--- base_model: Snowflake/snowflake-arctic-embed-m 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:200 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: What measures should be taken to ensure that automated systems are safe and effective before deployment? sentences: - " AI BILL 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 community. They should \nstemming\ \ from unintended, yet foreseeable, uses or \n \n \n \n \n \n \n \nSECTION 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 PROTECTIONS\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 \nand \ncontinuous \nmeasures \nto \nprotect\ \ \nindividuals \nand \ncommunities \nfrom 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\n" - "You should be protected from abusive data practices via built-in \nprotections\ \ and you should have agency over how data about \nyou is used. You should be\ \ protected from violations of privacy through \ndesign choices that ensure such\ \ protections are included by default, including \nensuring that data collection\ \ conforms to reasonable expectations and that \nonly data strictly necessary\ \ for the specific context is collected. Designers, de­\nvelopers, and deployers\ \ of automated systems should seek your permission \nand respect your decisions\ \ regarding collection, use, access, transfer, and de­\nletion of your data in\ \ appropriate ways and to the greatest extent possible; \nwhere not possible,\ \ alternative privacy by design safeguards should be used. \nSystems should not\ \ employ user experience and design decisions that obfus­\ncate user choice or\ \ burden users with defaults that are privacy invasive. Con­\nsent should only\ \ be used to justify collection of data in cases where it can be \nappropriately\ \ and meaningfully given. Any consent requests should be brief, \nbe understandable\ \ in plain language, and give you agency over data collection \nand the specific\ \ context of use; current hard-to-understand no­\ntice-and-choice practices for\ \ broad uses of data should be changed. Enhanced \nprotections and restrictions\ \ for data and inferences related to sensitive do­\nmains, including health, work,\ \ education, criminal justice, and finance, and \nfor data pertaining to youth\ \ should put you first. In sensitive domains, your \ndata and related inferences\ \ should only be used for necessary functions, and \nyou should be protected by\ \ ethical review and use prohibitions. You and your \ncommunities should be free\ \ from unchecked surveillance; surveillance tech­\nnologies should be subject\ \ to heightened oversight that includes at least \npre-deployment assessment of\ \ their potential harms and scope limits to pro­\ntect privacy and civil liberties.\ \ Continuous surveillance and monitoring \nshould not be used in education, work,\ \ housing, or in other contexts where the \nuse of such surveillance technologies\ \ is likely to limit rights, opportunities, or \naccess. Whenever possible, you\ \ should have access to reporting that confirms \nyour data decisions have been\ \ respected and provides an assessment of the \npotential impact of surveillance\ \ technologies on your rights, opportunities, or \naccess. \nDATA PRIVACY\n30\n" - "APPENDIX\nLisa Feldman Barrett \nMadeline Owens \nMarsha Tudor \nMicrosoft Corporation\ \ \nMITRE Corporation \nNational Association for the \nAdvancement of Colored\ \ People \nLegal Defense and Educational \nFund \nNational Association of Criminal\ \ \nDefense Lawyers \nNational Center for Missing & \nExploited Children \nNational\ \ Fair Housing Alliance \nNational Immigration Law Center \nNEC Corporation of\ \ America \nNew America’s Open Technology \nInstitute \nNew York Civil Liberties\ \ Union \nNo Name Provided \nNotre Dame Technology Ethics \nCenter \nOffice of\ \ the Ohio Public Defender \nOnfido \nOosto \nOrissa Rose \nPalantir \nPangiam\ \ \nParity Technologies \nPatrick A. Stewart, Jeffrey K. \nMullins, and Thomas\ \ J. Greitens \nPel Abbott \nPhiladelphia Unemployment \nProject \nProject On\ \ Government Oversight \nRecording Industry Association of \nAmerica \nRobert\ \ Wilkens \nRon Hedges \nScience, Technology, and Public \nPolicy Program at University\ \ of \nMichigan Ann Arbor \nSecurity Industry Association \nSheila Dean \nSoftware\ \ & Information Industry \nAssociation \nStephanie Dinkins and the Future \nHistories\ \ Studio at Stony Brook \nUniversity \nTechNet \nThe Alliance for Media Arts and\ \ \nCulture, MIT Open Documentary \nLab and Co-Creation Studio, and \nImmerse\ \ \nThe International Brotherhood of \nTeamsters \nThe Leadership Conference on\ \ \nCivil and Human Rights \nThorn \nU.S. Chamber of Commerce’s \nTechnology Engagement\ \ Center \nUber Technologies \nUniversity of Pittsburgh \nUndergraduate Student\ \ \nCollaborative \nUpturn \nUS Technology Policy Committee \nof the Association\ \ of Computing \nMachinery \nVirginia Puccio \nVisar Berisha and Julie Liss \n\ XR Association \nXR Safety Initiative \n• As an additional effort to reach out\ \ to stakeholders regarding the RFI, OSTP conducted two listening sessions\nfor\ \ members of the public. The listening sessions together drew upwards of 300 participants.\ \ The Science and\nTechnology Policy Institute produced a synopsis of both the\ \ RFI submissions and the feedback at the listening\nsessions.115\n61\n" - source_sentence: How does the document address algorithmic discrimination protections? sentences: - " \n \n \n \n \n \n \n \n \n \n \n \nSAFE AND EFFECTIVE \nSYSTEMS \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.\ \ \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\n\ ed conditions. This ongoing monitoring should include continuous evaluation of\ \ performance metrics and \nharm assessments, updates of any systems, and retraining\ \ of any machine learning models as necessary, as well \nas ensuring that fallback\ \ mechanisms are in place to allow reversion to a previously working system. Monitor­\n\ ing should take into account the performance of both technical system components\ \ (the algorithm as well as \nany hardware components, data inputs, etc.) and\ \ human operators. It should include mechanisms for testing \nthe actual accuracy\ \ of any predictions or recommendations generated by a system, not just a human\ \ operator’s \ndetermination of their accuracy. Ongoing monitoring procedures\ \ should include manual, human-led monitor­\ning as a check in the event there\ \ are shortcomings in automated monitoring systems. These monitoring proce­\n\ dures should be in place for the lifespan of the deployed automated system. \n\ Clear organizational oversight. Entities responsible for the development or use\ \ of automated systems \nshould lay out clear governance structures and procedures.