conditions?"]
[34,"Lack of Fairness: General","Inclusivity","All","Post-monitoring","Have you reviewed the AI systems to ensure that it does not reflect any intrinsic biases?"]
[35,"Improper Usage","Transparency","All","Development","Have you laid out instructions for users for the appropriate usage of the AI System?"]
[36,"Spreading Disinformation","Transparency","All","Development","Have you included methods to ensure that all outputs produced will be factually correct?"]
[37,"Bias","Inclusivity","All","Development","Have you assessed and, where applicable, managed the risk of bias in your solution including data bias, decision bias, and output bias  to ensure that no group is unfairly advantaged or disadvantaged? Have you tested the outputs against fairness metrics appropriate to your deployment context?"]
[38,"Toxic Output","Transparency","All","Development","Have you added content filters/ prompt instructions to ensure that the solution does not create outputs which are hateful, abusive, profane or obscene in nature?"]
[39,"Lack of Testing Diversity","Inclusivity","Custom AI","Development","Have you checked that the test data is suitable and sufficiently representative for the intended purposes, including appropriate statistical properties and context specific considerations?"]
[40,"Impact on Affected Communities","Inclusivity","All","Development","Have you considered the perspectives and concerns of communities likely to be affected by the outcomes of deploying the solution into the solution design?"]
[41,"Safety","Inclusivity","All","Development","Have you considered and, if applicable, managed the risk that deployment of your solution may constitute to the physical safety of individuals?"]
[42,"Impact on Human Agency","Inclusivity","All","Development","Have you taken measures to ensure that the outputs of the solution do not unfairly influence or manipulate end users' decisions?\n"]
[43,"Dangerous Use","Accountability","All","Development","Have you considered guardrails to prevent misuse of the generative AI solution for intentionally dangerous purposes?"]
[44,"Harmful Output","Security","All","Development","Have you assessed and, if applicable, managed the risk that a solution tailored to an individual may be manipulated to output private or sensitive data?"]
[45,"Incomplete Advice","Transparency","All","Development","Have you checked that, if your solution outputs recommendations, that the recommendations made are based on complete information appropriate to the context and support human decision making?"]
[46,"Harmful Code Generation","Transparency","All","Development","Have you assessed and, if applicable, managed the risk that the solution could generate harmful code?"]
[47,"Over- or Under-Reliance","Accountability","All","Post-monitoring","Have you checked that the intended users of the solution have the appropriate competencies, guidelines and training to make decisions using the outputs?"]
[48,"Lack of Transparency: General","Transparency","All","Post-monitoring","Have you checked that those interacting with the solution have access to appropriate information regarding the use of AI in the solution and it's capabilities and limitations?"]
[49,"Uncertain Data Provenance","Transparency","All","Development","Have you assessed how data lineage encompassing  ownership, origin, and transformations is managed across your solution?"]
[50,"Incomplete Usage Definition","Transparency","All","Initiation","Have you clearly defined the purpose of the solution and the expected outcomes to relevant stakeholders including internal management and the intended users?"]
[51,"Lack of Data Transparency","Transparency","Custom AI","Post-monitoring","If applicable, have training and tuning datasets been documented, in a structured manner with clear explanations of the fields, and approporaitely stored?"]
[52,"Non-Disclosure","Transparency","All","Development","Have you included a notification in the user interface to let the user know that they are interacting with AI solution?"]
[53,"Unreliable Source Attribution","Transparency","Custom AI","Development","Have you assessed that the source attribution is reliable for the solution, ensuring that outputs can be rationalised in relation to training data?"]
[54,"Lack of Explainability","Transparency","All","Development","Have you identified the level of explainability required for the use-case that will be developed and deployed, and implemented relevant metrics and a period of testing for monitoring?"]
[55,"Inaccessible Training Data","Transparency","Custom AI","Post-monitoring","Has the training data been made accessible to those with appropriate permissions?"]
[56,"Untraceable Attribution","Transparency","Custom AI","Post-monitoring","Is it possible for those with appropriate access permissions to utilise source attribution techniques to validate and audit the outputs of the solution?"]
[57,"Availability/ Quality of Training & Test Data","Transparency","Custom AI","Initiation","If applicable, is the training and testing data required for the solution available and of the appropriate quality?"]
[58,"Improper Data Curation","Transparency","Custom AI","Initiation","If applicable, have you followed appropriate data curation processes to ensure that the data used is appropriate to the solution?"]
[59,"Improper Retraining","Transparency","Custom AI","Development","If you have performed retraining for the solution, have you checked that the output data used is correct, appropriate and relevant to the use case?"]
[60,"Unrepresentative Data","Inclusivity","All","Initiation","If applicable, have you checked that data used is sufficiently representative for the intended purposes?"]
[61,"Data Contamination","Security","Custom AI","Initiation","If applicable, have you checked that the selection of training data for the solution is fit for the purpose, aligned with the solutions purpose and not part of other development tasks such as testing and evaluation?"]
[62,"Unrepresentative Risk Testing","Inclusivity","Custom AI","Development","Have you checked that the input data that has been used during the pre-deployment phase to test risks and system performance aligns with the input that will be evaluated post-deployment?"]
[63,"Lack of Privacy","Transparency","All","Development","Have you checked that, where PII or sensitive data is approved for the use case, the user data is collected and stored in a secure manner, and appropriate consent has been secured?"]
[64,"Clinical Data","Security","All","Initiation","If clinical data is being used, have you got sign-off from the clinical data team?"]
[65,"Reidentification","Transparency","All","Development","If applicable, have you implemented PII masking techniques so that an individual user can not be identified from the database associated with a solution?"]
