Model: lw2134/policy_gte_large_5
Author: lw2134
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, tensorboard, onnx, safetensors, new, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:224, loss:MultipleNegativesRankingLoss, custom_code, arxiv:1908.10084, arxiv:1705.00652, base_model:Alibaba-NLP/gte-large-en-v1.5, base_model:quantized:Alibaba-NLP/gte-large-en-v1.5, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (quantized): id: Alibaba-NLP/gte-large-en-v1.5
Card metadata: base_model: Alibaba-NLP/gte-large-en-v1.5
library_name: sentence-transformers
metrics: cosine_accuracy@1, cosine_accuracy@3, cosine_accuracy@5, cosine_accuracy@10, cosine_precision@1, cosine_precision@3, cosine_precision@5, cosine_precision@10, cosine_recall@1, cosine_recall@3, cosine_recall@5, cosine_recall@10, cosine_ndcg@10, cosine_mrr@10, cosine_map@100, dot_accuracy@1, dot_accuracy@3, dot_accuracy@5, dot_accuracy@10, dot_precision@1, dot_precision@3, dot_precision@5, dot_precision@10, dot_recall@1, dot_recall@3, dot_recall@5, dot_recall@10, dot_ndcg@10, dot_mrr@10, dot_map@100
pipeline_tag: sentence-similarity
tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:224, loss:MultipleNegativesRankingLoss
widget: source_sentence: What are some of the mental health impacts associated with the increased use of surveillance technologies in schools and workplaces, as mentioned in the context information?
sentences: 15 GV-1.3-004 Obtain input from stakeholder communities to identify unacceptable use , in 
accordance with activities in the AI RMF Map function . CBRN Information or Capabilities ; 
Obscene, Degrading, and/or 
Abusive Content ; Harmful Bias 
and Homogenization ; Dangerous, 
Violent, or Hateful Content  
GV-1.3-005 Maintain an updated hierarch y of identiﬁed and expected GAI risks connected to 
contexts of GAI model advancement and use, potentially including specialized risk 
levels for GAI systems that address issues such as model collapse and algorithmic 
monoculture.  Harmful Bias and Homogenization  
GV-1.3-006 Reevaluate organizational risk tolerances to account for unacceptable negative risk 
(such as where signiﬁcant negative impacts are imminent, severe harms are actually occurring, or large -scale risks could occur); and broad GAI negative risks, 
including: Immature safety or risk cultures related to AI and GAI design, development and deployment, public information integrity risks, including impacts on democratic processes, unknown long -term performance characteristics of GAI.  Information Integrity ; Dangerous , 
Violent, or Hateful Content ; CBRN 
Information or Capabilities  
GV-1.3-007 Devise a plan to halt development or deployment of a GAI system that poses unacceptable negative risk.  CBRN Information and Capability ; 
Information Security ; Information 
Integrity  
AI Actor Tasks: Governance and Oversight  
 
GOVERN 1.4: The risk management process and its outcomes are established through transparent policies, procedures, and other 
controls based on organizational risk priorities.  
Action ID  Suggested Action  GAI Risks  
GV-1.4-001 Establish policies and mechanisms to prevent GAI systems from generating 
CSAM, NCII or content that violates the law.   Obscene, Degrading, and/or 
Abusive Content ; Harmful Bias 
and Homogenization ; 
Dangerous, Violent, or Hateful Content
 
GV-1.4-002 Establish transparent acceptable use policies for GAI that address illegal use or 
applications of GAI.  CBRN Information or 
Capabilities ; Obscene, 
Degrading, and/or Abusive Content ; Data Privacy ; Civil 
Rights violations
 
AI Actor Tasks: AI Development, AI Deployment, Governance and Oversight, DATA PRIVACY 
WHY THIS PRINCIPLE IS IMPORTANT
This section provides a brief summary of the problems which the principle seeks to address and protect 
against, including illustrative examples. 