\ \ This includes clearly-stated governance proce­\ndures before deploying the\ \ system, as well as responsibility of specific individuals or entities to oversee\ \ ongoing \nassessment and mitigation. Organizational stakeholders including those\ \ with oversight of the business process \nor operation being automated, as well\ \ as other organizational divisions that may be affected due to the use of \n\ the system, should be involved in establishing governance procedures. Responsibility\ \ should rest high enough \nin the organization that decisions about resources,\ \ mitigation, incident response, and potential rollback can be \nmade promptly,\ \ with sufficient weight given to risk mitigation objectives against competing\ \ concerns. Those \nholding this responsibility should be made aware of any use\ \ cases with the potential for meaningful impact on \npeople’s rights, opportunities,\ \ or access as determined based on risk identification procedures. In some cases,\ \ \nit 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 \nreuse \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\n" - "TABLE OF CONTENTS\nFROM PRINCIPLES TO PRACTICE: A TECHNICAL COMPANION TO THE\ \ BLUEPRINT \nFOR AN AI BILL OF RIGHTS \n \nUSING THIS TECHNICAL COMPANION\n \n\ SAFE AND EFFECTIVE SYSTEMS\n \nALGORITHMIC DISCRIMINATION PROTECTIONS\n \nDATA\ \ PRIVACY\n \nNOTICE AND EXPLANATION\n \nHUMAN ALTERNATIVES, CONSIDERATION, AND\ \ FALLBACK\nAPPENDIX\n \nEXAMPLES OF AUTOMATED SYSTEMS\n \nLISTENING TO THE AMERICAN\ \ PEOPLE\nENDNOTES \n12\n14\n15\n23\n30\n40\n46\n53\n53\n55\n63\n13\n" - "APPENDIX\nSystems that impact the safety of communities such as automated traffic\ \ control systems, elec \n-ctrical grid controls, smart city technologies, and\ \ industrial emissions and environmental\nimpact control algorithms; and\nSystems\ \ related to access to benefits or services or assignment of penalties such as\ \ systems that\nsupport decision-makers who adjudicate benefits such as collating\ \ or analyzing information or\nmatching records, systems which similarly assist\ \ in the adjudication of administrative or criminal\npenalties, fraud detection\ \ algorithms, services or benefits access control algorithms, biometric\nsystems\ \ used as access control, and systems which make benefits or services related\ \ decisions on a\nfully or partially autonomous basis (such as a determination\ \ to revoke benefits).\n54\n" - source_sentence: What legislation is referenced in the context that became effective on October 3, 2008, regarding biometric information? sentences: - " \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\ HOW THESE PRINCIPLES CAN MOVE INTO PRACTICE\nReal-life examples of how these principles\ \ can become reality, through laws, policies, and practical \ntechnical and sociotechnical\ \ approaches to protecting rights, opportunities, and access. \nThe federal government\ \ is working to combat discrimination in mortgage lending. The Depart­\nment of\ \ Justice has launched a nationwide initiative to combat redlining, which includes\ \ reviewing how \nlenders who may be avoiding serving communities of color are\ \ conducting targeted marketing and advertising.51 \nThis initiative will draw\ \ upon strong partnerships across federal agencies, including the Consumer Financial\ \ \nProtection Bureau and prudential regulators. The Action Plan to Advance Property\ \ Appraisal and Valuation \nEquity includes a commitment from the agencies that\ \ oversee mortgage lending to include a \nnondiscrimination standard in the proposed\ \ rules for Automated Valuation Models.52\nThe Equal Employment Opportunity Commission\ \ and the Department of Justice have clearly \nlaid out how employers’ use of\ \ AI and other automated systems can result in \ndiscrimination against job applicants\ \ and employees with disabilities.53 The documents explain \nhow employers’ use\ \ of software that relies on algorithmic decision-making may violate existing\ \ requirements \nunder Title I of the Americans with Disabilities Act (“ADA”).\ \ This technical assistance also provides practical \ntips to employers on how\ \ to comply with the ADA, and to job applicants and employees who think that their\ \ \nrights may have been violated. \nDisparity assessments identified harms to\ \ Black patients' healthcare access. A widely \nused healthcare algorithm relied\ \ on the cost of each patient’s past medical care to predict future medical needs,\ \ \nrecommending early interventions for the patients deemed most at risk. This\ \ process discriminated \nagainst Black patients, who generally have less access\ \ to medical care and therefore have generated less cost \nthan white patients\ \ with similar illness and need. A landmark study documented this pattern and\ \ proposed \npractical ways that were shown to reduce this bias, such as focusing\ \ specifically on active chronic health \nconditions or avoidable future costs\ \ related to emergency visits and hospitalization.54 \nLarge employers have developed\ \ best practices to scrutinize the data and models used \nfor hiring. An industry\ \ initiative has developed Algorithmic Bias Safeguards for the Workforce, a structured\ \ \nquestionnaire that businesses can use proactively when procuring software\ \ to evaluate workers. It covers \nspecific technical questions such as the training\ \ data used, model training process, biases identified, and \nmitigation steps\ \ employed.55 \nStandards organizations have developed guidelines to incorporate\ \ accessibility criteria \ninto technology design processes. The most prevalent\ \ in the United States is the Access Board’s Section \n508 regulations,56 which\ \ are the technical standards for federal information communication technology\ \ (software, \nhardware, and web). Other standards include those issued by the\ \ International Organization for \nStandardization,57 and the World Wide Web Consortium\ \ Web Content Accessibility Guidelines,58 a globally \nrecognized voluntary consensus\ \ standard for web content and other information and communications \ntechnology.