[66,"Personal Information Exposure and storage","Security","All","Development","If the use of PII or sensitive data is not approved for the use case, have you ensured that this data is not recorded in the solution database or will not be shared with the user even if the user submits it?"]

{"sheet":"Risk Guidance - Initiation","index":2,"n_rows":9,"n_cols":6,"rows_kept":9,"cut":false,"hidden":false,"merged":[[0,0,0,5]]}
["AI Risk Guidance"]
["Risk","Area","Applies to","AI Lifecycle Stage\n","Question","Response"]
["Lack of AI Expertise","Accountability","All","Initiation","Do the team who are responsible for deploying and using the solution have the correct competencies and expertise to assess, develop, deploy and use the solution appropriately?"]
["Incomplete Usage Definition","Transparency","All","Initiation","Have you clearly defined the purpose of the solution and the expected outcomes to relevant stakeholders including internal management and the intended users?"]
["Availability/ Quality of Training & Test Data","Transparency","Custom AI","Initiation","If applicable, is the training and testing data required for the solution available and of the appropriate quality?"]
["Improper Data Curation","Transparency","Custom AI","Initiation","If applicable, have you followed appropriate data curation processes to ensure that the data used is appropriate to the solution?"]
["Unrepresentative Data","Inclusivity","All","Initiation","If applicable, have you checked that data used is sufficiently representative for the intended purposes?"]
["Data Contamination","Security","Custom AI","Initiation","If applicable, have you checked that the selection of training data for the solution is fit for the purpose, aligned with the solutions purpose and not part of other development tasks such as testing and evaluation?"]
["Clinical Data","Security","All","Initiation","If clinical data is being used, have you got sign-off from the clinical data team?"]

{"sheet":"Risk Guidance - Development","index":3,"n_rows":46,"n_cols":8,"rows_kept":46,"cut":false,"hidden":false,"merged":[[0,0,0,5]]}
["AI Risk Guidance"]
["Risk","Area","Applies to","AI Lifecycle Stage\n","Question","Response"]
["Environmental Impact","Inclusivity","Custom AI","Development","Have you assessed the environmental impact and sustainability of AI model training and management activities? "]
["Level of Automation - Loss of Oversight","Accountability","All","Development","What is the processes for human oversight?"]
["Lack of Maintainability","Accountability","Custom AI","Development","Have you checked that the solution is resilient to changes in environment, including external stimuli, data drift, or internal modifications?"]
["Complexity of Environment","Accountability","Custom AI","Development","Have you designed and developed the solution such that it is resilient to complex external environments datasets cover all relevant contexts in which the solution might be deployed?"]
["Confidential Information in Data","Compliance","Custom AI","Development","Has confidential information been included as part of the training, testing or validation datasets for the solution?"]
["Data Usage Rights Restrictions","Compliance","All","Development","Have you checked if any applicable terms of service, license compliance, or other IP issues which may restrict the ability to use certain data for developing this solution?"]
["IP Information in Prompt","Compliance","All","Development","Have you checked that no IP information is being included in the prompt or database for the GenAI model?"]
["Confidential Data in Prompt","Security","All","Development","Have you checked that the prompt does not include any confidential information about the organisation, its employees or other stakeholders (ex: patients, suppliers)?"]
["Revealing Confidential Information","Security","All","Development","Have you checked that the system does not reveal any confidential information about the organisation, its employees or other stakeholders (ex: patients, suppliers)?"]
["Copyright Infringement","Compliance","Custom AI","Development","Have you checked that the training data does not include any material that has been obtained without an appropriate license?"]
["Generating Content Ownership & IP","Compliance","All","Development","Have you considered, if applicable, that the Gen AI solution could generate content that is similar or identical to copyright material?"]
["Data Usage, Acquisition, transfer Restrictions","Compliance","All","Development","Have you checked to see if the solution complies with relevant laws or other restrictions, that limit or prohibit the use, collection or transfer of some data for specific AI use cases?"]
["Data Privacy Rights Alignment","Compliance","All","Development","Have you checked to see if the solution complies with relevant laws or other restrictions, that include provisions for data subject rights such as opt-out, right to access, and right to be forgotten?"]
["Third Party Relationships","Accountability","All","Development","If your AI System relies on partners and/or external code libraries have you checked if the current versions in use have any known vulnerabilities?"]
["Third Party Relationships","Accountability","All","Development","Are you aware whether or not your inputs / outputs will be used:\n1. To improve any models from the foundation model provider?\n2. To improve any 3rd party products or services from the foundation model provider?"]
["Third Party Relationships","Accountability","All","Development","Have you checked that the known limitations of the model, as listed in the vendor documentation, do not adversely affect the final solution?"]
["Hallucination","Transparency","All","Development","Have you tested the AI system for hallucination propensity, considering the potential to generate factually inaccurate or untruthful content?"]
["Prompt Leaking","Security","Custom AI","Development","Have you assessed, and if applicable, addressed, the possibility of an attack attempting to extract the model's system prompt?"]
["Prompt Injection Attack","Security","Custom AI","Development","Have you assessed, and if applicable, addressed, the possibility of an attack which forces a generative model that takes a prompt as input to produce unexpected output by manipulating the structure, instructions, or information contained in its prompt?"]
["Evasion Attack","Security","Custom AI","Development","Have you applied any techniques to control evasion attacks such as input perturbation, feature-space and model inversion attacks?"]
["Poor Model Accuracy","Transparency","All","Development","Have you tested the solution for accuracy using metrics relevant to its purpose?"]
["Data Poisoning","Security","Custom AI","Development","Have you considered guardrails within the solution, such as access controls or maintaining data lineage, to block data poisoning efforts?"]