Data privacy is a foundational and cross-cutting principle required for achieving all others in this framework. Surveil -
lance and data collection, sharing, use, and reuse now sit at the foundation of business models across many industries, 
with more and more companies tracking the behavior of the American public, building individual profiles based on this data, and using this granular-level information as input into automated systems that further track, profile, and impact the American public. Government agencies, particularly law enforcement agencies, also use and help develop a variety of technologies that enhance and expand surveillance capabilities, which similarly collect data used as input into other automated systems that directly impact people’s lives. Federal law has not grown to address the expanding scale of private data collection, or of the ability of governments at all levels to access that data and leverage the means of private collection.  
Meanwhile, members of the American public are often unable to access their personal data or make critical decisions about its collection and use. Data brokers frequently collect consumer data from numerous sources without consumers’ permission or 
knowledge.60 Moreover, there is a risk that inaccurate and faulty data can be used to 
make decisions about their lives, such as whether they will qualify for a loan or get a job. Use of surveillance 
technologies has increased in schools and workplaces, and, when coupled with consequential management and 
evaluation decisions, it is leading to mental health harms such as lowered self-confidence, anxiet y, depression, and 
a reduced ability to use analytical reasoning.61 Documented patterns show that personal data is being aggregated by 
data brokers to profile communities in harmful ways.62 The impact of all this data harvesting is corrosive, 
breeding distrust, anxiety, and other mental health problems; chilling speech, protest, and worker organizing; and 
threatening our democratic process.63 The American public should be protected from these growing risks. 
Increasingl y, some companies are taking these concerns seriously and integrating mechanisms to protect consumer 
privacy into their products by design and by default, including by minimizing the data they collect, communicating collection and use clearl y, and improving security practices. Federal government surveillance and other collection and 
use of data is governed by legal protections that help to protect civil liberties and provide for limits on data retention in some cases. Many states have also enacted consumer data privacy protection regimes to address some of these harms. 
Howeve r, these are not yet standard practices, and the United States lacks a comprehensive statutory or regulatory 
framework governing the rights of the public when it comes to personal data. While a patchwork of laws exists to guide the collection and use of personal data in specific contexts, including health, employment, education, and credit, it can be unclear how these laws apply in other contexts and in an increasingly automated societ y. Additional protec
-
tions would assure the American public that the automated systems they use are not monitoring their activities, collecting information on their lives, or otherwise surveilling them without context-specific consent or legal authori
-
ty. 
31, Applying The Blueprint for an AI Bill of Rights 
SENSITIVE DATA: Data and metadata are sensitive if they pertain to an individual in a sensitive domain 
(defined below); are generated by technologies used in a sensitive domain; can be used to infer data from a 
sensitive domain or sensitive data about an individual (such as disability-related data, genomic data, biometric data, behavioral data, geolocation data, data related to interaction with the criminal justice system, relationship history and legal status such as custody and divorce information, and home, work, or school environmental data); or have the reasonable potential to be used in ways that are likely to expose individuals to meaningful harm, such as a loss of privacy or financial harm due to identity theft. Data and metadata generated by or about those who are not yet legal adults is also sensitive, even if not related to a sensitive domain. Such data includes, but is not limited to, numerical, text, image, audio, or video data. 
SENSITIVE DOMAINS: “Sensitive domains” are those in which activities being conducted can cause material 
harms, including significant adverse effects on human rights such as autonomy and dignit y, as well as civil liber-
ties and civil rights. Domains that have historically been singled out as deserving of enhanced data protections 
or where such enhanced protections are reasonably expected by the public include, but are not limited to, health, family planning and care, employment, education, criminal justice, and personal finance. In the context of this framework, such domains are considered sensitive whether or not the specifics of a system context would necessitate coverage under existing la w, and domains and data that are considered sensitive are under-
stood to change over time based on societal norms and context. 
SURVEILLANCE TECHNOLOGY : “Surveillance technology” refers to products or services marketed for 
or that can be lawfully used to detect, monitor, intercept, collect, exploit, preserve, protect, transmit, and/or 
retain data, identifying information, or communications concerning individuals or groups. This framework 
limits its focus to both government and commercial use of surveillance technologies when juxtaposed with 
real-time or subsequent automated analysis and when such systems have a potential for meaningful impact 
on individuals’ or communities’ rights, opportunities, or access. UNDERSERVED COMMUNITIES: The term “underserved communities” refers to communities that have 
been systematically denied a full opportunity to participate in aspects of economic, social, and civic life, as 
exemplified by the list in the preceding definition of “equit y.” 