\ \ \nNIST has released Special Publication 1270, Towards a Standard for Identifying\ \ and Managing Bias \nin Artificial Intelligence.59 The special publication: describes\ \ the stakes and challenges of bias in artificial \nintelligence and provides\ \ examples of how and why it can chip away at public trust; identifies three categories\ \ \nof bias in AI – systemic, statistical, and human – and describes how and where\ \ they contribute to harms; and \ndescribes three broad challenges for mitigating\ \ bias – datasets, testing and evaluation, and human factors – and \nintroduces\ \ preliminary guidance for addressing them. Throughout, the special publication\ \ takes a socio-\ntechnical perspective to identifying and managing AI bias. \n\ 29\nAlgorithmic \nDiscrimination \nProtections \n" - " \n \nENDNOTES\n85. Mick Dumke and Frank Main. A look inside the watch list Chicago\ \ police fought to keep secret. The\nChicago Sun Times. May 18, 2017.\nhttps://chicago.suntimes.com/2017/5/18/18386116/a-look-inside-the-watch-list-chicago-police-fought­\n\ to-keep-secret\n86. Jay Stanley. Pitfalls of Artificial Intelligence Decisionmaking\ \ Highlighted In Idaho ACLU Case.\nACLU. Jun. 2, 2017.\nhttps://www.aclu.org/blog/privacy-technology/pitfalls-artificial-intelligence-decisionmaking­\n\ highlighted-idaho-aclu-case\n87. Illinois General Assembly. Biometric Information\ \ Privacy Act. Effective Oct. 3, 2008.\nhttps://www.ilga.gov/legislation/ilcs/ilcs3.asp?ActID=3004&ChapterID=57\n\ 88. Partnership on AI. ABOUT ML Reference Document. Accessed May 2, 2022.\nhttps://partnershiponai.org/paper/about-ml-reference-document/1/\n\ 89. See, e.g., the model cards framework: Margaret Mitchell, Simone Wu, Andrew\ \ Zaldivar, Parker\nBarnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa\ \ Deborah Raji, and Timnit Gebru.\nModel Cards for Model Reporting. In Proceedings\ \ of the Conference on Fairness, Accountability, and\nTransparency (FAT* '19).\ \ Association for Computing Machinery, New York, NY, USA, 220–229. https://\n\ dl.acm.org/doi/10.1145/3287560.3287596\n90. Sarah Ammermann. Adverse Action Notice\ \ Requirements Under the ECOA and the FCRA. Consumer\nCompliance Outlook. Second\ \ Quarter 2013.\nhttps://consumercomplianceoutlook.org/2013/second-quarter/adverse-action-notice-requirements­\n\ under-ecoa-fcra/\n91. Federal Trade Commission. Using Consumer Reports for Credit\ \ Decisions: What to Know About\nAdverse Action and Risk-Based Pricing Notices.\ \ Accessed May 2, 2022.\nhttps://www.ftc.gov/business-guidance/resources/using-consumer-reports-credit-decisions-what­\n\ know-about-adverse-action-risk-based-pricing-notices#risk\n92. Consumer Financial\ \ Protection Bureau. CFPB Acts to Protect the Public from Black-Box Credit\nModels\ \ Using Complex Algorithms. May 26, 2022.\nhttps://www.consumerfinance.gov/about-us/newsroom/cfpb-acts-to-protect-the-public-from-black­\n\ box-credit-models-using-complex-algorithms/\n93. Anthony Zaller. California Passes\ \ Law Regulating Quotas In Warehouses – What Employers Need to\nKnow About AB\ \ 701. Zaller Law Group California Employment Law Report. Sept. 24, 2021.\nhttps://www.californiaemploymentlawreport.com/2021/09/california-passes-law-regulating-quotas­\n\ in-warehouses-what-employers-need-to-know-about-ab-701/\n94. National Institute\ \ of Standards and Technology. AI Fundamental Research – Explainability.\nAccessed\ \ Jun. 4, 2022.\nhttps://www.nist.gov/artificial-intelligence/ai-fundamental-research-explainability\n\ 95. DARPA. Explainable Artificial Intelligence (XAI). Accessed July 20, 2022.\n\ https://www.darpa.mil/program/explainable-artificial-intelligence\n71\n" - " \nENDNOTES\n12. Expectations about reporting are intended for the entity developing\ \ or using the automated system. The\nresulting reports can be provided to the\ \ public, regulators, auditors, industry standards groups, or others\nengaged\ \ in independent review, and should be made public as much as possible consistent\ \ with law,\nregulation, and policy, and noting that intellectual property or\ \ law enforcement considerations may prevent\npublic release. These reporting\ \ expectations are important for transparency, so the American people can\nhave\ \ confidence that their rights, opportunities, and access as well as their expectations\ \ around\ntechnologies are respected.\n13. National Artificial Intelligence Initiative\ \ Office. Agency Inventories of AI Use Cases. Accessed Sept. 8,\n2022. https://www.ai.gov/ai-use-case-inventories/\n\ 14. National Highway Traffic Safety Administration. https://www.nhtsa.gov/\n15.\ \ See, e.g., Charles Pruitt. People Doing What They Do Best: The Professional\ \ Engineers and NHTSA. Public\nAdministration Review. Vol. 39, No. 4. Jul.-Aug.,\ \ 1979. https://www.jstor.org/stable/976213?seq=1\n16. The US Department of Transportation\ \ has publicly described the health and other benefits of these\n“traffic calming”\ \ measures. See, e.g.: U.S. Department of Transportation. Traffic Calming to Slow\ \ Vehicle\nSpeeds. Accessed Apr. 17, 2022. https://www.transportation.gov/mission/health/Traffic-Calming-to-Slow­\n\ Vehicle-Speeds\n17. Karen Hao. Worried about your firm’s AI ethics? These startups\ \ are here to help.\nA growing ecosystem of “responsible AI” ventures promise\ \ to help organizations monitor and fix their AI\nmodels. MIT Technology Review.\ \ Jan 15., 2021.\nhttps://www.technologyreview.com/2021/01/15/1016183/ai-ethics-startups/;\ \ Disha Sinha. Top Progressive\nCompanies Building Ethical AI to Look Out for\ \ in 2021. Analytics Insight. June 30, 2021. https://\nwww.analyticsinsight.net/top-progressive-companies-building-ethical-ai-to-look-out-for­\n\ in-2021/ https://www.technologyreview.com/2021/01/15/1016183/ai-ethics-startups/;\ \ Disha Sinha. Top\nProgressive Companies Building Ethical AI to Look Out for\ \ in 2021. Analytics Insight. June 30, 2021.\n18. Office of Management and Budget.\ \ Study to Identify Methods to Assess Equity: Report to the President.\nAug. 2021.