--- base_model: sentence-transformers/all-MiniLM-L6-v2 library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:180 - loss:MultipleNegativesRankingLoss widget: - source_sentence: How does the National Institute of Standards and Technology (NIST) plan to address risks associated with AI through its risk management framework? sentences: - "APPENDIX\nPanel 3: Equal Opportunities and Civil Justice. This event explored\ \ current and emerging uses of \ntechnology that impact equity of opportunity\ \ in employment, education, and housing. \nWelcome: \n•\nRashida Richardson, Senior\ \ Policy Advisor for Data and Democracy, White House Office of Science and\nTechnology\ \ Policy\n•\nDominique Harrison, Director for Technology Policy, The Joint Center\ \ for Political and Economic\nStudies\nModerator: Jenny Yang, Director, Office\ \ of Federal Contract Compliance Programs, Department of Labor \nPanelists: \n\ •\nChristo Wilson, Associate Professor of Computer Science, Northeastern University\n\ •\nFrida Polli, CEO, Pymetrics\n•\nKaren Levy, Assistant Professor, Department\ \ of Information Science, Cornell University\n•\nNatasha Duarte, Project Director,\ \ Upturn\n•\nElana Zeide, Assistant Professor, University of Nebraska College\ \ of Law\n•\nFabian Rogers, Constituent Advocate, Office of NY State Senator Jabari\ \ Brisport and Community\nAdvocate and Floor Captain, Atlantic Plaza Towers Tenants\ \ Association\nThe individual panelists described the ways in which AI systems\ \ and other technologies are increasingly being \nused to limit access to equal\ \ opportunities in education, housing, and employment. Education-related \nconcerning\ \ uses included the increased use of remote proctoring systems, student location\ \ and facial \nrecognition tracking, teacher evaluation systems, robot teachers,\ \ and more. Housing-related concerning uses \nincluding automated tenant background\ \ screening and facial recognition-based controls to enter or exit \nhousing complexes.\ \ Employment-related concerning uses included discrimination in automated hiring\ \ \nscreening and workplace surveillance. Various panelists raised the limitations\ \ of existing privacy law as a key \nconcern, pointing out that students should\ \ be able to reinvent themselves and require privacy of their student \nrecords\ \ and education-related data in order to do so. The overarching concerns of surveillance\ \ in these \ndomains included concerns about the chilling effects of surveillance\ \ on student expression, inappropriate \ncontrol of tenants via surveillance,\ \ and the way that surveillance of workers blurs the boundary between work \n\ and life and exerts extreme and potentially damaging control over workers' lives.\ \ Additionally, some panelists \npointed out ways that data from one situation\ \ was misapplied in another in a way that limited people's \nopportunities, for\ \ example data from criminal justice settings or previous evictions being used\ \ to block further \naccess to housing. Throughout, various panelists emphasized\ \ that these technologies are being used to shift the \nburden of oversight and\ \ efficiency from employers to workers, schools to students, and landlords to\ \ tenants, in \nways that diminish and encroach on equality of opportunity; assessment\ \ of these technologies should include \nwhether they are genuinely helpful in\ \ solving an identified problem. \nIn discussion of technical and governance interventions\ \ that that are needed to protect against the harms of \nthese technologies, panelists\ \ individually described the importance of: receiving community input into the\ \ \ndesign and use of technologies, public reporting on crucial elements of these\ \ systems, better notice and consent \nprocedures that ensure privacy based on\ \ context and use case, ability to opt-out of using these systems and \nreceive\ \ a fallback to a human process, providing explanations of decisions and how these\ \ systems work, the \nneed for governance including training in using these systems,\ \ ensuring the technological use cases are \ngenuinely related to the goal task\ \ and are locally validated to work, and the need for institution and protection\ \ \nof third party audits to ensure systems continue to be accountable and valid.