--- 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 \naccordance with activities in the AI RMF Map function . CBRN Information\ \ or Capabilities ; \nObscene, Degrading, and/or \nAbusive Content ; Harmful Bias\ \ \nand Homogenization ; Dangerous, \nViolent, or Hateful Content \nGV-1.3-005\ \ Maintain an updated hierarch y of identified and expected GAI risks connected\ \ to \ncontexts of GAI model advancement and use, potentially including specialized\ \ risk \nlevels for GAI systems that address issues such as model collapse and\ \ algorithmic \nmonoculture. Harmful Bias and Homogenization \nGV-1.3-006 Reevaluate\ \ organizational risk tolerances to account for unacceptable negative risk \n\ (such as where significant negative impacts are imminent, severe harms are actually\ \ occurring, or large -scale risks could occur); and broad GAI negative risks,\ \ \nincluding: 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 , \nViolent, or Hateful Content ; CBRN \n\ Information or Capabilities \nGV-1.3-007 Devise a plan to halt development or\ \ deployment of a GAI system that poses unacceptable negative risk. CBRN Information\ \ and Capability ; \nInformation Security ; Information \nIntegrity \nAI Actor\ \ Tasks: Governance and Oversight \n \nGOVERN 1.4: The risk management process\ \ and its outcomes are established through transparent policies, procedures, and\ \ other \ncontrols based on organizational risk priorities. \nAction ID Suggested\ \ Action GAI Risks \nGV-1.4-001 Establish policies and mechanisms to prevent\ \ GAI systems from generating \nCSAM, NCII or content that violates the law. \ \ Obscene, Degrading, and/or \nAbusive Content ; Harmful Bias \nand Homogenization\ \ ; \nDangerous, Violent, or Hateful Content\n \nGV-1.4-002 Establish transparent\ \ acceptable use policies for GAI that address illegal use or \napplications of\ \ GAI. CBRN Information or \nCapabilities ; Obscene, \nDegrading, and/or Abusive\ \ Content ; Data Privacy ; Civil \nRights violations\n \nAI Actor Tasks: AI Development,\ \ AI Deployment, Governance and Oversight" - "DATA PRIVACY \nWHY THIS PRINCIPLE IS IMPORTANT\nThis section provides a brief\ \ summary of the problems which the principle seeks to address and protect \n\ against, including illustrative examples. \nData privacy is a foundational and\ \ cross-cutting principle required for achieving all others in this framework.\ \ Surveil -\nlance and data collection, sharing, use, and reuse now sit at the\ \ foundation of business models across many industries, \nwith 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. \nMeanwhile,\ \ 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\ \ \nknowledge.60 Moreover, there is a risk that inaccurate and faulty data can\ \ be used to \nmake decisions about their lives, such as whether they will qualify\ \ for a loan or get a job. Use of surveillance \ntechnologies has increased in\ \ schools and workplaces, and, when coupled with consequential management and\ \ \nevaluation decisions, it is leading to mental health harms such as lowered\ \ self-confidence, anxiet y, depression, and \na reduced ability to use analytical\ \ reasoning.61 Documented patterns show that personal data is being aggregated\ \ by \ndata brokers to profile communities in harmful ways.62 The impact of all\ \ this data harvesting is corrosive, \nbreeding distrust, anxiety, and other mental\ \ health problems; chilling speech, protest, and worker organizing; and \nthreatening\ \ our democratic process.63 The American public should be protected from these\ \ growing risks. \nIncreasingl y, some companies are taking these concerns seriously\ \ and integrating mechanisms to protect consumer \nprivacy 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 \nuse 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. \nHoweve r, these are not yet standard\ \ practices, and the United States lacks a comprehensive statutory or regulatory\ \ \nframework 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\n-\ntions 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\n-\nty. \n31" - "Applying The Blueprint for an AI Bill of Rights \nSENSITIVE DATA: Data and metadata\ \ are sensitive if they pertain to an individual in a sensitive domain \n(defined\ \ below); are generated by technologies used in a sensitive domain; can be used\ \ to infer data from a \nsensitive 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. \nSENSITIVE DOMAINS: “Sensitive\ \ domains” are those in which activities being conducted can cause material \n\ harms, including significant adverse effects on human rights such as autonomy\ \ and dignit y, as well as civil liber-\nties and civil rights. Domains that have\ \ historically been singled out as deserving of enhanced data protections \nor\ \ 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-\nstood to change over time based on societal\ \ norms and context. \nSURVEILLANCE TECHNOLOGY : “Surveillance technology” refers\ \ to products or services marketed for \nor that can be lawfully used to detect,\ \ monitor, intercept, collect, exploit, preserve, protect, transmit, and/or \n\ retain data, identifying information, or communications concerning individuals\ \ or groups. This framework \nlimits its focus to both government and commercial\ \ use of surveillance technologies when juxtaposed with \nreal-time or subsequent\ \ automated analysis and when such systems have a potential for meaningful impact\ \ \non individuals’ or communities’ rights, opportunities, or access. UNDERSERVED\ \ COMMUNITIES: The term “underserved communities” refers to communities that have\ \ \nbeen systematically denied a full opportunity to participate in aspects of\ \ economic, social, and civic life, as \nexemplified by the list in the preceding\ \ definition of “equit y.” \n11" - 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 \nSYSTEMS \nWHY THIS PRINCIPLE IS IMPORTANT\nThis section\ \ provides a brief summary of the problems which the principle seeks to address\ \ and protect \nagainst, including illustrative examples. \n• AI-enabled “nudification”\ \ technology that creates images where people appear to be nude—including apps\ \ that\nenable non-technical users to create or alter images of individuals without\ \ their consent—has proliferated at an\nalarming rate. Such technology is becoming\ \ a common form of image-based abuse that disproportionately\nimpacts women. As\ \ these tools become more sophisticated, they are producing altered images that\ \ are increasing -\nly realistic and are difficult for both humans and AI to detect\ \ as inauthentic. Regardless of authenticit y, the expe -\nrience of harm to victims\ \ of non-consensual intimate images can be devastatingly real—affecting their\ \ personal\nand professional lives, and impacting their mental and physical health.10\n\ • A company installed AI-powered cameras in its delivery vans in order to evaluate\ \ the road safety habits of its driv -\ners, but the system incorrectly penalized\ \ drivers when other cars cut them off or when other events beyond\ntheir control\ \ took place on the road. As a result, drivers were incorrectly ineligible to\ \ receive a bonus.11\n17" - "NOTICE & \nEXPLANATION \nWHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS\nThe expectations\ \ for automated systems are meant to serve as a blueprint for the development\ \ of additional \ntechnical standards and practices that are tailored for particular\ \ sectors and contexts. \nTailored to the level of risk. An assessment should\ \ be done to determine the level of risk of the auto -\nmated system. In settings\ \ where the consequences are high as determined by a risk assessment, or extensive\ \ \noversight 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. \nValid. The explanation provided by a system should accurately reflect\ \ the factors and the influences that led \nto 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. \nDemonstrate protections for notice and explanation \n\ Reporting. Summary reporting should document the determinations made based on\ \ the above consider -\nations, 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. \n\ 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 \nPRINCIPLES \nTO PRACTICE \nA T ECHINCAL COMPANION TO\nTHE Blueprint for\ \ an \nAI B ILL OF RIGHTS\n12" - "3 the abuse, misuse, and unsafe repurposing by humans (adversarial or not ),\ \ and others result \nfrom interactions between a human and an AI system. \n\ • Time scale: GAI risks may materialize abruptly or across extended periods\ \ . Example s include \nimmediate (and/or prolonged) emotional harm and potential\ \ risks to physical safety due to the \ndistribution of harmful deepfake images\ \ , or the lo ng-term effect of disinformation on soci etal \ntrust in public \ \ institutions . \nThe presence of risks and where they fall along the dimensions\ \ above will vary depending on the \ncharacteristics of the GAI model , system,\ \ or use case at hand. These characteristics include but are not \nlimited to\ \ GAI model or system architecture, training mechanisms and libraries , data\ \ types used for \ntraining or fine -tuning , levels of model access or availability\ \ of model weights, and application or use \ncase context. \nOrganizations may\ \ choose to tailor how they measure GAI risks based on these characteristics\ \ . They may \nadditionally wish to allocate risk management resources relative\ \ to the severity and likelihood of \nnegative impact s, including where and how\ \ these risks manifest , and their direct and material impacts \nharms in the\ \ context of GAI use. Mitigations for model or system level risks may differ from\ \ mitigations \nfor use-case or ecosystem level risks. \nImportantly, some GAI\ \ risks are un known , and are therefore difficult to properly scope or evaluate\ \ given \nthe uncertaint y about potential GAI scale, complexity, and capabilities.