Table of Contents
Safe and Effective Systems
You should be protected from unsafe or ineffective systems.
Algorithmic Discrimination Protections
You should not face discrimination by algorithms and systems should be used and designed in an equitable way.
Data Privacy
You should be protected from abusive data practices via built-in protections and you should have agency over how data about you is used.
Notice and Explanation
You should know that an automated system is being used and understand how and why it contributes to outcomes that impact you.
Human Alternatives, Consideration, and Fallback
You should be able to opt out, where appropriate, and have access to a person who can quickly consider and remedy problems you encounter.
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US: Blueprint for an AI Bill of Rights
As adopted in October 2022
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Safe and Effective Systems
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You should be protected from unsafe or ineffective systems.
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Automated systems should be developed with consultation from diverse communities, stakeholders, and domain experts to identify concerns, risks, and potential impacts of the system.
Systems should undergo pre-deployment testing, risk identification and mitigation, and ongoing monitoring that demonstrate they are safe and effective based on their intended use, mitigation of unsafe outcomes including those beyond the intended use, and adherence to domain-specific standards.
Outcomes of these protective measures should include the possibility of not deploying the system or removing a system from use.
Automated systems should not be designed with an intent or reasonably foreseeable possibility of endangering your safety or the safety of your community.
They should be designed to proactively protect you from harms stemming from unintended, yet foreseeable, uses or impacts of automated systems.
You should be protected from inappropriate or irrelevant data use in the design, development, and deployment of automated systems, and from the compounded harm of its reuse.
Independent evaluation and reporting that confirms that the system is safe and effective, including reporting of steps taken to mitigate potential harms, should be performed and the results made public whenever possible.
Algorithmic Discrimination Protections
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You should not face discrimination by algorithms and systems should be used and designed in an equitable way.
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Algorithmic discrimination occurs when automated systems contribute to unjustified different treatment or impacts disfavoring people based on their race, color, ethnicity, sex (including pregnancy, childbirth, and related medical conditions, gender identity, intersex status, and sexual orientation), religion, age, national origin, disability, veteran status, genetic information, or any other classification protected by law.
Depending on the specific circumstances, such algorithmic discrimination may violate legal protections.
Designers, developers, and deployers of automated systems should take proactive and continuous measures to protect individuals and communities from algorithmic discrimination and to use and design systems in an equitable way.
This protection should include proactive equity assessments as part of the system design, use of representative data and protection against proxies for demographic features, ensuring accessibility for people with disabilities in design and development, pre-deployment and ongoing disparity testing and mitigation, and clear organizational oversight.
Independent evaluation and plain language reporting in the form of an algorithmic impact assessment, including disparity testing results and mitigation information, should be performed and made public whenever possible to confirm these protections.
Data Privacy
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You should be protected from abusive data practices via built-in protections and you should have agency over how data about you is used.
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You should be protected from violations of privacy through design choices that ensure such protections are included by default, including ensuring that data collection conforms to reasonable expectations and that only data strictly necessary for the specific context is collected.
Designers, developers, and deployers of automated systems should seek your permission and respect your decisions regarding collection, use, access, transfer, and deletion of your data in appropriate ways and to the greatest extent possible; where not possible, alternative privacy by design safeguards should be used.
Systems should not employ user experience and design decisions that obfuscate user choice or burden users with defaults that are privacy invasive.
Consent should only be used to justify collection of data in cases where it can be appropriately and meaningfully given.
Any consent requests should be brief, be understandable in plain language, and give you agency over data collection and the specific context of use; current hard-to-understand notice-and-choice practices for broad uses of data should be changed.
Enhanced protections and restrictions for data and inferences related to sensitive domains, including health, work, education, criminal justice, and finance, and for data pertaining to youth should put you first.
In sensitive domains, your data and related inferences should only be used for necessary functions, and you should be protected by ethical review and use prohibitions.