11, source_sentence: Discuss the implications of automatic signature verification software on voter disenfranchisement in the United States, as highlighted in the article by Kyle Wiggers. What are the potential risks associated with this technology?
sentences: ENDNOTES
96. National Science Foundation. NSF Program on Fairness in Artificial Intelligence in Collaboration
with Amazon (FAI). Accessed July 20, 2022.
https://www.nsf.gov/pubs/2021/nsf21585/nsf21585.htm
97. Kyle Wiggers. Automatic signature verification software threatens to disenfranchise U.S. voters.
VentureBeat. Oct. 25, 2020.
https://venturebeat.com/2020/10/25/automatic-signature-verification-software-threatens-to-disenfranchise-u-s-voters/
98. Ballotpedia. Cure period for absentee and mail-in ballots. Article retrieved Apr 18, 2022.
https://ballotpedia.org/Cure_period_for_absentee_and_mail-in_ballots
99. Larry Buchanan and Alicia Parlapiano. Two of these Mail Ballot Signatures are by the Same Person.
Which Ones? New York Times. Oct. 7, 2020.
https://www.nytimes.com/interactive/2020/10/07/upshot/mail-voting-ballots-signature-
matching.html
100. Rachel Orey and Owen Bacskai. The Low Down on Ballot Curing. Nov. 04, 2020.
https://bipartisanpolicy.org/blog/the-low-down-on-ballot-curing/101. Andrew Kenney. 'I'm shocked that they need to have a smartphone': System for unemployment
benefits exposes digital divide. USA Today. May 2, 2021.
https://www.usatoday.com/story/tech/news/2021/05/02/unemployment-benefits-system-leaving-
people-behind/4915248001/
102. Allie Gross. UIA lawsuit shows how the state criminalizes the unemployed . Detroit Metro-Times.
Sep. 18, 2015.
https://www.metrotimes.com/news/uia-lawsuit-shows-how-the-state-criminalizes-the-unemployed-2369412
103. Maia Szalavitz. The Pain Was Unbearable. So Why Did Doctors Turn Her Away? Wired. Aug. 11,
2021. https://www.wired.com/story/opioid-drug-addiction-algorithm-chronic-pain/
104. Spencer Soper. Fired by Bot at Amazon: "It's You Against the Machine" . Bloomberg, Jun. 28, 2021.
https://www.bloomberg.com/news/features/2021-06-28/fired-by-bot-amazon-turns-to-machine-
managers-and-workers-are-losing-out
105. Definitions of ‘equity’ and ‘underserved communities’ can be found in the Definitions section of
this document as well as in Executive Order on Advancing Racial Equity and Support for Underserved
Communities Through the Federal Government:https://www.whitehouse.gov/briefing-room/presidential-actions/2021/01/20/executive-order-advancing-racial-equity-and-support-for-underserved-communities-through-the-federal-government/
106. HealthCare.gov. Navigator - HealthCare.gov Glossary. Accessed May 2, 2022.
https://www.healthcare.gov/glossary/navigator/
72, SAFE AND EFFECTIVE 
SYSTEMS 
WHY THIS PRINCIPLE IS IMPORTANT
This section provides a brief summary of the problems which the principle seeks to address and protect 
against, including illustrative examples. 
• AI-enabled “nudification” technology that creates images where people appear to be nude—including apps that
enable non-technical users to create or alter images of individuals without their consent—has proliferated at an
alarming rate. Such technology is becoming a common form of image-based abuse that disproportionately
impacts women. As these tools become more sophisticated, they are producing altered images that are increasing -
ly realistic and are difficult for both humans and AI to detect as inauthentic. Regardless of authenticit y, the expe -
rience of harm to victims of non-consensual intimate images can be devastatingly real—affecting their personal
and professional lives, and impacting their mental and physical health.10
• A company installed AI-powered cameras in its delivery vans in order to evaluate the road safety habits of its driv -
ers, but the system incorrectly penalized drivers when other cars cut them off or when other events beyond
their control took place on the road. As a result, drivers were incorrectly ineligible to receive a bonus.11
17, NOTICE & 
EXPLANATION 
WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS
The expectations for automated systems are meant to serve as a blueprint for the development of additional 
technical standards and practices that are tailored for particular sectors and contexts. 