\ \ https://www.whitehouse.gov/wp-content/uploads/2021/08/OMB-Report-on-E013985­\n\ Implementation_508-Compliant-Secure-v1.1.pdf\n19. National Institute of Standards\ \ and Technology. AI Risk Management Framework. Accessed May 23,\n2022. https://www.nist.gov/itl/ai-risk-management-framework\n\ 20. U.S. Department of Energy. U.S. Department of Energy Establishes Artificial\ \ Intelligence Advancement\nCouncil. U.S. Department of Energy Artificial Intelligence\ \ and Technology Office. April 18, 2022. https://\nwww.energy.gov/ai/articles/us-department-energy-establishes-artificial-intelligence-advancement-council\n\ 21. Department of Defense. U.S Department of Defense Responsible Artificial Intelligence\ \ Strategy and\nImplementation Pathway. Jun. 2022. https://media.defense.gov/2022/Jun/22/2003022604/-1/-1/0/\n\ Department-of-Defense-Responsible-Artificial-Intelligence-Strategy-and-Implementation­\n\ Pathway.PDF\n22. Director of National Intelligence. Principles of Artificial Intelligence\ \ Ethics for the Intelligence\nCommunity. https://www.dni.gov/index.php/features/2763-principles-of-artificial-intelligence-ethics-for­\n\ the-intelligence-community\n64\n" - source_sentence: How does the Blueprint for an AI Bill of Rights relate to existing laws and regulations regarding automated systems? sentences: - " \n \n \n \n \n \n \n \n \n \n \n \n \n \nAbout this Document \nThe Blueprint\ \ for an AI Bill of Rights: Making Automated Systems Work for the American People\ \ was \npublished by the White House Office of Science and Technology Policy in\ \ October 2022. This framework was \nreleased one year after OSTP announced the\ \ launch of a process to develop “a bill of rights for an AI-powered \nworld.”\ \ Its release follows a year of public engagement to inform this initiative. The\ \ framework is available \nonline at: https://www.whitehouse.gov/ostp/ai-bill-of-rights\ \ \nAbout the Office of Science and Technology Policy \nThe Office of Science\ \ and Technology Policy (OSTP) was established by the National Science and Technology\ \ \nPolicy, Organization, and Priorities Act of 1976 to provide the President\ \ and others within the Executive Office \nof the President with advice on the\ \ scientific, engineering, and technological aspects of the economy, national\ \ \nsecurity, health, foreign relations, the environment, and the technological\ \ recovery and use of resources, among \nother topics. OSTP leads interagency\ \ science and technology policy coordination efforts, assists the Office of \n\ Management and Budget (OMB) with an annual review and analysis of Federal research\ \ and development in \nbudgets, and serves as a source of scientific and technological\ \ analysis and judgment for the President with \nrespect to major policies, plans,\ \ and programs of the Federal Government. \nLegal Disclaimer \nThe Blueprint for\ \ an AI Bill of Rights: Making Automated Systems Work for the American People\ \ is a white paper \npublished by the White House Office of Science and Technology\ \ Policy. It is intended to support the \ndevelopment of policies and practices\ \ that protect civil rights and promote democratic values in the building, \n\ deployment, and governance of automated systems. \nThe Blueprint for an AI Bill\ \ of Rights is non-binding and does not constitute U.S. government policy. It\ \ \ndoes not supersede, modify, or direct an interpretation of any existing statute,\ \ regulation, policy, or \ninternational instrument. It does not constitute binding\ \ guidance for the public or Federal agencies and \ntherefore does not require\ \ compliance with the principles described herein. It also is not determinative\ \ of what \nthe U.S. government’s position will be in any international negotiation.\ \ Adoption of these principles may not \nmeet the requirements of existing statutes,\ \ regulations, policies, or international instruments, or the \nrequirements of\ \ the Federal agencies that enforce them. These principles are not intended to,\ \ and do not, \nprohibit or limit any lawful activity of a government agency,\ \ including law enforcement, national security, or \nintelligence activities.\ \ \nThe appropriate application of the principles set forth in this white paper\ \ depends significantly on the \ncontext in which automated systems are being\ \ utilized. In some circumstances, application of these principles \nin whole\ \ or in part may not be appropriate given the intended use of automated systems\ \ to achieve government \nagency missions. Future sector-specific guidance will\ \ likely be necessary and important for guiding the use of \nautomated systems\ \ in certain settings such as AI systems used as part of school building security\ \ or automated \nhealth diagnostic systems. \nThe Blueprint for an AI Bill of\ \ Rights recognizes that law enforcement activities require a balancing of \n\ equities, for example, between the protection of sensitive law enforcement information\ \ and the principle of \nnotice; as such, notice may not be appropriate, or may\ \ need to be adjusted to protect sources, methods, and \nother law enforcement\ \ equities. Even in contexts where these principles may not apply in whole or\ \ in part, \nfederal departments and agencies remain subject to judicial, privacy,\ \ and civil liberties oversight as well as \nexisting policies and safeguards\ \ that govern automated systems, including, for example, Executive Order 13960,\ \ \nPromoting the Use of Trustworthy Artificial Intelligence in the Federal Government\ \ (December 2020). \nThis 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 \n\ Responsible 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\n" - " \nENDNOTES\n12. Expectations about reporting are intended for the entity developing\ \ or using the automated system. The\nresulting reports can be provided to the\ \ public, regulators, auditors, industry standards groups, or others\nengaged\ \ in independent review, and should be made public as much as possible consistent\ \ with law,\nregulation, and policy, and noting that intellectual property or\ \ law enforcement considerations may prevent\npublic release. These reporting\ \ expectations are important for transparency, so the American people can\nhave\ \ confidence that their rights, opportunities, and access as well as their expectations\ \ around\ntechnologies are respected.