\ \ \n57" - "NOTICE & \nEXPLANATION \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. \nAutomated systems now determine\ \ opportunities, from employment to credit, and directly shape the American \n\ public’s experiences, from the courtroom to online classrooms, in ways that profoundly\ \ impact people’s lives. But this \nexpansive impact is not always visible. An\ \ applicant might not know whether a person rejected their resume or a \nhiring\ \ algorithm moved them to the bottom of the list. A defendant in the courtroom\ \ might not know if a judge deny­\ning their bail is informed by an automated\ \ system that labeled them “high risk.” From correcting errors to contesting \n\ decisions, people are often denied the knowledge they need to address the impact\ \ of automated systems on their lives. \nNotice and explanations also serve an\ \ important safety and efficacy purpose, allowing experts to verify the reasonable­\n\ ness of a recommendation before enacting it. \nIn order to guard against potential\ \ harms, the American public needs to know if an automated system is being used.\ \ \nClear, brief, and understandable notice is a prerequisite for achieving the\ \ other protections in this framework. Like­\nwise, the public is often unable\ \ to ascertain how or why an automated system has made a decision or contributed\ \ to a \nparticular outcome. The decision-making processes of automated systems\ \ tend to be opaque, complex, and, therefore, \nunaccountable, whether by design\ \ or by omission. These factors can make explanations both more challenging and\ \ \nmore important, and should not be used as a pretext to avoid explaining important\ \ decisions to the people impacted \nby those choices. In the context of automated\ \ systems, clear and valid explanations should be recognized as a baseline \n\ requirement. \nProviding notice has long been a standard practice, and in many\ \ cases is a legal requirement, when, for example, \nmaking a video recording\ \ of someone (outside of a law enforcement or national security context). In some\ \ cases, such \nas credit, lenders are required to provide notice and explanation\ \ to consumers. Techniques used to automate the \nprocess of explaining such systems\ \ are under active research and improvement and such explanations can take many\ \ \nforms. Innovative companies and researchers are rising to the challenge and\ \ creating and deploying explanatory \nsystems that can help the public better\ \ understand decisions that impact them. \nWhile notice and explanation requirements\ \ are already in place in some sectors or situations, the American public \ndeserve\ \ to know consistently and across sectors if an automated system is being used\ \ in a way that impacts their rights, \nopportunities, or access. This knowledge\ \ should provide confidence in how the public is being treated, and trust in the\ \ \nvalidity and reasonable use of automated systems. \n•\nA lawyer representing\ \ an older client with disabilities who had been cut off from Medicaid-funded\ \ home\nhealth-care assistance couldn't determine why, especially since the decision\ \ went against historical access\npractices. In a court hearing, the lawyer learned\ \ from a witness that the state in which the older client\nlived had recently\ \ adopted a new algorithm to determine eligibility.83 The lack of a timely explanation\ \ made it\nharder to understand and contest the decision.\n•\nA formal child welfare\ \ investigation is opened against a parent based on an algorithm and without the\ \ parent\never being notified that data was being collected and used as part of\ \ an algorithmic child maltreatment\nrisk assessment.84 The lack of notice or\ \ an explanation makes it harder for those performing child\nmaltreatment assessments\ \ to validate the risk assessment and denies parents knowledge that could help\ \ them\ncontest a decision.\n41" - "SAFE AND EFFECTIVE \nSYSTEMS \nHOW THESE PRINCIPLES CAN MOVE INTO PRACTICE\n\ Real-life examples of how these principles can become reality, through laws, policies,\ \ and practical \ntechnical and sociotechnical approaches to protecting rights,\ \ opportunities, and access. ­­\nExecutive Order 13960 on Promoting the Use of\ \ Trustworthy Artificial Intelligence in the \nFederal Government requires that\ \ certain federal agencies adhere to nine principles when \ndesigning, developing,\ \ acquiring, or using AI for purposes other than national security or \ndefense.\ \ These principles—while taking into account the sensitive law enforcement and\ \ other contexts in which \nthe federal government may use AI, as opposed to private\ \ sector use of AI—require that AI is: (a) lawful and \nrespectful of our Nation’s\ \ values; (b) purposeful and performance-driven; (c) accurate, reliable, and effective;\ \ (d) \nsafe, secure, and resilient; (e) understandable; (f ) responsible and\ \ traceable; (g) regularly monitored; (h) transpar-\nent; and, (i) accountable.