\ \ Other risks may be known but \ndifficult to estimate given the wide range of\ \ GAI stakeholders, uses, inputs, and outputs . Challenges with \nrisk estimation\ \ are aggravated by a lack of visibility into GAI training data, and the generally\ \ immature \nstate of the science of AI measurement and safety today . This document\ \ focuses on risks for which there \nis an existing empirical evidence base at\ \ the time this profile was written ; for example, speculative risks \nthat may\ \ potentially arise in more advanced, future GAI systems are not considered .\ \ Future updates may \nincorporate additional risks or provide further details\ \ on the risks identified below. \nTo guide organizations in identifying and managing\ \ GAI risks, a set of risks unique to or exacerbated by \nthe development and\ \ use of GAI are defined below.5 Each risk is labeled according to the outcome\ \ , \nobject, or source of the risk (i.e., some are risks “to ” a subject or\ \ domain and others are risks “of” or \n“from” an issue or theme ). These\ \ risks provide a lens through which organizations can frame and execute \nrisk\ \ management efforts. To help streamline risk management efforts, each risk is\ \ mapped in Section 3 \n(as well as in tables in Appendix B) to relevant Trustworthy\ \ AI Characteristics identified in the AI RMF . \n \n \n5 These risks can be\ \ further categorized by organizations depending on their unique approaches to\ \ risk definition \nand management. One possible way to further categorize these\ \ risks, derived in part from the UK’s International \nScientific Report on the\ \ Safety of Advanced AI , could be: 1 ) Technical / Model risks (or risk from\ \ malfunction): \nConfabulation; Dangerous or Violent Recommendations; Data Privacy;\ \ Value Chain and Component Integration; \nHarmful Bias, and Homogenization ;\ \ 2) Misuse by humans (or malicious use): CBRN Information or Capabilities ;\ \ \nData Privacy; Human -AI Configuration; Obscene, Degrading, and/or Abusive Content;\ \ Information Integrity; \nInformation Security; 3) Ecosystem / societal risks\ \ (or systemic risks) : Data Privacy; Environmental; Intellectual \nProperty .\ \ 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 \ncompromise a system’s availability or the confidentiality\ \ or integrity of training data, code, or \nmodel weights. \n10. Intellectual\ \ Property: Eased production or replication of alleged copyrighted, trademarked,\ \ or \nlicensed content without authorization (possibly in situations which do\ \ not fall under fair use ); \neased exposure of trade secrets; or plagiari sm\ \ or illegal replication . \n11. Obscen e, Degrading, and/or A busive Content\ \ : Eased production of and access to obscene , \ndegrading, and/or abusive imagery\ \ which can cause harm , including synthetic child sexual abuse \nmaterial (CSAM)\ \ , and nonconsensual intimate images (NCII) of adults . \n12. Value Chain and\ \ Component Integration : Non-transparent or untraceable integration of \nupstream\ \ third- party components, including data that has been improperly obtained or\ \ not \nprocessed and cleaned due to increased automation from GAI; improper supplier\ \ vetting across \nthe AI lifecycle ; or other issues that diminish transparency\ \ or accountability for downstream \nusers. \n2.1. CBRN Information or Capabilities\ \ \nIn the future, GAI may enable malicious actors to more easily access CBRN\ \ weapons and/or relevant \nknowledge, information , materials, tools, or technologies\ \ that could be misused to assist in the design, \ndevelopment, production, or\ \ use of CBRN weapons or other dangerous materials or agents . While \nrelevant\ \ biological and chemical threat knowledge and information is often publicly\ \ accessible , LLMs \ncould facilitate its analysis or synthesis , particularly\ \ by individuals without formal scientific training or \nexpertise. \nRecent\ \ research on this topic found that LLM outputs regarding biological threat creation\ \ and attack \nplanning pr ovided minima l assistance beyond traditional search\ \ engine queries, suggesting that state -of-\nthe-art LLMs at the time these studies\ \ were conducted do not substantially increase the operational \nlikelihood of\ \ such an attack. The physical synthesis development, production, and use of\ \ chemical or \nbiological agents will continue to require both applicable expertise\ \ and supporting materials and \ninfrastructure . The impact of GAI on chemical\ \ or biological agent misuse will depend on what the key \nbarriers for malicious\ \ actors are (e.g., whether information access is one such barrier ), and how\ \ well GAI \ncan help actors address those barriers . \nFurthermore , chemical\ \ and biological design tools (BDTs) – highly specialized AI systems trained\ \ on \nscientific data that aid in chemical and biological design – may augment\ \ design capabilities in chemistry \nand biology beyond what text -based LLMs\ \ are able to provide . As these models become more \nefficacious , including for\ \ beneficial uses, it will be important to assess their potential to be used for\ \ \nharm, such as the ideation and design of novel harmful chemical