\n13. National Artificial Intelligence Initiative\ \ Office. Agency Inventories of AI Use Cases. Accessed Sept. 8,\n2022. https://www.ai.gov/ai-use-case-inventories/\n\ 14. National Highway Traffic Safety Administration. https://www.nhtsa.gov/\n15.\ \ See, e.g., Charles Pruitt. People Doing What They Do Best: The Professional\ \ Engineers and NHTSA. Public\nAdministration Review. Vol. 39, No. 4. Jul.-Aug.,\ \ 1979. https://www.jstor.org/stable/976213?seq=1\n16. The US Department of Transportation\ \ has publicly described the health and other benefits of these\n“traffic calming”\ \ measures. See, e.g.: U.S. Department of Transportation. Traffic Calming to Slow\ \ Vehicle\nSpeeds. Accessed Apr. 17, 2022. https://www.transportation.gov/mission/health/Traffic-Calming-to-Slow­\n\ Vehicle-Speeds\n17. Karen Hao. Worried about your firm’s AI ethics? These startups\ \ are here to help.\nA growing ecosystem of “responsible AI” ventures promise\ \ to help organizations monitor and fix their AI\nmodels. MIT Technology Review.\ \ Jan 15., 2021.\nhttps://www.technologyreview.com/2021/01/15/1016183/ai-ethics-startups/;\ \ Disha Sinha. Top Progressive\nCompanies Building Ethical AI to Look Out for\ \ in 2021. Analytics Insight. June 30, 2021. https://\nwww.analyticsinsight.net/top-progressive-companies-building-ethical-ai-to-look-out-for­\n\ in-2021/ https://www.technologyreview.com/2021/01/15/1016183/ai-ethics-startups/;\ \ Disha Sinha. Top\nProgressive Companies Building Ethical AI to Look Out for\ \ in 2021. Analytics Insight. June 30, 2021.\n18. Office of Management and Budget.\ \ Study to Identify Methods to Assess Equity: Report to the President.\nAug. 2021.\ \ https://www.whitehouse.gov/wp-content/uploads/2021/08/OMB-Report-on-E013985­\n\ Implementation_508-Compliant-Secure-v1.1.pdf\n19. National Institute of Standards\ \ and Technology. AI Risk Management Framework. Accessed May 23,\n2022. https://www.nist.gov/itl/ai-risk-management-framework\n\ 20. U.S. Department of Energy. U.S. Department of Energy Establishes Artificial\ \ Intelligence Advancement\nCouncil. U.S. Department of Energy Artificial Intelligence\ \ and Technology Office. April 18, 2022. https://\nwww.energy.gov/ai/articles/us-department-energy-establishes-artificial-intelligence-advancement-council\n\ 21. Department of Defense. U.S Department of Defense Responsible Artificial Intelligence\ \ Strategy and\nImplementation Pathway. Jun. 2022. https://media.defense.gov/2022/Jun/22/2003022604/-1/-1/0/\n\ Department-of-Defense-Responsible-Artificial-Intelligence-Strategy-and-Implementation­\n\ Pathway.PDF\n22. Director of National Intelligence. Principles of Artificial Intelligence\ \ Ethics for the Intelligence\nCommunity. https://www.dni.gov/index.php/features/2763-principles-of-artificial-intelligence-ethics-for­\n\ the-intelligence-community\n64\n" - " \n12 \nCSAM. Even when trained on “clean” data, increasingly capable GAI models\ \ can synthesize or produce \nsynthetic NCII and CSAM. Websites, mobile apps,\ \ and custom-built models that generate synthetic NCII \nhave moved from niche\ \ internet forums to mainstream, automated, and scaled online businesses. \n\ Trustworthy AI Characteristics: Fair with Harmful Bias Managed, Safe, Privacy\ \ Enhanced \n2.12. \nValue Chain and Component Integration \nGAI value chains\ \ involve many third-party components such as procured datasets, pre-trained models,\ \ \nand software libraries. These components might be improperly obtained or not\ \ properly vetted, leading \nto diminished transparency or accountability for\ \ downstream users. While this is a risk for traditional AI \nsystems and some\ \ other digital technologies, the risk is exacerbated for GAI due to the scale\ \ of the \ntraining data, which may be too large for humans to vet; the difficulty\ \ of training foundation models, \nwhich leads to extensive reuse of limited numbers\ \ of models; and the extent to which GAI may be \nintegrated into other devices\ \ and services. As GAI systems often involve many distinct third-party \ncomponents\ \ and data sources, it may be difficult to attribute issues in a system’s behavior\ \ to any one of \nthese sources. \nErrors in third-party GAI components can also\ \ have downstream impacts on accuracy and robustness. \nFor example, test datasets\ \ commonly used to benchmark or validate models can contain label errors. \nInaccuracies\ \ in these labels can impact the “stability” or robustness of these benchmarks,\ \ which many \nGAI practitioners consider during the model selection process.\ \ \nTrustworthy AI Characteristics: Accountable and Transparent, Explainable\ \ and Interpretable, Fair with \nHarmful Bias Managed, Privacy Enhanced, Safe,\ \ Secure and Resilient, Valid and Reliable \n3. \nSuggested Actions to Manage\ \ GAI Risks \nThe following suggested actions target risks unique to or exacerbated\ \ by GAI. \nIn addition to the suggested actions below, AI risk management activities\ \ and actions set forth in the AI \nRMF 1.0 and Playbook are already applicable\ \ for managing GAI risks. Organizations are encouraged to \napply the activities\ \ suggested in the AI RMF and its Playbook when managing the risk of GAI systems.\ \ \nImplementation of the suggested actions will vary depending on the type of\ \ risk, characteristics of GAI \nsystems, stage of the GAI lifecycle, and relevant\ \ AI actors involved. \nSuggested actions to manage GAI risks can be found in\ \ the tables below: \n• \nThe suggested actions are organized by relevant AI RMF\ \ subcategories to streamline these \nactivities alongside implementation of the\ \ AI RMF. \n• \nNot every subcategory of the AI RMF is included in this document.13\ \ Suggested actions are \nlisted for only some subcategories. \n \n \n13 As this\ \ document was focused on the GAI PWG efforts and primary considerations (see Appendix\ \ A), AI RMF \nsubcategories not addressed here may be added later. \n" - source_sentence: What proactive steps should be taken during the design phase of automated systems to assess equity and prevent algorithmic discrimination? sentences: - " \n \n \n \n \n \n \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. \nAny automated system should be tested to help ensure\ \ it is free from algorithmic discrimination before it can be \nsold or used.