\ \ The Blueprint for an AI Bill of Rights is consistent with the Executive Order.\ \ \nAffected agencies across the federal government have released AI use case\ \ inventories13 and are implementing \nplans to bring those AI systems into compliance\ \ with the Executive Order or retire them. \nThe law and policy landscape for\ \ motor vehicles shows that strong safety regulations—and \nmeasures to address\ \ harms when they occur—can enhance innovation in the context of com-\nplex technologies.\ \ Cars, like automated digital systems, comprise a complex collection of components.\ \ \nThe National Highway Traffic Safety Administration,14 through its rigorous\ \ standards and independent \nevaluation, helps make sure vehicles on our roads\ \ are safe without limiting manufacturers’ ability to \ninnovate.15 At the same\ \ time, rules of the road are implemented locally to impose contextually appropriate\ \ \nrequirements on drivers, such as slowing down near schools or playgrounds.16\n\ From large companies to start-ups, industry is providing innovative solutions\ \ that allow \norganizations to mitigate risks to the safety and efficacy of AI\ \ systems, both before \ndeployment and through monitoring over time.17 These\ \ innovative solutions include risk \nassessments, auditing mechanisms, assessment\ \ of organizational procedures, dashboards to allow for ongoing \nmonitoring,\ \ documentation procedures specific to model assessments, and many other strategies\ \ that aim to \nmitigate risks posed by the use of AI to companies’ reputation,\ \ legal responsibilities, and other product safety \nand effectiveness concerns.\ \ \nThe Office of Management and Budget (OMB) has called for an expansion of opportunities\ \ \nfor meaningful stakeholder engagement in the design of programs and services.\ \ OMB also \npoints to numerous examples of effective and proactive stakeholder\ \ engagement, including the Community-\nBased Participatory Research Program developed\ \ by the National Institutes of Health and the participatory \ntechnology assessments\ \ developed by the National Oceanic and Atmospheric Administration.18\nThe National\ \ Institute of Standards and Technology (NIST) is developing a risk \nmanagement\ \ framework to better manage risks posed to individuals, organizations, and \n\ society by AI.19 The NIST AI Risk Management Framework, as mandated by Congress,\ \ is intended for \nvoluntary use to help incorporate trustworthiness considerations\ \ into the design, development, use, and \nevaluation of AI products, services,\ \ and systems. The NIST framework is being developed through a consensus-\ndriven,\ \ open, transparent, and collaborative process that includes workshops and other\ \ opportunities to provide \ninput. The NIST framework aims to foster the development\ \ of innovative approaches to address \ncharacteristics of trustworthiness including\ \ accuracy, explainability and interpretability, reliability, privacy, \nrobustness,\ \ safety, security (resilience), and mitigation of unintended and/or harmful bias,\ \ as well as of \nharmful \nuses. \nThe \nNIST \nframework \nwill \nconsider \n\ and \nencompass \nprinciples \nsuch \nas \ntransparency, accountability, and fairness\ \ during pre-design, design and development, deployment, use, \nand testing and\ \ evaluation of AI technologies and systems. It is expected to be released in\ \ the winter of 2022-23. \n21" - source_sentence: How should explanations provided by automated systems be tailored to meet the needs of different audiences? sentences: - "You should know that an automated system is being used, \nand understand how\ \ and why it contributes to outcomes \nthat impact you. Designers, developers,\ \ and deployers of automat­\ned systems should provide generally accessible plain\ \ language docu­\nmentation including clear descriptions of the overall system\ \ func­\ntioning and the role automation plays, notice that such systems are in\ \ \nuse, the individual or organization responsible for the system, and ex­\n\ planations of outcomes that are clear, timely, and accessible. Such \nnotice should\ \ be kept up-to-date and people impacted by the system \nshould be notified of\ \ significant use case or key functionality chang­\nes. You should know how and\ \ why an outcome impacting you was de­\ntermined by an automated system, including\ \ when