["Jailbreaking","Security","All","Development","Have you considered incorporating content filters / prompt instructions to ensure that the solution cannot be jailbroken and made to give responses outside its intended scope?",null,null,"`"]
["Prompt Priming","Security","All","Development","Have you considered incorporating content filters / prompt instructions to ensure that the solution does not reveal sensitive information?"]
["Lack of Robustness","Security","Custom AI","Development","Have you considered testing the system to ensure that it will still produce a reliable output even in the presence of invalid inputs or stressful environmental conditions?"]
["Improper Usage","Transparency","All","Development","Have you laid out instructions for users for the appropriate usage of the AI System?"]
["Spreading Disinformation","Transparency","All","Development","Have you included methods to ensure that all outputs produced will be factually correct?"]
["Bias","Inclusivity","All","Development","Have you assessed and, where applicable, managed the risk of bias in your solution including data bias, decision bias, and output bias  to ensure that no group is unfairly advantaged or disadvantaged? Have you tested the outputs against fairness metrics appropriate to your deployment context?"]
["Toxic Output","Transparency","All","Development","Have you added content filters/ prompt instructions to ensure that the solution does not create outputs which are hateful, abusive, profane or obscene in nature?"]
["Lack of Testing Diversity","Inclusivity","Custom AI","Development","Have you checked that the test data is suitable and sufficiently representative for the intended purposes, including appropriate statistical properties and context specific considerations?"]
["Impact on Affected Communities","Inclusivity","All","Development","Have you considered the perspectives and concerns of communities likely to be affected by the outcomes of deploying the solution into the solution design?"]
["Safety","Inclusivity","All","Development","Have you considered and, if applicable, managed the risk that deployment of your solution may constitute to the physical safety of individuals?"]
["Impact on Human Agency","Inclusivity","All","Development","Have you taken measures to ensure that the outputs of the solution do not unfairly influence or manipulate end users' decisions?\n"]
["Dangerous Use","Accountability","All","Development","Have you considered guardrails to prevent misuse of the generative AI solution for intentionally dangerous purposes?"]
["Harmful Output","Security","All","Development","Have you assessed and, if applicable, managed the risk that a solution tailored to an individual may be manipulated to output private or sensitive data?"]
["Incomplete Advice","Transparency","All","Development","Have you checked that, if your solution outputs recommendations, that the recommendations made are based on complete information appropriate to the context and support human decision making?"]
["Harmful Code Generation","Transparency","All","Development","Have you assessed and, if applicable, managed the risk that the solution could generate harmful code?"]
["Uncertain Data Provenance","Transparency","All","Development","Have you assessed how data lineage encompassing  ownership, origin, and transformations is managed across your solution?"]
["Non-Disclosure","Transparency","All","Development","Have you included a notification in the user interface to let the user know that they are interacting with AI solution?"]
["Unreliable Source Attribution","Transparency","Custom AI","Development","Have you assessed that the source attribution is reliable for the solution, ensuring that outputs can be rationalised in relation to training data?"]
["Lack of Explainability","Transparency","All","Development","Have you identified the level of explainability required for the use-case that will be developed and deployed, and implemented relevant metrics and a period of testing for monitoring?"]
["Improper Retraining","Transparency","Custom AI","Development","If you have performed retraining for the solution, have you checked that the output data used is correct, appropriate and relevant to the use case?"]
["Unrepresentative Risk Testing","Inclusivity","Custom AI","Development","Have you checked that the input data that has been used during the pre-deployment phase to test risks and system performance aligns with the input that will be evaluated post-deployment?"]
["Lack of Privacy","Transparency","All","Development","Have you checked that, where PII or sensitive data is approved for the use case, the user data is collected and stored in a secure manner, and appropriate consent has been secured?"]

{"sheet":"Risk Guidance - Post-Monitoring","index":4,"n_rows":15,"n_cols":6,"rows_kept":15,"cut":false,"hidden":false,"merged":[[0,0,0,5]]}
["AI Risk Guidance"]
["Risk","Area","Applies to","AI Lifecycle Stage\n","Question","Response"]
["Impact on Cultural Diversity","Inclusivity","All","Post-monitoring","Have you evaluated the output of the AI System when it is asked questions based on religion and race?"]
["Lack of Accountability","Accountability","All","Post-monitoring","Who will be responsible for addressing any system performance issues identified through continuous risk monitoring?"]
["System Hardware Issues","Security","Custom AI","Post-monitoring","Have you considered an incident management plan in case hardware issues disrupt the availability of, or service provided by, the solution? "]
["System Lifecycle Issues","Transparency","All","Post-monitoring","How are known and emerging risks are managed across the lifecycle of the solution?"]
["Technology Readiness","Accountability","All","Post-monitoring","How are technical updates to the software, library or 3rd party platform incorporated when they occur?"]
["Security: General","Security","All","Post-monitoring","\nHow are security risks evaluated and monitored?"]
["Incorrect Risk Testing","Security","All","Post-monitoring","Have you checked that any techniques and metrics for measuring risk are appropriate to the management of the related risk?"]
["Lack of Fairness: General","Inclusivity","All","Post-monitoring","Have you reviewed the AI systems to ensure that it does not reflect any intrinsic biases?"]
["Over- or Under-Reliance","Accountability","All","Post-monitoring","Have you checked that the intended users of the solution have the appropriate competencies, guidelines and training to make decisions using the outputs?"]
["Lack of Transparency: General","Transparency","All","Post-monitoring","Have you checked that those interacting with the solution have access to appropriate information regarding the use of AI in the solution and it's capabilities and limitations?"]
["Lack of Data Transparency","Transparency","Custom AI","Post-monitoring","If applicable, have training and tuning datasets been documented, in a structured manner with clear explanations of the fields, and approporaitely stored?"]