\ \ Protection against algorithmic discrimination should include designing to ensure\ \ equity, broadly \nconstrued. Some algorithmic discrimination is already prohibited\ \ under existing anti-discrimination law. The \nexpectations set out below describe\ \ proactive technical and policy steps that can be taken to not only \nreinforce\ \ those legal protections but extend beyond them to ensure equity for underserved\ \ communities48 \neven in circumstances where a specific legal protection may\ \ not be clearly established. These protections \nshould be instituted throughout\ \ the design, development, and deployment process and are described below \nroughly\ \ in the order in which they would be instituted. \nProtect the public from algorithmic\ \ discrimination in a proactive and ongoing manner \nProactive assessment of equity\ \ in design. Those responsible for the development, use, or oversight of \nautomated\ \ systems should conduct proactive equity assessments in the design phase of the\ \ technology \nresearch and development or during its acquisition to review potential\ \ input data, associated historical \ncontext, accessibility for people with disabilities,\ \ and societal goals to identify potential discrimination and \neffects on equity\ \ resulting from the introduction of the technology. The assessed groups should\ \ be as inclusive \nas possible of the underserved communities mentioned in the\ \ equity definition: Black, Latino, and Indigenous \nand Native American persons,\ \ Asian Americans and Pacific Islanders and other persons of color; members of\ \ \nreligious minorities; women, girls, and non-binary people; lesbian, gay, bisexual,\ \ transgender, queer, and inter-\nsex (LGBTQI+) persons; older adults; persons\ \ with disabilities; persons who live in rural areas; and persons \notherwise\ \ adversely affected by persistent poverty or inequality. Assessment could include\ \ both qualitative \nand quantitative evaluations of the system. This equity assessment\ \ should also be considered a core part of the \ngoals of the consultation conducted\ \ as part of the safety and efficacy review. \nRepresentative and robust data.\ \ Any data used as part of system development or assessment should be \nrepresentative\ \ of local communities based on the planned deployment setting and should be reviewed\ \ for bias \nbased on the historical and societal context of the data. Such data\ \ should be sufficiently robust to identify and \nhelp to mitigate biases and\ \ potential harms. \nGuarding against proxies. Directly using demographic information\ \ in the design, development, or \ndeployment of an automated system (for purposes\ \ other than evaluating a system for discrimination or using \na system to counter\ \ discrimination) runs a high risk of leading to algorithmic discrimination and\ \ should be \navoided. In many cases, attributes that are highly correlated with\ \ demographic features, known as proxies, can \ncontribute to algorithmic discrimination.\ \ In cases where use of the demographic features themselves would \nlead to illegal\ \ algorithmic discrimination, reliance on such proxies in decision-making (such\ \ as that facilitated \nby an algorithm) may also be prohibited by law. Proactive\ \ testing should be performed to identify proxies by \ntesting for correlation\ \ between demographic information and attributes in any data used as part of system\ \ \ndesign, development, or use. If a proxy is identified, designers, developers,\ \ and deployers should remove the \nproxy; if needed, it may be possible to identify\ \ alternative attributes that can be used instead. At a minimum, \norganizations\ \ should ensure a proxy feature is not given undue weight and should monitor the\ \ system closely \nfor any resulting algorithmic discrimination. \n26\nAlgorithmic\ \ \nDiscrimination \nProtections \n" - " \n \n \n \n \n \n \nHUMAN ALTERNATIVES, \nCONSIDERATION, AND \nFALLBACK \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.\ \ \nEquitable. Consideration should be given to ensuring outcomes of the fallback\ \ and escalation system are \nequitable when compared to those of the automated\ \ system and such that the fallback and escalation \nsystem provides equitable\ \ access to underserved communities.105 \nTimely. Human consideration and fallback\ \ are only useful if they are conducted and concluded in a \ntimely manner. The\ \ determination of what is timely should be made relative to the specific automated\ \ \nsystem, and the review system should be staffed and regularly assessed to\ \ ensure it is providing timely \nconsideration and fallback. In time-critical\ \ systems, this mechanism should be immediately available or, \nwhere possible,\ \ available before the harm occurs. Time-critical systems include, but are not\ \ limited to, \nvoting-related systems, automated building access and other access\ \ systems, systems that form a critical \ncomponent of healthcare, and systems\ \ that have the ability to withhold wages or otherwise cause \nimmediate financial\ \ penalties. \nEffective. The organizational structure surrounding processes for\ \ consideration and fallback should \nbe designed so that if the human decision-maker\ \ charged with reassessing a decision determines that it \nshould be overruled,\ \ the new decision will be effectively enacted. This includes ensuring that the\ \ new \ndecision is entered into the automated system throughout its components,\ \ any previous repercussions from \nthe old decision are also overturned, and\ \ safeguards are put in place to help ensure that future decisions do \nnot result\ \ in the same errors. \nMaintained. The human consideration and fallback process\ \ and any associated automated processes \nshould be maintained and supported\ \ as long as the relevant automated system continues to be in use. \nInstitute\ \ training, assessment, and oversight to combat automation bias and ensure any\ \ \nhuman-based components of a system are effective. \nTraining and assessment.