or biological\ \ agents . \nWhile some of these described capabilities lie beyond the reach\ \ of existing GAI tools, ongoing \nassessments of this risk would be enhanced\ \ by monitoring both the ability of AI tools to facilitate CBRN \nweapons planning\ \ and GAI systems’ connection or access to relevant data and tools . \nTrustworthy\ \ 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\nFFECTIVE SYSTEMS\nineffective systems. Automated systems\ \ should be \ncommunities, stakeholders, and domain experts to identify \nSystems\ \ should undergo pre-deployment testing, risk \nthat demonstrate they are safe\ \ and effective based on \nincluding those beyond the intended use, and adherence\ \ to \nprotective measures should include the possibility of not \nAutomated systems\ \ should not be designed with an intent \nreasonably foreseeable possibility of\ \ endangering your safety or the safety of your communit y. They should \nstemming\ \ from unintended, yet foreseeable, uses or \n \n \n \n \n SECTION TITLE\n\ BLUEPRINT FOR AN\nSAFE AND E \nYou should be protected from unsafe or \ndeveloped\ \ with consultation from diverse \nconcerns, risks, and potential impacts of the\ \ system. \nidentification and mitigation, and ongoing monitoring \ntheir intended\ \ use, mitigation of unsafe outcomes \ndomain-specific standards. Outcomes of\ \ these \ndeploying the system or removing a system from use. \nor \nbe designed\ \ to proactively protect you from harms \nimpacts of automated systems. You should\ \ be protected from inappropriate or irrelevant data use in the \ndesign, development,\ \ and deployment of automated systems, and from the compounded harm of its reuse.\ \ \nIndependent evaluation and reporting that confirms that the system is safe\ \ and effective, including reporting of \nsteps taken to mitigate potential harms,\ \ should be performed and the results made public whenever possible. \nALGORITHMIC\ \ DISCRIMINATION P ROTECTIONS\nYou should not face discrimination by algorithms\ \ and systems should be used and designed in \nan equitable way. Algorithmic\ \ discrimination occurs when automated systems contribute to unjustified \ndifferent\ \ treatment or impacts disfavoring people based on their race, color, ethnicity,\ \ sex (including \npregnancy, childbirth, and related medical conditions, gender\ \ identity, intersex status, and sexual \norientation), religion, age, national\ \ origin, disability, veteran status, genetic information, or any other \nclassification\ \ protected by law. Depending on the specific circumstances, such algorithmic\ \ discrimination \nmay violate legal protections. Designers, developers, and\ \ deployers of automated systems should take \nproactive and continuous measures\ \ to protect individuals and communities from algorithmic \ndiscrimination and\ \ to use and design systems in an equitable way. This protection should include\ \ proactive \nequity assessments as part of the system design, use of representative\ \ data and protection against proxies \nfor demographic features, ensuring accessibility\ \ for people with disabilities in design and development, \npre-deployment and\ \ ongoing disparity testing and mitigation, and clear organizational oversight.\ \ Independent \nevaluation and plain language reporting in the form of an algorithmic\ \ impact assessment, including \ndisparity testing results and mitigation information,\ \ should be performed and made public whenever \npossible to confirm these protections.\ \ \n5" - "DATA PRIVACY \nWHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS\nThe expectations\ \ for automated systems are meant to serve as a blueprint for the development\ \ of additional \ntechnical standards and practices that are tailored for particular\ \ sectors and contexts. \nIn addition to the privacy expectations above for general\ \ non-sensitive data, any system collecting, using, shar-\ning, or storing sensitive\ \ data should meet the expectations belo w. Depending on the technological use\ \ case and \nbased on an ethical assessment, consent for sensitive data may need\ \ to be acquired from a guardian and/or child. \nProvide enhanced protections\ \ for data related to sensitive domains \nNecessar y function s only . Sensitive\ \ data should only be used for functions strictly necessary for that \ndomain\ \ or for functions that are required for administrative reasons (e.g., school\ \ attendance records), unless \nconsent is acquired, if appropriate, and the additional\ \ expectations in this section are met. Consent for non-\nnecessary functions\ \ should be optional, i.e., should not be required, incentivized, or coerced in\ \ order to \nreceive opportunities or access to services. In cases where data\ \ is provided to an entity (e.g., health insurance \ncompany) in order to facilitate\ \ payment for such a need, that data should only be used for that purpose. \n\ Ethical review and use prohibitions. Any use of sensitive data or decision process\ \ based in part on sensi-\ntive data that might limit