Tailored to the level of risk. An assessment should be done to determine the level of risk of the auto -
mated system. In settings where the consequences are high as determined by a risk assessment, or extensive 
oversight is expected (e.g., in criminal justice or some public sector settings), explanatory mechanisms should be built into the system design so that the system’s full behavior can be explained in advance (i.e., only fully transparent models should be used), rather than as an after-the-decision interpretation. In other settings, the extent of explanation provided should be tailored to the risk level. 
Valid. The explanation provided by a system should accurately reflect the factors and the influences that led 
to a particular decision, and should be meaningful for the particular customization based on purpose, target, and level of risk. While approximation and simplification may be necessary for the system to succeed based on the explanatory purpose and target of the explanation, or to account for the risk of fraud or other concerns related to revealing decision-making information, such simplifications should be done in a scientifically supportable way. Where appropriate based on the explanatory system, error ranges for the explanation should be calculated and included in the explanation, with the choice of presentation of such information balanced with usability and overall interface complexity concerns. 
Demonstrate protections for notice and explanation 
Reporting. Summary reporting should document the determinations made based on the above consider -
ations, including: the responsible entities for accountability purposes; the goal and use cases for the system, identified users, and impacted populations; the assessment of notice clarity and timeliness; the assessment of the explanation's validity and accessibility; the assessment of the level of risk; and the account and assessment of how explanations are tailored, including to the purpose, the recipient of the explanation, and the level of risk. Individualized profile information should be made readily available to the greatest extent possible that includes explanations for any system impacts or inferences. Reporting should be provided in a clear plain language and machine-readable manner. 
44, source_sentence: How does the document aim to bridge the gap between theoretical principles and practical applications in the context of AI rights?
sentences: FROM 
PRINCIPLES 
TO PRACTICE 
A T ECHINCAL COMPANION TO
THE Blueprint for an 
AI B ILL OF RIGHTS
12, 3 the abuse, misuse, and unsafe repurposing by humans (adversarial or not ), and  others result 
from interactions between a human and an AI system.   
• Time  scale: GAI risks  may materialize  abruptly or  across extended periods . Example s include 
immediate  (and/or prolonged) emotional harm  and potential risks to physical safety  due to the 
distribution of harmful deepfake images , or the lo ng-term eﬀect of disinformation on soci etal 
trust in public  institutions . 
The presence of risks  and where they fall along the dimensions above will vary depending on the 
characteristics of the GAI model , system, or use case at hand. These characteristics include but are not 
limited to GAI model or system architecture, training mechanisms  and libraries , data types  used for 
training or ﬁne -tuning , levels of model access or availability of model weights,  and application or use 
case context. 
Organizations may choose to tailor how they measure  GAI risks  based on  these characteristics . They may 
additionally wish to  allocate risk management resources relative to the severity and likelihood of 
negative impact s, including where and how these risks manifest , and their direct and material impacts  
harms in the context of GAI use. Mitigations for model or system  level risks may diﬀer from mitigations  
for use-case or ecosystem level risks.  
Importantly, some GAI risks are un known , and are therefore diﬃcult to properly scope or evaluate given  
the uncertaint y about potential GAI scale, complexity, and capabilities. Other risks may be known but  
diﬃcult to estimate  given the wide range of GAI stakeholders, uses, inputs, and outputs . Challenges  with 
risk estimation are aggravated by a lack of visibility into GAI training data, and the generally immature 
state of the science of AI measurement and safety  today . This document focuses on risks for which there 
is an existing empirical evidence base at the time this proﬁle was written ; for example,  speculative risks 
that may potentially arise in more advanced, future GAI systems are not considered . Future updates may 
incorporate additional risks or provide further details on the risks identiﬁed below.  