\ \ Anyone administering, interacting with, or interpreting the outputs of an auto­\n\ mated system should receive training in that system, including how to properly\ \ interpret outputs of a system \nin light of its intended purpose and in how\ \ to mitigate the effects of automation bias. The training should reoc­\ncur regularly\ \ to ensure it is up to date with the system and to ensure the system is used\ \ appropriately. Assess­\nment should be ongoing to ensure that the use of the\ \ system with human involvement provides for appropri­\nate results, i.e., that\ \ the involvement of people does not invalidate the system's assessment as safe\ \ and effective \nor lead to algorithmic discrimination. \nOversight. Human-based\ \ systems have the potential for bias, including automation bias, as well as other\ \ \nconcerns that may limit their effectiveness. The results of assessments of\ \ the efficacy and potential bias of \nsuch human-based systems should be overseen\ \ by governance structures that have the potential to update the \noperation of\ \ the human-based system in order to mitigate these effects. \n50\n" - " \n \n \nApplying 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 \ndata,\ \ behavioral data, geolocation data, data related to interaction with the criminal\ \ justice system, relationship \nhistory and legal status such as custody and\ \ divorce information, and home, work, or school environmental \ndata); or have\ \ the reasonable potential to be used in ways that are likely to expose individuals\ \ to meaningful \nharm, such as a loss of privacy or financial harm due to identity\ \ theft. Data and metadata generated by or about \nthose who are not yet legal\ \ adults is also sensitive, even if not related to a sensitive domain. Such data\ \ includes, \nbut 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 \nharms, including significant adverse effects\ \ on human rights such as autonomy and dignity, 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, \nhealth, family planning\ \ and care, employment, education, criminal justice, and personal finance. In\ \ the context \nof this framework, such domains are considered sensitive whether\ \ or not the specifics of a system context \nwould necessitate coverage under\ \ existing law, and domains and data that are considered sensitive are under­\n\ stood 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 \nretain 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. \nUNDERSERVED 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 “equity.” \n11\n" model-index: - name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-m results: - task: type: information-retrieval name: Information Retrieval dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy@1 value: 0.7 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9666666666666667 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.19333333333333338 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.10000000000000003 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9666666666666667 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8478532019852957 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7983333333333333 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7983333333333333 name: Cosine Map@100 - type: dot_accuracy@1 value: 0.7 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.9 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.9666666666666667 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 1.0 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.7 name: Dot Precision@1 - type: dot_precision@3 value: 0.3 name: Dot Precision@3 - type: dot_precision@5 value: 0.19333333333333338 name: Dot Precision@5 - type: dot_precision@10 value: 0.10000000000000003 name: Dot Precision@10 - type: dot_recall@1 value: 0.7 name: Dot Recall@1 - type: dot_recall@3 value: 0.9 name: Dot Recall@3 - type: dot_recall@5 value: 0.9666666666666667 name: Dot Recall@5 - type: dot_recall@10 value: 1.0 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.8478532019852957 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.7983333333333333 name: Dot Mrr@10 - type: dot_map@100 value: 0.7983333333333333 name: Dot Map@100 --- # SentenceTransformer based on Snowflake/snowflake-arctic-embed-m This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m). It maps sentences & paragraphs to a 768-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:** [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 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': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, '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}) (2): Normalize() ) ``` ## 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("rgtlai/ai-policy-ft") # Run inference sentences = [ 'What proactive steps should be taken during the design phase of automated systems to assess equity and prevent algorithmic discrimination?', ' \n \n \n \n \n \n \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. \nAny automated system should be tested to help ensure it is free from algorithmic discrimination before it can be \nsold or used. Protection against algorithmic discrimination should include designing to ensure equity, broadly \nconstrued. Some algorithmic discrimination is already prohibited under existing anti-discrimination law. The \nexpectations set out below describe proactive technical and policy steps that can be taken to not only \nreinforce those legal protections but extend beyond them to ensure equity for underserved communities48 \neven in circumstances where a specific legal protection may not be clearly established. These protections \nshould be instituted throughout the design, development, and deployment process and are described below \nroughly in the order in which they would be instituted. \nProtect the public from algorithmic discrimination in a proactive and ongoing manner \nProactive assessment of equity in design. Those responsible for the development, use, or oversight of \nautomated systems should conduct proactive equity assessments in the design phase of the technology \nresearch and development or during its acquisition to review potential input data, associated historical \ncontext, accessibility for people with disabilities, and societal goals to identify potential discrimination and \neffects on equity resulting from the introduction of the technology. The assessed groups should be as inclusive \nas possible of the underserved