["Inaccessible Training Data","Transparency","Custom AI","Post-monitoring","Has the training data been made accessible to those with appropriate permissions?"]
["Untraceable Attribution","Transparency","Custom AI","Post-monitoring","Is it possible for those with appropriate access permissions to utilise source attribution techniques to validate and audit the outputs of the solution?"]

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["AI Risk List"]
["Type of Risk","Main Risk Category","Risk","Area","Definition/ Explanation"]
["Strategic Risk","Image and Brand Reputation","Impact on Cultural Diversity","Inclusivity","AI systems might overly represent certain cultures that result in a homogenisation of culture and thoughts."]
[null,null,"Environmental Impact","Inclusivity","The use of AI can cause effects from an environmental point of view. The use of AI can have positive effects on the environment. For example, an AI system can be used to reduce nitrogen oxide in a gas turbine. The use of AI can also have a negative effect on the environment due to the extensive use of resources. For example, the training phase of some AI systems requires computing resources and can consume substantial amounts of electrical power."]
[null,"Organisational Structure","Lack of Accountability","Accountability","Accountability refers both to a characteristic of organisations and to a system:  \n- Organisational accountability means that an organisation takes responsibility for its decisions and actions by explaining them and being answerable for them to governing bodies, to legal authorities and more broadly to stakeholders.  \n- System accountability relates to being able to trace the decisions and actions of an entity to that entity. The use of AI can change existing accountability frameworks. Where previously persons performed actions for which they would be held accountable, such actions can now be fully or partially performed by AI systems."]
[null,null,"Lack of AI Expertise","Accountability","AI systems and their development are different from non-AI software solutions. A selection of dedicated specialists with inter-disciplinary skillsets and expertise in assessing, developing and deploying AI systems is needed. Organisations should ensure that people with such expertise are engaged in the development and specification of AI systems. Expertise of AI should extend to the end users of AI systems. Users should have sufficient understanding of how the AI system functions and are empowered to detect and override erroneous decisions or outputs."]
[null,"Control & Supervision","Level of Automation - Loss of Oversight","Accountability","AI systems can operate with different levels of automation. They can range from no automation where an operator fully controls the system to fully automated systems. AI systems are often automated systems. Depending on the specific use case, the automated decisions of such systems can have an effect on various areas of concern such as safety, fairness or security. For a level of automation where an external agent must be ready when necessary, the handover from the system to the agent can be a risk source (e.g. time constraints, attention of the agent)."]
[null,null,"Lack of Maintainability","Accountability","Maintainability is related to the ability of the organisation to handle modifications of the AI system in order to correct defects or adjust to new requirements. Because AI systems based on ML are trained and do not follow a rule-based approach, the maintainability of an AI system and its implications should be investigated."]
[null,null,"Complexity of Environment","Accountability","The complexity of the environment of an AI system determines the range of potential situations an AI system is intended to support in its operational context. Certain AI technologies like ML are specifically suited to handle complex environments and are therefore often used for systems used for complex environments like automated driving. A great challenge however is to identify in the design and development process all relevant situations that the system is expected to handle and that the training and test data cover all these situations. Hence, complex environments can result in additional risks relative to simple environments."]
["Compliance Risk","Intellectual Property Violation","Confidential Information in Data","Compliance","Confidential information might be included as part of the data that is used to train or tune the model."]
[null,null,"Data Usage Rights Restrictions","Compliance","Terms of service, license compliance, or other IP issues may restrict the ability to use certain data for building models."]
[null,null,"IP Information in Prompt","Compliance","Copyrighted information or other intellectual property might be included as a part of the prompt that is sent to the model."]
[null,null,"Confidential Data in Prompt","Security","Confidential information might be included as a part of the prompt that is sent to the model."]
[null,null,"Revealing Confidential Information","Security","When confidential information is used in training data, fine-tuning data, or as part of the prompt, models might reveal that data in the generated output. Revealing confidential information is a type of data leakage."]
[null,null,"Copyright Infringement","Compliance","A model might generate content that is similar or identical to existing work protected by copyright or covered by open-source license agreement."]
[null,null,"Generating Content Ownership & IP","Compliance","Legal uncertainty about the ownership and intellectual property rights of AI-generated content."]
[null,"Regulations","Data Usage Restrictions","Compliance","Laws and other restrictions can limit or prohibit the use of some data for specific AI use cases."]
[null,null,"Data Acquisition Restrictions","Compliance","Laws and other regulations might limit the collection of certain types of data for specific AI use cases."]
[null,null,"Data Transfer Restrictions","Compliance","Laws and other restrictions can limit or prohibit transferring data."]
[null,null,"Data Privacy Rights Alignment","Compliance","Existing laws could include providing data subject rights such as opt-out, right to access, and right to be forgotten."]
[null,null,"Clinical Data Restrictions","Compliance","Using clinical data without appropriate approval, governance, or safeguards may breach regulatory, ethical, or legal obligations. The definition of clinical data should be determined by each organisation based on its specific context."]
["Operations Risk","Sourcing Risk","Third Party Relationships","Accountability","Lack of alignment between supplier and organisation's approach to the responsible development or use of the AI Systems; this risk include the absence of  adequate contractual obligations and of needed documentation about AI systems purchased."]
[null,"IT Systems Risk","Hallucination","Transparency","Hallucinations generate factually inaccurate or untruthful content with respect to the model’s training data or input. This is also sometimes referred to lack of faithfulness or lack of groundedness."]
[null,null,"Prompt Leaking","Security","A prompt leak attack attempts to extract a model's system prompt (also known as the system message)."]
[null,null,"Prompt Injection Attack","Security","A prompt injection attack forces a generative model that takes a prompt as input to produce unexpected output by manipulating the structure, instructions, or information contained in its prompt."]