To guide organizations in identifying and managing GAI risks, a set of risks unique to or exacerbated by 
the development and use of GAI are deﬁned below.5 Each risk is  labeled according to  the outcome , 
object,  or source of the risk  (i.e., some are risks “to ” a subject  or domain  and others are  risks  “of” or 
“from” an issue or  theme ). These risks provide a lens  through which organizations can frame and execute 
risk management eﬀorts.  To help streamline risk management eﬀorts, each risk is mapped in Section 3  
(as well as in tables in Appendix B)  to relevant Trustworthy AI Characteristics  identiﬁed in the AI RMF .  
 
 
5 These risks can be further categorized by organizations depending on their unique approaches to risk deﬁnition 
and management. One possible way to further categorize these risks, derived in part from the UK’s International 
Scientiﬁc Report on the Safety of Advanced AI , could be: 1 ) Technical / Model risks (or risk from malfunction): 
Confabulation; Dangerous or Violent Recommendations; Data Privacy; Value Chain and Component Integration; 
Harmful Bias, and Homogenization ; 2) Misuse by humans (or malicious use):  CBRN Information  or Capabilities ; 
Data Privacy; Human -AI Conﬁguration; Obscene, Degrading, and/or Abusive Content; Information Integrity;  
Information Security; 3) Ecosystem / societal risks (or systemic risks) : Data Privacy; Environmental; Intellectual 
Property . We also note that some risks are cross -cutting between these categories., 5 operations , or other cyberattacks ; increas ed attack surface  for targeted cyberattacks , which may 
compromise a system’s availability or the conﬁdentiality or integrity of training data, code, or 
model weights.   
10. Intellectual Property:  Eased production or replication  of alleged  copyrighted, trademarked, or 
licensed content without authorization  (possibly in situations which do not fall under fair use ); 
eased exposure of trade secrets;  or plagiari sm or illegal replication .  
11. Obscen e, Degrading, and/or A busive Content : Eased production of and access to obscene , 
degrading, and/or abusive  imagery  which can cause harm , including synthetic child sexual abuse 
material (CSAM) , and nonconsensual intimate images (NCII) of adults . 
12. Value Chain and Component Integration : Non-transparent or untraceable  integration of 
upstream third- party components, including data that has been improperly obtained or not 
processed and cleaned due to increased automation from GAI; improper supplier vetting across 
the AI lifecycle ; or other issues that diminish transparency or accountability for downstream 
users.  
2.1. CBRN Information or Capabilities  
In the future, GAI may enable malicious actors to more easily  access CBRN  weapons and/or relevant 
knowledge, information , materials, tools, or technologies that could be misused  to assist in the design, 
development, production, or use of CBRN weapons or other dangerous materials  or agents . While 
relevant biological and chemical threat knowledge and information is often  publicly accessible , LLMs  
could facilitate its analysis or synthesis , particularly by individuals  without formal scientiﬁc training  or 
expertise.  
Recent research on this topic found that LLM outputs regarding biological threat creation  and attack 
planning  pr ovided minima l assistance  beyond traditional search engine queries, suggesting that state -of-
the-art LLMs at the time these studies were conducted do not substantially  increase the operational 
likelihood of such an attack.  The physical synthesis development, production, and use of chemical or 
biological agents will continue to require both applicable expertise and supporting materials and 
infrastructure . The impact of GAI on chemical or biological agent misuse will depend on  what the  key 
barriers  for malicious actors  are (e.g., whether information access is one such barrier ), and how well GAI 
can help actors  address those barriers .  
Furthermore , chemical and biological design tools  (BDTs)  – highly specialized AI systems  trained on 
scientiﬁc data that aid in chemical and biological design – may augment  design capabilities in chemistry 
and biology beyond what text -based LLMs are able to provide . As these models become more 
eﬃcacious , including for beneﬁcial uses, it will be important to assess their potential to be used for 
harm, such as the ideation and design of novel harmful chemical or biological agents .  