communities mentioned in the equity definition: Black, Latino, and Indigenous \nand Native American persons, Asian Americans and Pacific Islanders and other persons of color; members of \nreligious minorities; women, girls, and non-binary people; lesbian, gay, bisexual, transgender, queer, and inter-\nsex (LGBTQI+) persons; older adults; persons with disabilities; persons who live in rural areas; and persons \notherwise adversely affected by persistent poverty or inequality. Assessment could include both qualitative \nand quantitative evaluations of the system. This equity assessment should also be considered a core part of the \ngoals of the consultation conducted as part of the safety and efficacy review. \nRepresentative and robust data. Any data used as part of system development or assessment should be \nrepresentative of local communities based on the planned deployment setting and should be reviewed for bias \nbased on the historical and societal context of the data. Such data should be sufficiently robust to identify and \nhelp to mitigate biases and potential harms. \nGuarding against proxies. Directly using demographic information in the design, development, or \ndeployment of an automated system (for purposes other than evaluating a system for discrimination or using \na system to counter discrimination) runs a high risk of leading to algorithmic discrimination and should be \navoided. In many cases, attributes that are highly correlated with demographic features, known as proxies, can \ncontribute to algorithmic discrimination. In cases where use of the demographic features themselves would \nlead to illegal algorithmic discrimination, reliance on such proxies in decision-making (such as that facilitated \nby an algorithm) may also be prohibited by law. Proactive testing should be performed to identify proxies by \ntesting for correlation between demographic information and attributes in any data used as part of system \ndesign, development, or use. If a proxy is identified, designers, developers, and deployers should remove the \nproxy; if needed, it may be possible to identify alternative attributes that can be used instead. At a minimum, \norganizations should ensure a proxy feature is not given undue weight and should monitor the system closely \nfor any resulting algorithmic discrimination. \n26\nAlgorithmic \nDiscrimination \nProtections \n', ' \n \n \nApplying 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 \ndata, behavioral data, geolocation data, data related to interaction with the criminal justice system, relationship \nhistory and legal status such as custody and divorce information, and home, work, or school environmental \ndata); or have the reasonable potential to be used in ways that are likely to expose individuals to meaningful \nharm, such as a loss of privacy or financial harm due to identity theft. Data and metadata generated by or about \nthose who are not yet legal adults is also sensitive, even if not related to a sensitive domain. Such data includes, \nbut 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 \nharms, including significant adverse effects on human rights such as autonomy and dignity, as well as civil liber\xad\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, \nhealth, family planning and care, employment, education, criminal justice, and personal finance. In the context \nof this framework, such domains are considered sensitive whether or not the specifics of a system context \nwould necessitate coverage under existing law, and domains and data that are considered sensitive are under\xad\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 \nretain 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. \nUNDERSERVED 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 “equity.” \n11\n', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # 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 | 0.7 | | cosine_accuracy@3 | 0.9 | | cosine_accuracy@5 | 0.9667 | | cosine_accuracy@10 | 1.0 | | cosine_precision@1 | 0.7 | | cosine_precision@3 | 0.3 | | cosine_precision@5 | 0.1933 | | cosine_precision@10 | 0.1 | | cosine_recall@1 | 0.7 | | cosine_recall@3 | 0.9 | | cosine_recall@5 | 0.9667 | | cosine_recall@10 | 1.0 | | cosine_ndcg@10 | 0.8479 | | cosine_mrr@10 | 0.7983 | | **cosine_map@100** | **0.7983** | | dot_accuracy@1 | 0.7 | | dot_accuracy@3 | 0.9 | | dot_accuracy@5 | 0.9667 | | dot_accuracy@10 | 1.0 | | dot_precision@1 | 0.7 | | dot_precision@3 | 0.3 | | dot_precision@5 | 0.1933 | | dot_precision@10 | 0.1 | | dot_recall@1 | 0.7 | | dot_recall@3 | 0.9 | | dot_recall@5 | 0.9667 | | dot_recall@10 | 1.0 | | dot_ndcg@10 | 0.8479 | | dot_mrr@10 | 0.7983 | | dot_map@100 | 0.7983 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 200 training samples * Columns: sentence_0 and sentence_1 * Approximate statistics based on the first 200 samples: | | sentence_0 | sentence_1 | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence_0 | sentence_1 | |:----------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------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| What is the purpose of the AI Bill of Rights mentioned in the context? |









BLUEPRINT FOR AN
AI BILL OF
RIGHTS
MAKING AUTOMATED
SYSTEMS WORK FOR
THE AMERICAN PEOPLE
OCTOBER 2022
| | When was the Blueprint for an AI Bill of Rights published? |









BLUEPRINT FOR AN
AI BILL OF
RIGHTS
MAKING AUTOMATED
SYSTEMS WORK FOR
THE AMERICAN PEOPLE
OCTOBER 2022
| | What is the purpose of the Blueprint for an AI Bill of Rights as published 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
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2
 | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `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`: 16 - `per_device_eval_batch_size`: 16 - `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 | 13 | 0.7303 | | 2.0 | 26 | 0.7356 | | 3.0 | 39 | 0.7828 | | 3.8462 | 50 | 0.7817 | | 4.0 | 52 | 0.7817 | | 5.0 | 65 | 0.7983 | ### Framework Versions - Python: 3.11.10 - Sentence Transformers: 3.1.1 - Transformers: 4.44.2 - PyTorch: 2.4.1 - Accelerate: 0.34.2 - Datasets: 3.0.0 - 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", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### 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} } ```