rights, opportunities, or\ \ access, whether the decision is automated or not, should go \nthrough a thorough\ \ ethical review and monitoring, both in advance and by periodic review (e.g.,\ \ via an indepen-\ndent ethics committee or similarly robust process). In some\ \ cases, this ethical review may determine that data \nshould not be used or shared\ \ for specific uses even with consent. Some novel uses of automated systems in\ \ this \ncontext, where the algorithm is dynamically developing and where the\ \ science behind the use case is not well \nestablished, may also count as human\ \ subject experimentation, and require special review under organizational \n\ compliance bodies applying medical, scientific, and academic human subject experimentation\ \ ethics rules and \ngovernance procedures. \nData quality. In sensitive domains,\ \ entities should be especially careful to maintain the quality of data to \n\ avoid adverse consequences arising from decision-making based on flawed or inaccurate\ \ data. Such care is \nnecessary in a fragmented, complex data ecosystem and for\ \ datasets that have limited access such as for fraud \nprevention and law enforcement.\ \ It should be not left solely to individuals to carry the burden of reviewing\ \ and \ncorrecting data. Entities should conduct regula r, independent audits\ \ and take prompt corrective measures to \nmaintain accurate, timel y, and complete\ \ data. \nLimit access to sensitive data and derived data. Sensitive data and\ \ derived data should not be sold, \nshared, or made public as part of data brokerage\ \ or other agreements. Sensitive data includes data that can be \nused to infer\ \ sensitive information; even systems that are not directly marketed as sensitive\ \ domain technologies \nare expected to keep sensitive data private. Access to\ \ such data should be limited based on necessity and based \non a principle of\ \ local control, such that those individuals closest to the data subject have\ \ more access while \nthose who are less proximate do not (e.g., a teacher has\ \ access to their students’ daily progress data while a \nsuperintendent does\ \ not). \nReporting. In addition to the reporting on data privacy (as listed\ \ above for non-sensitive data), entities devel-\noping technologies related to\ \ a sensitive domain and those collecting, using, storing, or sharing sensitive\ \ data \nshould, whenever appropriate, regularly provide public reports describing:\ \ any data security lapses or breaches \nthat resulted in sensitive data leaks;\ \ the numbe r, type, and outcomes of ethical pre-reviews undertaken; a \ndescription\ \ of any data sold, shared, or made public, and how that data was assessed to\ \ determine it did not pres-\nent a sensitive data risk; and ongoing risk identification\ \ and management procedures, and any mitigation added \nbased on these procedures.\ \ Reporting should be provided in a clear and machine-readable manne r. \n38" - "SAFE AND EFFECTIVE \nSYSTEMS \nWHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS\n\ The expectations for automated systems are meant to serve as a blueprint for the\ \ development of additional \ntechnical standards and practices that are tailored\ \ for particular sectors and contexts. \nOngoing monitoring. Automated systems\ \ should have ongoing monitoring procedures, including recalibra -\ntion procedures,\ \ in place to ensure that their performance does not fall below an acceptable\ \ level over time, \nbased on changing real-world conditions or deployment contexts,\ \ post-deployment modification, or unexpect -\ned 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\n-\ning 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\n-\n\ ing as a check in the event there are shortcomings in automated monitoring systems.\ \ These monitoring proce -\ndures should be in place for the lifespan of the deployed\ \ automated system. \nClear 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\n\ -\ndures 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. \nAvoid inappropriate, low-quality, or irrelevant data use and the\ \ compounded harm of its reuse \nRelevant and high-quality data. Data used as\ \ part of any automated system’s creation, evaluation, or \ndeployment should\ \ be relevant, of high quality, and tailored to the task at hand. Relevancy should\ \ be \nestablished based on research-backed demonstration of the causal influence\ \ of the data to the specific use case \nor justified more generally based on\ \ a reasonable expectation of usefulness in the domain and/or for the \nsystem\ \ design or ongoing development. Relevance of data should not be established solely\ \ by appealing to \nits historical connection to the outcome. High quality and\ \ tailored data should be representative of the task at \nhand and errors from\ \ data entry or other sources should be measured and limited. Any data used as\ \ the target \nof a prediction process should receive particular attention to\ \ the quality and validity of the predicted outcome \nor