While some of these described capabilities  lie beyond the reach  of existing GAI tools, ongoing 
assessments of this risk would be enhanced by monitoring both  the ability of  AI tools  to facilitate CBRN 
weapons planning and GAI systems’  connection or access to relevant data and tools . 
Trustworthy AI Characteristic : Safe , Explainable and Interpretable, source_sentence: What are the key components that should be included in the ongoing monitoring procedures for automated systems to ensure their performance remains acceptable over time?
sentences: AI B ILL OF RIGHTS
FFECTIVE  SYSTEMS
ineffective systems. Automated systems should be 
communities, stakeholders, and domain experts to identify 
Systems should undergo pre-deployment testing, risk 
that demonstrate they are safe and effective based on 
including those beyond the intended use, and adherence to 
protective measures should include the possibility of not 
Automated systems should not be designed with an intent 
reasonably foreseeable possibility of endangering your safety or the safety of your communit y. They should 
stemming from unintended, yet foreseeable, uses or 
   
 
  
 
 SECTION  TITLE
BLUEPRINT FOR AN
SAFE AND E 
You should be protected from unsafe or 
developed with consultation from diverse 
concerns, risks, and potential impacts of the system. 
identification and mitigation, and ongoing monitoring 
their intended use, mitigation of unsafe outcomes 
domain-specific standards. Outcomes of these 
deploying the system or removing a system from use. 
or 
be designed to proactively protect you from harms 
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 P ROTECTIONS
You should not face discrimination by algorithms and systems should be used and designed in 
an equitable way.  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. 
5, DATA PRIVACY 
WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS
The expectations for automated systems are meant to serve as a blueprint for the development of additional 
technical standards and practices that are tailored for particular sectors and contexts. 
In addition to the privacy expectations above for general non-sensitive data, any system collecting, using, shar-
ing, or storing sensitive data should meet the expectations belo w. Depending on the technological use case and 
based on an ethical assessment, consent for sensitive data may need to be acquired from a guardian and/or child. 
Provide enhanced protections for data related to sensitive domains 
Necessar y function s only . Sensitive data should only be used for functions strictly necessary for that 
domain or for functions that are required for administrative reasons (e.g., school attendance records), unless 
consent is acquired, if appropriate, and the additional expectations in this section are met. Consent for non-
necessary functions should be optional, i.e., should not be required, incentivized, or coerced in order to 
receive opportunities or access to services. In cases where data is provided to an entity (e.g., health insurance 
company) in order to facilitate payment for such a need, that data should only be used for that purpose. 
Ethical review and use prohibitions. Any use of sensitive data or decision process based in part on sensi-
tive data that might limit rights, opportunities, or access, whether the decision is automated or not, should go 
through a thorough ethical review and monitoring, both in advance and by periodic review (e.g., via an indepen-
dent ethics committee or similarly robust process). In some cases, this ethical review may determine that data 
should not be used or shared for specific uses even with consent. Some novel uses of automated systems in this 
context, where the algorithm is dynamically developing and where the science behind the use case is not well 
established, may also count as human subject experimentation, and require special review under organizational 
compliance bodies applying medical, scientific, and academic human subject experimentation ethics rules and 
governance procedures. 
Data  quality. In sensitive domains, entities should be especially careful to maintain the quality of data to 
avoid adverse consequences arising from decision-making based on flawed or inaccurate data. Such care is 
necessary in a fragmented, complex data ecosystem and for datasets that have limited access such as for fraud 
prevention and law enforcement. It should be not left solely to individuals to carry the burden of reviewing and 
correcting data. Entities should conduct regula r, independent audits and take prompt corrective measures to 
maintain accurate, timel y, and complete data. 
Limit access  to sensitive  data  and derived data. Sensitive data and derived data should not be sold, 
shared, or made public as part of data brokerage or other agreements. Sensitive data includes data that can be 
used to infer sensitive information; even systems that are not directly marketed as sensitive domain technologies 
are expected to keep sensitive data private. Access to such data should be limited based on necessity and based 
on a principle of local control, such that those individuals closest to the data subject have more access while 
those who are less proximate do not (e.g., a teacher has access to their students’ daily progress data while a 
superintendent does not). 