the automated \nsystem is not the sole input determining the outcome. Automated\ \ \nsystems should provide explanations that are technically valid, \nmeaningful\ \ and useful to you and to any operators or others who \nneed to understand the\ \ system, and calibrated to the level of risk \nbased on the context. Reporting\ \ that includes summary information \nabout these automated systems in plain language\ \ and assessments of \nthe clarity and quality of the notice and explanations\ \ should be made \npublic whenever possible. \nNOTICE AND EXPLANATION\n40" - "WHAT 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. \nDemonstrate that the system protects against algorithmic discrimination\ \ \nIndependent evaluation. As described in the section on Safe and Effective\ \ Systems, entities should allow \nindependent evaluation of potential algorithmic\ \ discrimination caused by automated systems they use or \noversee. In the case\ \ of public sector uses, these independent evaluations should be made public unless\ \ law \nenforcement or national security restrictions prevent doing so. Care should\ \ be taken to balance individual \nprivacy with evaluation data access needs;\ \ in many cases, policy-based and/or technological innovations and \ncontrols\ \ allow access to such data without compromising privacy. \nReporting. Entities\ \ responsible for the development or use of automated systems should provide \n\ reporting of an appropriately designed algorithmic impact assessment,50 with clear\ \ specification of who \nperforms the assessment, who evaluates the system, and\ \ how corrective actions are taken (if necessary) in \nresponse to the assessment.\ \ This algorithmic impact assessment should include at least: the results of any\ \ \nconsultation, design stage equity assessments (potentially including qualitative\ \ analysis), accessibility \ndesigns and testing, disparity testing, document\ \ any remaining disparities, and detail any mitigation \nimplementation and assessments.\ \ This algorithmic impact assessment should be made public whenever \npossible.\ \ Reporting should be provided in a clear and machine-readable manner using plain\ \ language to \nallow for more straightforward public accountability. \n28\nAlgorithmic\ \ \nDiscrimination \nProtections" - "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. \nAn automated system should provide demonstrably clear,\ \ timely, understandable, and accessible notice of use, and \nexplanations as\ \ to how and why a decision was made or an action was taken by the system. These\ \ expectations are \nexplained below. \nProvide clear, timely, understandable,\ \ and accessible notice of use and explanations ­\nGenerally accessible plain\ \ language documentation. The entity responsible for using the automated \nsystem\ \ should ensure that documentation describing the overall system (including any\ \ human components) is \npublic and easy to find. The documentation should describe,\ \ in plain language, how the system works and how \nany automated component is\ \ used to determine an action or decision. It should also include expectations\ \ about \nreporting described throughout this framework, such as the algorithmic\ \ impact assessments described as \npart of Algorithmic Discrimination Protections.\ \ \nAccountable. Notices should clearly identify the entity responsible for designing\ \ each component of the \nsystem and the entity using it. \nTimely and up-to-date.\ \ Users should receive notice of the use of automated systems in advance of using\ \ or \nwhile being impacted by the technology. An explanation should be available\ \ with the decision itself, or soon \nthereafter. Notice should be kept up-to-date\ \ and people impacted by the system should be notified of use case \nor key functionality\ \ changes. \nBrief and clear. Notices and explanations should be assessed, such\ \ as by research on users’ experiences, \nincluding user testing, to ensure that\ \ the people using or impacted by the automated system are able to easily \nfind\ \ notices and explanations, read them quickly, and understand and act on them.