label to ensure the goal\ \ of the automated system is appropriately identified and measured. Additionally\ \ , \njustification should be documented for each data attribute and source to\ \ explain why it is appropriate to use \nthat data to inform the results of the\ \ automated system and why such use will not violate any applicable laws. \nIn\ \ cases of high-dimensional and/or derived attributes, such justifications can\ \ be provided as overall \ndescriptions of the attribute generation process and\ \ appropriateness. \n19" - 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\n• OSTP conducted meetings with a variety of stakeholders in the private\ \ sector and civil society. Some of these\nmeetings were specifically focused\ \ on providing ideas related to the development of the Blueprint for an AI\nBill\ \ of Rights while others provided useful general context on the positive use cases,\ \ potential harms, and/or\noversight possibilities for these technologies. Participants\ \ in these conversations from the private sector and\ncivil society included:\n\ Adobe \nAmerican 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 \nA.I. Working Group\nVibrent HealthWarehouse Worker ResourceCenterWaymap\n\ 62" - "This white paper recognizes that national security (which includes certain law\ \ enforcement and \nhomeland security activities) and defense activities are of\ \ increased sensitivity and interest to our nation’s \nadversaries and are often\ \ subject to special requirements, such as those governing classified information\ \ and \nother protected data. Such activities require alternative, compatible\ \ safeguards through existing policies that \ngovern automated systems and AI,\ \ such as the Department of Defense (DOD) AI Ethical Principles and \nResponsible\ \ AI Implementation Pathway and the Intelligence Community (IC) AI Ethics Principles\ \ and \nFramework. The implementation of these policies to national security and\ \ defense activities can be informed by \nthe Blueprint for an AI Bill of Rights\ \ where feasible. \nThe Blueprint for an AI Bill of Rights is not intended to,\ \ and does not, create any legal right, benefit, or \ndefense, substantive or\ \ procedural, enforceable at law or in equity by any party against the United\ \ States, its \ndepartments, agencies, or entities, its officers, employees, or\ \ agents, or any other person, nor does it constitute a \nwaiver of sovereign\ \ immunity. \nCopyright Information \nThis document is a work of the United States\ \ Government and is in the public domain (see 17 U.S.C. §105). \n2" - "This white paper recognizes that national security (which includes certain law\ \ enforcement and \nhomeland security activities) and defense activities are of\ \ increased sensitivity and interest to our nation’s \nadversaries and are often\ \ subject to special requirements, such as those governing classified information\ \ and \nother protected data. Such activities require alternative, compatible\ \ safeguards through existing policies that \ngovern automated systems and AI,\ \ such as the Department of Defense (DOD) AI Ethical Principles and \nResponsible\ \ AI Implementation Pathway and the Intelligence Community (IC) AI Ethics Principles\ \ and \nFramework. The implementation of these policies to national security and\ \ defense activities can be informed by \nthe Blueprint for an AI Bill of Rights\ \ where feasible. \nThe Blueprint for an AI Bill of Rights is not intended to,\ \ and does not, create any legal right, benefit, or \ndefense, substantive or\ \ procedural, enforceable at law or in equity by any party against the United\ \ States, its \ndepartments, agencies, or entities, its officers, employees, or\ \ agents, or any other person, nor does it constitute a \nwaiver of sovereign\ \ immunity. \nCopyright Information \nThis document is a work of the United States\ \ Government and is in the public domain (see 17 U.S.C. §105). \n2" model-index: - name: SentenceTransformer based on Alibaba-NLP/gte-large-en-v1.5 results: - task: type: information-retrieval name: Information Retrieval dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy@1 value: 0.7222222222222222 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9814814814814815 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 1.0 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7222222222222222 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3271604938271604 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.19999999999999993 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09999999999999996 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7222222222222222 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9814814814814815 name: Cosine Recall@3 - type: cosine_recall@5 value: 1.0 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8816489692632687 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8410493827160495 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8410493827160495 name: Cosine Map@100 - type: dot_accuracy@1 