Reporting.  In addition to the reporting on data privacy (as listed above for non-sensitive data), entities devel-
oping technologies related to a sensitive domain and those collecting, using, storing, or sharing sensitive data 
should, whenever appropriate, regularly provide public reports describing: any data security lapses or breaches 
that resulted in sensitive data leaks; the numbe r, type, and outcomes of ethical pre-reviews undertaken; a 
description of any data sold, shared, or made public, and how that data was assessed to determine it did not pres-
ent a sensitive data risk; and ongoing risk identification and management procedures, and any mitigation added 
based on these procedures. Reporting should be provided in a clear and machine-readable manne r. 
38, SAFE AND EFFECTIVE 
SYSTEMS 
WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS
The expectations for automated systems are meant to serve as a blueprint for the development of additional 
technical standards and practices that are tailored for particular sectors and contexts. 
Ongoing monitoring. Automated systems should have ongoing monitoring procedures, including recalibra -
tion procedures, in place to ensure that their performance does not fall below an acceptable level over time, 
based on changing real-world conditions or deployment contexts, post-deployment modification, or unexpect -
ed conditions. This ongoing monitoring should include continuous evaluation of performance metrics and harm assessments, updates of any systems, and retraining of any machine learning models as necessary, as well as ensuring that fallback mechanisms are in place to allow reversion to a previously working system. Monitor
-
ing should take into account the performance of both technical system components (the algorithm as well as any hardware components, data inputs, etc.) and human operators. It should include mechanisms for testing the actual accuracy of any predictions or recommendations generated by a system, not just a human operator’s determination of their accuracy. Ongoing monitoring procedures should include manual, human-led monitor
-
ing as a check in the event there are shortcomings in automated monitoring systems. These monitoring proce -
dures should be in place for the lifespan of the deployed automated system. 
Clear organizational oversight. Entities responsible for the development or use of automated systems should lay out clear governance structures and procedures.  This includes clearly-stated governance proce
-
dures before deploying the system, as well as responsibility of specific individuals or entities to oversee ongoing assessment and mitigation. Organizational stakeholders including those with oversight of the business process or operation being automated, as well as other organizational divisions that may be affected due to the use of the system, should be involved in establishing governance procedures. Responsibility should rest high enough in the organization that decisions about resources, mitigation, incident response, and potential rollback can be made promptly, with sufficient weight given to risk mitigation objectives against competing concerns. Those holding this responsibility should be made aware of any use cases with the potential for meaningful impact on people’s rights, opportunities, or access as determined based on risk identification procedures.  In some cases, it may be appropriate for an independent ethics review to be conducted before deployment. 
Avoid inappropriate, low-quality, or irrelevant data use and the compounded harm of its reuse 
Relevant and high-quality data. Data used as part of any automated system’s creation, evaluation, or 
deployment should be relevant, of high quality, and tailored to the task at hand. Relevancy should be 
established based on research-backed demonstration of the causal influence of the data to the specific use case 
or justified more generally based on a reasonable expectation of usefulness in the domain and/or for the 
system design or ongoing development. Relevance of data should not be established solely by appealing to 
its historical connection to the outcome. High quality and tailored data should be representative of the task at 
hand and errors from data entry or other sources should be measured and limited. Any data used as the target 
of a prediction process should receive particular attention to the quality and validity of the predicted outcome 
or label to ensure the goal of the automated system is appropriately identified and measured. Additionally , 
justification should be documented for each data attribute and source to explain why it is appropriate to use 
that data to inform the results of the automated system and why such use will not violate any applicable laws. 
In cases of high-dimensional and/or derived attributes, such justifications can be provided as overall 
descriptions of the attribute generation process and appropriateness. 