[null,null,"Evasion Attack","Security","Evasion attacks attempt to make a model output incorrect results by slightly perturbing the input data that is sent to the trained model."]
[null,null,"Poor Model Accuracy","Transparency","Poor model accuracy occurs when a model’s performance is insufficient to the task it was designed for. Low accuracy might occur if the model is not correctly engineered, or there are changes to the model’s expected inputs."]
[null,null,"System Hardware Issues","Security","Risk sources related to hardware include hardware errors based on defective components or transferring trained ML models between different systems."]
[null,null,"System Lifecycle Issues","Transparency","Sources of risks can appear over the entire AI System lifecycle (flaws in design, inadequate deployment, lack of maintenance, issues with decommissioning)."]
[null,null,"Technology Readiness","Accountability","Technology readiness indicates how mature a given technology is in a given application context. Less mature technologies used in the development and application of AI systems can impose risks that are unknown to the organisation or are hard to assess. For mature technologies a larger variety of experience data can be available, making risks easier to identify and to assess. However, there is also a risk of complacency and technical debt if technologies are mature."]
[null,"Security Risk","Security: General","Security","Information security risk management in the context of AI, and in particular with regard to AI systems based on ML approaches, have several new issues such as data poisoning, adversarial attacks and model stealing should be considered beyond classical information and system security concerns."]
[null,null,"Data Poisoning","Security","A type of adversarial attack where an adversary or malicious insider injects intentionally corrupted, false, misleading, or incorrect samples into the training or fine-tuning datasets."]
[null,null,"Jailbreaking","Security","A jailbreaking attack attempts to break through the guardrails that are established in the model to perform restricted actions."]
[null,null,"Prompt Priming","Security","Because generative models tend to produce output like the input provided, the model can be prompted to reveal specific kinds of information. For example, adding personal information in the prompt increases its likelihood of generating similar kinds of personal information in its output. If personal data was included as part of the model’s training, there is a possibility it could be revealed."]
[null,null,"Incorrect Risk Testing","Accountability","A metric selected to measure or track a risk is incorrectly selected, incompletely measuring the risk, or measuring the wrong risk for the given context."]
[null,null,"Lack of Robustness","Security","Robustness is related to the ability of a system to maintain its level of performance under the various circumstances of its usage. The degree to which an AI system or related component can function correctly in the presence of invalid inputs or stressful environmental conditions should be taken into consideration as well as the ability to reproduce measures and results. Robustness poses new challenges in the context of AI systems. Neural network architectures represent a specific challenge as they are both hard to explain and sometimes have unexpected behaviour due to their nonlinear nature. Characterising the robustness of neural networks is an open area of research, and there are limitations to both testing and verification approaches."]
[null,"Health & People Risk","Lack of Fairness: General","Inclusivity","The use of AI systems for automated decision-making can be unfair to specific persons or groups of persons. Unfair outcomes have a number of causes such as bias in objective functions, imbalanced data sets, and human biases in training data and in providing feedback to systems. Unfairness can also be caused by a bias issue in the product concept, the problem formulation or choices about when and where to deploy AI systems."]
[null,null,"Improper Usage","Transparency","Improper usage occurs when a model is used for a purpose that it was not originally designed for."]
[null,null,"Spreading Disinformation","Transparency","Generative AI models might be used to intentionally create misleading or false information to deceive or influence a targeted audience."]
[null,null,"Decision Bias","Inclusivity","Decision bias occurs when one group is unfairly advantaged over another due to decisions of the model. This might be caused by biases in the data used and also amplified as a result of the bias in a model’s training data."]
[null,null,"Output Bias","Inclusivity","Generated content might unfairly represent certain groups or individuals."]
[null,null,"Toxic Output","Transparency","Toxic output occurs when the model produces hateful, abusive, and profane (HAP) or obscene content. This also includes behaviours like bullying."]
[null,null,"Lack of Testing Diversity","Inclusivity","AI model risks are socio-technical, so their testing needs input from a broad set of disciplines and diverse testing practices."]
[null,null,"Impact on Affected Communities","Inclusivity","It is important to include the perspectives or concerns of communities that are affected by model outcomes when designing and building models. Failing to include these perspectives makes it difficult to understand the relevant context for the model and to engender trust within these communities."]
[null,null,"Safety","Inclusivity","The use of AI systems can introduce new safety threats. Safety relates to the expectation that a system does not, under defined conditions, lead to a state in which human life, health, property or the environment is endangered. Use of AI systems in automated vehicles, manufacturing devices, and robots can introduce risks related to safety. Specific standards for particular application domains (e.g. the design of machinery, transport, medical devices) should be taken into account for AI systems in these domains."]
[null,null,"Impact on Human Agency","Inclusivity","AI might affect the individuals’ ability to make choices and act independently in their best interests."]
[null,null,"Dangerous Use","Accountability","Generative AI models might be used with the sole intention of harming people."]
[null,null,"Harmful Output","Security","For AI systems, protecting privacy includes protecting the data used for building and operating the AI system, ensuring that the AI system cannot be used to give unwarranted access to its data, and protecting access to models personalised for an individual."]
[null,null,"Incomplete Advice","Transparency","When a model provides advice without having enough information, resulting in possible harm if the advice is followed."]
[null,null,"Harmful Code Generation","Transparency","Models might generate code that causes harm or unintentionally affects other systems."]
[null,null,"Over- or Under-Reliance","Accountability","In AI-assisted decision-making tasks, reliance measures how much a person trusts (and potentially acts on) a model’s output. Over-reliance occurs when a person puts too much trust in a model, accepting a model’s output when the model’s output is likely incorrect. Under-reliance is the opposite, where the person doesn’t trust the model but should."]