value: 0.7037037037037037 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.9814814814814815 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 1.0 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 1.0 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.7037037037037037 name: Dot Precision@1 - type: dot_precision@3 value: 0.3271604938271604 name: Dot Precision@3 - type: dot_precision@5 value: 0.19999999999999993 name: Dot Precision@5 - type: dot_precision@10 value: 0.09999999999999996 name: Dot Precision@10 - type: dot_recall@1 value: 0.7037037037037037 name: Dot Recall@1 - type: dot_recall@3 value: 0.9814814814814815 name: Dot Recall@3 - type: dot_recall@5 value: 1.0 name: Dot Recall@5 - type: dot_recall@10 value: 1.0 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.8748143350701476 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.8317901234567903 name: Dot Mrr@10 - type: dot_map@100 value: 0.8317901234567903 name: Dot Map@100 --- # SentenceTransformer based on Alibaba-NLP/gte-large-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-large-en-v1.5](https://huggingface.co./Alibaba-NLP/gte-large-en-v1.5). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [Alibaba-NLP/gte-large-en-v1.5](https://huggingface.co./Alibaba-NLP/gte-large-en-v1.5) - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 1024 tokens - **Similarity Function:** Cosine Similarity ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co./models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NewModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ 'What are the key principles and frameworks mentioned in the white paper that govern the implementation of AI in national security and defense activities?', 'This white paper recognizes that national security (which includes certain law enforcement and \nhomeland security activities) and defense activities are of increased sensitivity and interest to our nation’s \nadversaries and are often subject to special requirements, such as those governing classified information and \nother protected data. Such activities require alternative, compatible safeguards through existing policies that \ngovern automated systems and AI, such as the Department of Defense (DOD) AI Ethical Principles and \nResponsible AI Implementation Pathway and the Intelligence Community (IC) AI Ethics Principles and \nFramework. The implementation of these policies to national security and defense activities can be informed by \nthe Blueprint for an AI Bill of Rights where feasible. \nThe Blueprint for an AI Bill of Rights is not intended to, and does not, create any legal right, benefit, or \ndefense, substantive or procedural, enforceable at law or in equity by any party against the United States, its \ndepartments, agencies, or entities, its officers, employees, or agents, or any other person, nor does it constitute a \nwaiver of sovereign immunity. \nCopyright Information \nThis document is a work of the United States Government and is in the public domain (see 17 U.S.C. §105). \n2', "APPENDIX\n• OSTP conducted meetings with a variety of stakeholders in the private sector and civil society. Some of these\nmeetings were specifically focused on providing ideas related to the development of the Blueprint for an AI\nBill of Rights while others provided useful general context on the positive use cases, potential harms, and/or\noversight possibilities for these technologies. Participants in these conversations from the private sector and\ncivil society included:\nAdobe \nAmerican 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 \nA.I. Working Group\nVibrent HealthWarehouse Worker ResourceCenterWaymap\n62", ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:----------| | cosine_accuracy@1 | 0.7222 | | cosine_accuracy@3 | 0.9815 | | cosine_accuracy@5 | 1.0 | | cosine_accuracy@10 | 1.0 | | cosine_precision@1 | 0.7222 | | cosine_precision@3 | 0.3272 | | cosine_precision@5 | 0.2 | | cosine_precision@10 | 0.1 | | cosine_recall@1 | 0.7222 | | cosine_recall@3 | 0.9815 | | cosine_recall@5 | 1.0 | | cosine_recall@10 | 1.0 | | cosine_ndcg@10 | 0.8816 | | cosine_mrr@10 | 0.841 | | **cosine_map@100** | **0.841** | | dot_accuracy@1 | 0.7037 | | dot_accuracy@3 | 0.9815 | | dot_accuracy@5 | 1.0 | | dot_accuracy@10 | 1.0 | | dot_precision@1 | 0.7037 | | dot_precision@3 | 0.3272 | | dot_precision@5 | 0.2 | | dot_precision@10 | 0.1 | | dot_recall@1 | 0.7037 | | dot_recall@3 | 0.9815 | | dot_recall@5 | 1.0 | | dot_recall@10 | 1.0 | | dot_ndcg@10 | 0.8748 | | dot_mrr@10 | 0.8318 | | dot_map@100 | 0.8318 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 224 training samples * Columns: sentence_0 and sentence_1 * Approximate statistics based on the first 224 samples: | | sentence_0 | sentence_1 | |:--------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence_0 | sentence_1 | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What are the primary objectives outlined in the "Blueprint for an AI Bill of Rights" as it pertains to the American people? | BLUEPRINT FOR AN
AI B ILL OF
RIGHTS