19, source_sentence: What are the key principles and frameworks mentioned in the white paper that govern the implementation of AI in national security and defense activities?
sentences: APPENDIX
• OSTP conducted meetings with a variety of stakeholders in the private sector and civil society. Some of these
meetings were specifically focused on providing ideas related to the development of the Blueprint for an AI
Bill of Rights while others provided useful general context on the positive use cases, potential harms, and/or
oversight possibilities for these technologies. Participants in these conversations from the private sector and
civil society included:
Adobe 
American Civil Liberties Union (ACLU) The Aspen Commission on Information Disorder The Awood Center The Australian Human Rights Commission Biometrics Institute The Brookings Institute BSA | The Software Alliance Cantellus Group Center for American Progress Center for Democracy and Technology Center on Privacy and Technology at Georgetown Law Christiana Care Color of Change Coworker Data Robot Data Trust Alliance Data and Society Research Institute Deepmind EdSAFE AI Alliance Electronic Privacy Information Center (EPIC) Encode Justice Equal AI Google Hitachi's AI Policy Committee The Innocence Project Institute of Electrical and Electronics Engineers (IEEE) Intuit Lawyers Committee for Civil Rights Under Law Legal Aid Society The Leadership Conference on Civil and Human Rights Meta Microsoft The MIT AI Policy Forum Movement Alliance Project The National Association of Criminal Defense Lawyers O’Neil Risk Consulting & Algorithmic Auditing The Partnership on AI Pinterest The Plaintext Group pymetrics SAP The Security Industry Association Software and Information Industry Association (SIIA) Special Competitive Studies Project Thorn United for Respect University of California at Berkeley Citris Policy Lab University of California at Berkeley Labor Center Unfinished/Project Liberty Upturn US Chamber of Commerce US Chamber of Commerce Technology Engagement Center 
A.I. Working Group
Vibrent HealthWarehouse Worker ResourceCenterWaymap
62, This white paper recognizes that national security (which includes certain law enforcement and 
homeland security activities) and defense activities are of increased sensitivity and interest to our nation’s 
adversaries and are often subject to special requirements, such as those governing classified information and 
other protected data. Such activities require alternative, compatible safeguards through existing policies that 
govern automated systems and AI, such as the Department of Defense (DOD) AI Ethical Principles and 
Responsible AI Implementation Pathway and the Intelligence Community (IC) AI Ethics Principles and 
Framework. The implementation of these policies to national security and defense activities can be informed by 
the Blueprint for an AI Bill of Rights where feasible. 
The Blueprint for an AI Bill of Rights is not intended to, and does not, create any legal right, benefit, or 
defense, substantive or procedural, enforceable at law or in equity by any party against the United States, its 
departments, agencies, or entities, its officers, employees, or agents, or any other person, nor does it constitute a 
waiver of sovereign immunity. 
Copyright Information 
This document is a work of the United States Government and is in the public domain (see 17 U.S.C. §105). 
2, This white paper recognizes that national security (which includes certain law enforcement and 
homeland security activities) and defense activities are of increased sensitivity and interest to our nation’s 
adversaries and are often subject to special requirements, such as those governing classified information and 
other protected data. Such activities require alternative, compatible safeguards through existing policies that 
govern automated systems and AI, such as the Department of Defense (DOD) AI Ethical Principles and 
Responsible AI Implementation Pathway and the Intelligence Community (IC) AI Ethics Principles and 
Framework. The implementation of these policies to national security and defense activities can be informed by 
the Blueprint for an AI Bill of Rights where feasible. 
The Blueprint for an AI Bill of Rights is not intended to, and does not, create any legal right, benefit, or 
defense, substantive or procedural, enforceable at law or in equity by any party against the United States, its 
departments, agencies, or entities, its officers, employees, or agents, or any other person, nor does it constitute a 
waiver of sovereign immunity. 
Copyright Information 
This document is a work of the United States Government and is in the public domain (see 17 U.S.C. §105). 
2
model-index: name: SentenceTransformer based on Alibaba-NLP/gte-large-en-v1.5
results: task: type: information-retrieval
name: Information Retrieval
dataset: name: Unknown
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value: 0.8748143350701476
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name: Dot Mrr@10
verified: no, type: dot_map@100
value: 0.8317901234567903
name: Dot Map@100
verified: no
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