[null,"Information Security Risk","Lack of Transparency: General","Transparency","Lack of model/system transparency is due to insufficient documentation of the model design, development, and evaluation process and the absence of insights into the inner workings of the model."]
[null,null,"Uncertain Data Provenance","Transparency","Data provenance refers to tracing history of data, which includes its ownership, origin, and transformations. Without standardised and established methods for verifying where the data came from, there are no guarantees that the data is the same as the original source and has the correct usage terms."]
[null,null,"Incomplete Usage Definition","Transparency","Since foundation models can be used for many purposes, a model’s intended use is important for defining the relevant risks of that model. As the use changes, the relevant risks might correspondingly change."]
[null,null,"Lack of Data Transparency","Transparency","Lack of data transparency is due to insufficient documentation of training or tuning dataset details."]
[null,null,"Non-Disclosure","Transparency","Content might not be clearly disclosed as AI generated."]
[null,null,"Unreliable Source Attribution","Transparency","Source attribution is the AI system's ability to describe from what training data it generated a portion or all its output. Since current techniques are based on approximations, these attributions might be incorrect."]
[null,null,"Lack of Explainability","Transparency","AI system explainability relates to an ability to rationalise and help to understand how, for a specific system, its outcome has been generated. Many times, explanations for model output decisions might be difficult, imprecise, or not possible to obtain."]
[null,null,"Inaccessible Training Data","Transparency","AI system explainability relates to an ability to rationalise and help to understand how, for a specific system, its outcome has been generated. Many times, explanations for model output decisions might be difficult, imprecise, or not possible to obtain."]
[null,null,"Untraceable Attribution","Transparency","The content of the training data used for generating the model’s output is not accessible. Without the ability to access training data content, the possibility of using source attribution techniques can be severely limited or impossible. This makes it difficult for users, model validators, and auditors to understand and trust the model."]
[null,null,"Availability/ Quality of Training & Test Data","Transparency","AI systems based on ML need training and test data in order to train and verify the systems for the intended behaviour. The deployed AI system operates on production data. The training, test and production data should be fit to the intended behaviour with respect to data type and quality. Training and test data should be validated for their currency and relevance for the intended purpose. The amount of training and test data required can vary based on the intended functionality and complexity of the environment. The training and test data should have sufficiently diverse features in order to provide strong predictive power for the AI system. Furthermore, consistency should be ensured across training and test data, while using independent datasets when applicable. It is possible that training and test data is not available in the company and is sourced externally. Data quality should be ensured also in that case"]
[null,null,"Improper Data Curation","Transparency","Improper collection and preparation of training or tuning data includes data label errors and by using data with conflicting information or misinformation."]
[null,null,"Improper Retraining","Transparency","Using undesirable output (for example, inaccurate, inappropriate, and user content) for retraining purposes can result in unexpected model behaviour."]
[null,null,"Unrepresentative Data","Inclusivity","Unrepresentative data occurs when the data used by a system or the training / fine-tuning data is not sufficiently representative of the underlying population or does not measure the phenomenon of interest."]
[null,null,"Data Contamination","Security","Data contamination occurs when incorrect data is used for training. For example, data that is not aligned with model’s purpose or data that is already set aside for other development tasks such as testing and evaluation."]
[null,null,"Unrepresentative Risk Testing","Inclusivity","Testing is unrepresentative when the test inputs are mismatched with the inputs that are expected during deployment."]
[null,null,"Lack of Privacy","Transparency","For AI systems, protecting privacy includes protecting the data used for building and operating the AI system, ensuring that the AI system cannot be used to give unwarranted access to its data, and protecting access to models personalized for an individual or that can be used to infer information or characteristics of similar individuals. Improper collections, uses and disclosures of personal information can also have direct impacts on fundamental human rights such as discrimination and freedom of expression and information. Impacts on ethic principles in terms of respect of human values, and human dignity should also be considered. Inclusion or presence of personal identifiable information (PII) and sensitive personal information (SPI) in the data used for training or fine tuning the model might result in unwanted disclosure of that information."]
[null,null,"Reidentification","Transparency","Even with the removal of personal identifiable information (PII) and sensitive personal information (SPI) from data, it might be possible to identify persons due to correlations to other features available in the data."]
[null,null,"Personal Information in Prompt","Security","If personal information or sensitive personal information is included in the prompt, it might be unintentionally disclosed in the models’ output. In addition to accidental disclosure, prompt data might be stored or later used for other purposes like model evaluation and retraining, and might appear in their output if not properly removed. "]
[null,null,"Exposing Personal Information","Transparency","When personal identifiable information (PII) or sensitive personal information (SPI) are used in training data, fine-tuning data, or as part of the prompt, models might reveal that data in the generated output. Revealing personal information is a type of data leakage."]