MAKING AUTOMATED
SYSTEMS WORK FOR
THE AMERICAN PEOPLE
OCTOBER 2022 | | In what ways does the document propose to ensure that automated systems are designed to work effectively for the benefit of society? | BLUEPRINT FOR AN
AI B ILL OF
RIGHTS
MAKING AUTOMATED
SYSTEMS WORK FOR
THE AMERICAN PEOPLE
OCTOBER 2022 | | What is the primary purpose of the Blueprint for an AI Bill of Rights as outlined by the White House Office of Science and Technology Policy? | About this Document
The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People was
published by the White House Office of Science and Technology Policy in October 2022. This framework was
released one year after OSTP announced the launch of a process to develop “a bill of rights for an AI-powered
world.” Its release follows a year of public engagement to inform this initiative. The framework is available
online at: https://www.whitehouse.gov/ostp/ai-bill-of-rights
About the Office of Science and Technology Policy
The Office of Science and Technology Policy (OSTP) was established by the National Science and Technology
Policy, Organization, and Priorities Act of 1976 to provide the President and others within the Executive Office
of the President with advice on the scientific, engineering, and technological aspects of the economy, national
security, health, foreign relations, the environment, and the technological recovery and use of resources, among
other topics. OSTP leads interagency science and technology policy coordination efforts, assists the Office of
Management and Budget (OMB) with an annual review and analysis of Federal research and development in
budgets, and serves as a source of scientific and technological analysis and judgment for the President with
respect to major policies, plans, and programs of the Federal Government.
Legal Disclaimer
The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People is a white paper
published by the White House Office of Science and Technology Policy. It is intended to support the
development of policies and practices that protect civil rights and promote democratic values in the building,
deployment, and governance of automated systems.
The Blueprint for an AI Bill of Rights is non-binding and does not constitute U.S. government policy. It
does not supersede, modify, or direct an interpretation of any existing statute, regulation, policy, or
international instrument. It does not constitute binding guidance for the public or Federal agencies and
therefore does not require compliance with the principles described herein. It also is not determinative of what
the U.S. government’s position will be in any international negotiation. Adoption of these principles may not
meet the requirements of existing statutes, regulations, policies, or international instruments, or the
requirements of the Federal agencies that enforce them. These principles are not intended to, and do not,
prohibit or limit any lawful activity of a government agency, including law enforcement, national security, or
intelligence activities.
The appropriate application of the principles set forth in this white paper depends significantly on the
context in which automated systems are being utilized. In some circumstances, application of these principles
in whole or in part may not be appropriate given the intended use of automated systems to achieve government
agency missions. Future sector-specific guidance will likely be necessary and important for guiding the use of
automated systems in certain settings such as AI systems used as part of school building security or automated
health diagnostic systems.
The Blueprint for an AI Bill of Rights recognizes that law enforcement activities require a balancing of
equities, for example, between the protection of sensitive law enforcement information and the principle of
notice; as such, notice may not be appropriate, or may need to be adjusted to protect sources, methods, and
other law enforcement equities. Even in contexts where these principles may not apply in whole or in part,
federal departments and agencies remain subject to judicial, privacy, and civil liberties oversight as well as
existing policies and safeguards that govern automated systems, including, for example, Executive Order 13960,
Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government (December 2020).
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. | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 5 - `per_device_eval_batch_size`: 5 - `num_train_epochs`: 5 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 5 - `per_device_eval_batch_size`: 5 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `eval_use_gather_object`: False - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin
### Training Logs | Epoch | Step | cosine_map@100 | |:-----:|:----:|:--------------:| | 1.0 | 45 | 0.8410 | ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.1.1 - Transformers: 4.44.2 - PyTorch: 2.4.1+cu121 - Accelerate: 0.34.2 - Datasets: 3.0.1 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```