You and your communities should be free from unchecked surveillance; surveillance technologies should be subject to heightened oversight that includes at least pre-deployment assessment of their potential harms and scope limits to protect privacy and civil liberties.
Continuous surveillance and monitoring should not be used in education, work, housing, or in other contexts where the use of such surveillance technologies is likely to limit rights, opportunities, or access.
Whenever possible, you should have access to reporting that confirms your data decisions have been respected and provides an assessment of the potential impact of surveillance technologies on your rights, opportunities, or access.
Notice and Explanation
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You should know that an automated system is being used and understand how and why it contributes to outcomes that impact you.
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Designers, developers, and deployers of automated systems should provide generally accessible plain language documentation including clear descriptions of the overall system functioning and the role automation plays, notice that such systems are in use, the individual or organization responsible for the system, and explanations of outcomes that are clear, timely, and accessible.
Such notice should be kept up-to-date and people impacted by the system should be notified of significant use case or key functionality changes.
You should know how and why an outcome impacting you was determined by an automated system, including when the automated system is not the sole input determining the outcome.
Automated systems should provide explanations that are technically valid, meaningful and useful to you and to any operators or others who need to understand the system, and calibrated to the level of risk based on the context.
Reporting that includes summary information about these automated systems in plain language and assessments of the clarity and quality of the notice and explanations should be made public whenever possible.
Human Alternatives, Consideration, and Fallback
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You should be able to opt out, where appropriate, and have access to a person who can quickly consider and remedy problems you encounter.
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You should be able to opt out from automated systems in favor of a human alternative, where appropriate.
Appropriateness should be determined based on reasonable expectations in a given context and with a focus on ensuring broad accessibility and protecting the public from especially harmful impacts.
In some cases, a human or other alternative may be required by law.
You should have access to timely human consideration and remedy by a fallback and escalation process if an automated system fails, it produces an error, or you would like to appeal or contest its impacts on you.
Human consideration and fallback should be accessible, equitable, effective, maintained, accompanied by appropriate operator training, and should not impose an unreasonable burden on the public.
Automated systems with an intended use within sensitive domains, including, but not limited to, criminal justice, employment, education, and health, should additionally be tailored to the purpose, provide meaningful access for oversight, include training for any people interacting with the system, and incorporate human consideration for adverse or high-risk decisions.
Reporting that includes a description of these human governance processes and assessment of their timeliness, accessibility, outcomes, and effectiveness should be made public whenever possible.
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What OECD AI Principle are emphasised?
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1.1 - Inclusive growth 1.2 - Human-centred values 1.3 - Transparency + explainability 1.4 - Robustness, security + safety 1.5 - Accountability
What is the policy area?
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Data governance Design and testing standards Regulatory Compliance and Transparency Authorisation, registration and licensing Other operating conditions Consumer protection
Which policy instrument is applied?
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Data protection regulation Design requirement Documentation requirement Legal due diligence requirement Notification requirement Organisational requirement Performance monitoring requirement Public disclosure requirement Risk management requirement Testing requirement Prohibition of goods and services Consumer protection, nes User/subject right
Which regulatory requirements are imposed?
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General data protection requirement Data composition requirement Human oversight requirement System safety requirement Other system design requirement General testing requirement Bias testing requirement General performance monitoring requirement Risk monitoring requirement Right to information on system-in-use Right to object to AI-assisted service or decision (“optionality”) Right to contest AI-assisted decision Right to request explanation of AI-assisted decision Right to request human determination of AI-assisted decision Human oversight documentation requirement Technical reference material documentation requirement Testing results documentation requirement Other documentation requirement System-in-use disclosure requirement Human oversight disclosure requirement Technical reference material disclosure requirement Testing results disclosure requirement Other public disclosure requirement Other notification requirement Risk assessment requirement Impact assessment requirement Prohibition of goods and services Other organisational requirement Risk management requirement Data subject right Consumer protection, nes Non-discrimination requirement
Are there risk-based requirements?
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Which AI model type is regulated?
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What's the legal nature of the provision?
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