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["Q.No.","Questions","Choices","Sample use-case for QR","AI Risk","Why the use cases may trigger such risks","Risk Category Assignment"]
[1,"Which geographies will the system be deployed in?","1. Qatar"]
[null,null,"2. EU"]
[null,null,"3. NA"]
[null,null,"4. Other"]
[null,null,"5. Global"]
[2,"Does the solution use/will use Artificial Intelligence? (refer to the AI Policy for the definition of AI and AI Systems)","1. Yes"]
[null,null,"2. No"]
[null,"Does the solution use Generative AI?","1. No"]
[null,null,"2. Yes, in part (a Gen AI model is part of the proposed solution, in combination with Classical AI components)"]
[null,null,"3. Yes, in entirety (there are no other AI components)"]
[3,"Are you developing or placing on the market (including deploying within Qatar Airways) the project, system or solution that: \n(please select all that apply)","1.Deploys subliminal, manipulative, or deceptive techniques to distort behavior and impair informed decision-making, causing significant harm.  "]
[null,null,"2. Exploits vulnerabilities related to age, disability, or socio-economic circumstances to distort behavior, causing significant harm.  "]
[null,null,"3. Biometric categorization systems infer sensitive attributes (race, political opinions, trade union membership, religious or philosophical beliefs, sex life, or sexual orientation), except labeling or filtering of lawfully acquired biometric datasets or when law enforcement categorizes biometric data.  "]
[null,null,"4. Social scoring, i.e., evaluating or classifying individuals or groups based on social behavior or personal traits, causes detrimental or unfavorable treatment of those people.  "]
[null,null,"5. Assessing the risk of an individual committing criminal offenses solely based on profiling or personality traits, except when used to augment human assessments based on objective, verifiable facts directly linked to criminal activity. "]
[null,null,"6. Compiling facial recognition databases by untargeted scraping of facial images from the Internet or CCTV footage in publicly accessible spaces for law enforcement, except when:\n6a - searching for missing persons, abduction victims, and people who have been human trafficked or sexually exploited \n\n6b- preventing substantial and imminent threat to life, or foreseeable terrorist attack "]
[null,null,"7. Inferring emotions in workplaces or educational institutions, except for medical or safety reasons. "]
[null,null,"8. ‘Real-time’ remote biometric identification (RBI) in publicly accessible spaces for law enforcement, except when: \n8a -  searching for missing persons, abduction victims, and people who have been human trafficked or sexually exploited\n \n8b - preventing substantial and imminent threat to life, or foreseeable terrorist attack\n \n8c - identifying suspects in serious crimes (e.g., murder, rape, armed robbery, narcotic and illegal weapons trafficking, organized crime, and environmental crime, etc.). "]
[null,null,"None"]
[4,"Are you developing or placing on the market (including deploying within Qatar Airways) the project, system or solution that: \n(please select all that apply)","1. Used as a safety component "]
[null,null,"2. A product covered by EU laws in Annex I of the EU AI Act and is required to undergo a third-party conformity assessment under the Annex I laws "]
[null,null," 3. Non-banned biometrics:  \n\n3a - A remote biometric identification system, excluding biometric verification, that confirms a person is who they claim to be.  \n\n3b - A biometric categorization system inferring sensitive or protected attributes or characteristics.  \n\n3c - An emotion recognition system "]
[null,null,"4. Critical Infrastructure:  It is a safety component in managing and operating critical digital infrastructure, road traffic and the supply of water, gas, heating and electricity. ",null,null,null,null,null,null,"Questions for high risk"]
[null,null,"5.Education and Vocational Training: \n\n5a - An AI system determining access, admission or assignment to educational and vocational training institutions at all levels. \n\n5b - Evaluating learning outcomes, including those used to steer the student’s learning process. \n\n5c - Assessing the appropriate level of education for an individual. \n\n5d - Monitoring and detecting prohibited student behavior during tests. ",null,null,null,null,null,null,"1. Used as a safety component",null,"1.Deploys subliminal, manipulative, or deceptive techniques to distort behavior and impair informed decision-making, causing significant harm.  "]
[null,null,"6.  Employment, workers management and access to self-employment: \n6a - Used for recruitment or selection, particularly targeted job ads, analyzing and filtering applications, and evaluating candidates. \n\n6b - Promotion and termination of contracts, allocating tasks based on personality traits and characteristics, and monitoring and evaluating performance. \n\n",null,null,null,null,null,null,"2. A product covered by EU laws in Annex I of the EU AI Act and is required to undergo a third-party conformity assessment under the Annex I laws",null,"2. Exploits vulnerabilities related to age, disability, or socio-economic circumstances to distort behavior, causing significant harm.  "]
[null,null," 7. Access to and enjoyment of essential public and private services:   \n7a - Used by public authorities to assess eligibility for benefits and services, including their allocation, reduction, revocation, or recovery. \n\n7b -Used to evaluate creditworthiness, except when detecting financial fraud. \n\n7c - Used to evaluate and classify emergency calls, including dispatch prioritizing of police, firefighters, medical aid and urgent patient triage service. \n\n7d - Used to conduct risk assessments and pricing in health and life insurance. ",null,null,null,null,null,null,"3a. Non-banned biometrics: A remote biometric identification system, excluding biometric verification, that confirms a person is who they claim to be",null,"3. Biometric categorization systems infer sensitive attributes (race, political opinions, trade union membership, religious or philosophical beliefs, sex life, or sexual orientation), except labeling or filtering of lawfully acquired biometric datasets or when law enforcement categorizes biometric data.  "]
[null,null,"8.Law enforcement: \n8a - Used to assess an individual’s risk of becoming a crime victim. \n\n8b - Used to interpret or conduct polygraphs. \n\n8c - Used to evaluate evidence reliability during criminal investigations or prosecutions. \n\n8d - Used to assess an individual’s risk of offending or re-offending, not solely based on profiling or assessing personality traits or past criminal behavior. \n\n8e - Used to profile during criminal detections, investigations or prosecutions. ",null,null,null,null,null,null,"3b. Non-banned biometrics: A biometric categorization system inferring sensitive or protected attributes or characteristics",null,"4. Social scoring, i.e., evaluating or classifying individuals or groups based on social behavior or personal traits, causes detrimental or unfavorable treatment of those people.  "]
[null,null,"9.Migration, asylum and border control management: \n9a - Used to interpret or conduct polygraphs or assessments of irregular migration or health risks. \n\n9b - Examination of applications for asylum, visa and residence permits, or associated complaints related to eligibility.  \n\n9c - Used to detect, recognize or identify individuals, except verifying travel documents. ",null,null,null,null,null,null,"3c. Non-banned biometrics: An emotion recognition system",null,"5. Assessing the risk of an individual committing criminal offenses solely based on profiling or personality traits, except when used to augment human assessments based on objective, verifiable facts directly linked to criminal activity. "]
[null,null,"10. Administration of justice and democratic processes: \n\n10a -  Used in researching and interpreting facts and applying the law to concrete facts or used in alternative dispute resolution. \n\n10b - Used in influencing elections and referenda outcomes or voting behavior, excluding outputs that do not directly interact with people, like tools used to organize, optimize and structure political campaigns. ",null,null,null,null,null,null,"4. Critical Infrastructure: It is a safety component in managing and operating critical digital infrastructure, road traffic and the supply of water, gas, heating and electricity",null,"6. Compiling facial recognition databases by untargeted scraping of facial images from the Internet or CCTV footage in publicly accessible spaces for law enforcement."]
[null,null,"11. None of the above",null,null,null,null,null,null,"5a. Education and Vocational Training: An AI system determining access, admission or assignment to educational and vocational training institutions at all levels",null,"7. Inferring emotions in workplaces or educational institutions, except for medical or safety reasons. "]
[5,"Will the project/solution be used, or could it be used for, or involved in, any of the following activities? (Please select all that apply)","1. Generate patterns, recommendations or inferences that relate to groups (e.g. based on gender, race, ethnicity, political affiliation, sexual orientation), or generate outcomes that treat individuals or groups differently from each other based on this.",null,null,null,null,null,null,"5b. Education and Vocational Training: Evaluating learning outcomes, including those used to steer the student’s learning process",null,"8. Real-time remote biometric identification (RBI) in publicly accessible spaces for law enforcement, except when: "]
[null,null,"2. Generate an outcome or inferences based on a group’s shared interests (e.g. segmentation, profiling etc.). *",null,null,null,null,null,null,"5c. Education and Vocational Training: Assessing the appropriate level of education for an individual",null,"9. None"]
[null,null,"3. Generate inferences, behavioural insights or personalised profiles of individuals.",null,null,null,null,null,null,"5d. Education and Vocational Training: Monitoring and detecting prohibited student behavior during tests"]
[null,null,"4. Generate an outcome that can affect an individual’s or population’s human rights, health, or economic well-being.",null,null,null,null,null,null,"6a. Employment, workers management and access to self-employment: Used for recruitment or selection, particularly targeted job ads, analyzing and filtering applications, and evaluating candidates"]
[null,null,"5. Conduct or enable surveillance of individuals or groups",null,null,null,null,null,null,"6b. Employment, workers management and access to self-employment: Promotion and termination of contracts, allocating tasks based on personality traits and characteristics, and monitoring and evaluating performance"]
[null,null,"6. Enable or automate content moderation ",null,null,null,null,null,null,"7a. Access to and enjoyment of essential public and private services: Used by public authorities to assess eligibility for benefits and services, including their allocation, reduction, revocation, or recovery"]
[null,null,"7. Potentially harm people when used for military/defence objectives (such as in developing weapons, etc.)",null,null,null,null,null,null,"7b. Access to and enjoyment of essential public and private services: Used to evaluate creditworthiness, except when detecting financial fraud"]
[null,null,"8. Enable or facilitate gambling products or services",null,null,null,null,null,null,"7c. Access to and enjoyment of essential public and private services: Used to evaluate and classify emergency calls, including dispatch prioritizing of police, firefighters, medical aid and urgent patient triage service"]
[null,null,"9. Influence or manipulate people's behaviours, or help spread misinformation through social media ",null,null,null,null,null,null,"7d. Access to and enjoyment of essential public and private services: Used to conduct risk assessments and pricing in health and life insurance"]
[null,null,"10. Potentially impact people's health or their access to health services when used for Health or Pharmaceuticals products or services",null,null,null,null,null,null,"8a. Law enforcement: Used to assess an individual’s risk of becoming a crime victim"]
[null,null,"11. None of the above",null,null,null,null,null,null,"8b. Law enforcement: Used to interpret or conduct polygraphs"]
[6,"Does the AI System analyse PII data?","1. Yes",null,null,null,null,"In DPIA",null,"8c. Law enforcement: Used to evaluate evidence reliability during criminal investigations or prosecutions"]
[null,null,"2. No",null,null,null,null,null,null,"8d. Law enforcement: Used to assess an individual’s risk of offending or re-offending, not solely based on profiling or assessing personality traits or past criminal behavior"]
[null,null,null,null,null,null,null,null,null,"8e. Law enforcement: Used to profile during criminal detections, investigations or prosecutions"]
[null,null,null,null,null,null,null,null,null,"9a. Migration, asylum and border control management: Used to interpret or conduct polygraphs or assessments of irregular migration or health risks"]
[null,null,null,null,null,null,null,null,null,"9b. Migration, asylum and border control management: Examination of applications for asylum, visa and residence permits, or associated complaints related to eligibility"]
[null,null,null,null,null,null,null,null,null,"9c. Migration, asylum and border control management: Used to detect, recognize or identify individuals, except verifying travel documents"]
[null,null,null,null,null,null,null,null,null,"10a. Administration of justice and democratic processes: Used in researching and interpreting facts and applying the law to concrete facts or used in alternative dispute resolution"]
[null,null,null,null,null,null,null,null,null,"10b. Administration of justice and democratic processes: Used in influencing elections and referenda outcomes or voting behavior, excluding outputs that do not directly interact with people, like tools used to organize, optimize and structure political campaigns"]
[null,null,null,null,null,null,null,null,null,"11. None of the above"]