\ \ This includes ensuring that \nnotices and explanations are accessible to users\ \ with disabilities and are available in the language(s) and read-\ning level\ \ appropriate for the audience. Notices and explanations may need to be available\ \ in multiple forms, \n(e.g., on paper, on a physical sign, or online), in order\ \ to meet these expectations and to be accessible to the \nAmerican public. \n\ Provide explanations as to how and why a decision was made or an action was taken\ \ by an \nautomated system \nTailored to the purpose. Explanations should be tailored\ \ to the specific purpose for which the user is \nexpected to use the explanation,\ \ and should clearly state that purpose. An informational explanation might \n\ differ from an explanation provided to allow for the possibility of recourse,\ \ an appeal, or one provided in the \ncontext of a dispute or contestation process.\ \ For the purposes of this framework, 'explanation' should be \nconstrued broadly.\ \ An explanation need not be a plain-language statement about causality but could\ \ consist of \nany mechanism that allows the recipient to build the necessary\ \ understanding and intuitions to achieve the \nstated purpose. Tailoring should\ \ be assessed (e.g., via user experience research). \nTailored to the target of\ \ the explanation. Explanations should be targeted to specific audiences and \n\ clearly state that audience. An explanation provided to the subject of a decision\ \ might differ from one provided \nto an advocate, or to a domain expert or decision\ \ maker. Tailoring should be assessed (e.g., via user experience \nresearch).\ \ \n43" - source_sentence: What is the purpose of the email address ai-equity@ostpeopgov created by OSTP? sentences: - "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. \nAn automated system should provide demonstrably clear,\ \ timely, understandable, and accessible notice of use, and \nexplanations as\ \ to how and why a decision was made or an action was taken by the system. These\ \ expectations are \nexplained below. \nProvide clear, timely, understandable,\ \ and accessible notice of use and explanations ­\nGenerally accessible plain\ \ language documentation. The entity responsible for using the automated \nsystem\ \ should ensure that documentation describing the overall system (including any\ \ human components) is \npublic and easy to find. The documentation should describe,\ \ in plain language, how the system works and how \nany automated component is\ \ used to determine an action or decision. It should also include expectations\ \ about \nreporting described throughout this framework, such as the algorithmic\ \ impact assessments described as \npart of Algorithmic Discrimination Protections.\ \ \nAccountable. Notices should clearly identify the entity responsible for designing\ \ each component of the \nsystem and the entity using it. \nTimely and up-to-date.\ \ Users should receive notice of the use of automated systems in advance of using\ \ or \nwhile being impacted by the technology. An explanation should be available\ \ with the decision itself, or soon \nthereafter. Notice should be kept up-to-date\ \ and people impacted by the system should be notified of use case \nor key functionality\ \ changes. \nBrief and clear. Notices and explanations should be assessed, such\ \ as by research on users’ experiences, \nincluding user testing, to ensure that\ \ the people using or impacted by the automated system are able to easily \nfind\ \ notices and explanations, read them quickly, and understand and act on them.\ \ This includes ensuring that \nnotices and explanations are accessible to users\ \ with disabilities and are available in the language(s) and read-\ning level\ \ appropriate for the audience. Notices and explanations may need to be available\ \ in multiple forms, \n(e.g., on paper, on a physical sign, or online), in order\ \ to meet these expectations and to be accessible to the \nAmerican public. \n\ Provide explanations as to how and why a decision was made or an action was taken\ \ by an \nautomated system \nTailored to the purpose. Explanations should be tailored\ \ to the specific purpose for which the user is \nexpected to use the explanation,\ \ and should clearly state that purpose. An informational explanation might \n\ differ from an explanation provided to allow for the possibility of recourse,\ \ an appeal, or one provided in the \ncontext of a dispute or contestation process.\ \ For the purposes of this framework, 'explanation' should be \nconstrued broadly.\ \ An explanation need not be a plain-language statement about causality but could\ \ consist of \nany mechanism that allows the recipient to build the necessary\ \ understanding and intuitions to achieve the \nstated purpose. Tailoring should\ \ be assessed (e.g., via user experience research). \nTailored to the target of\ \ the explanation. Explanations should be targeted to specific audiences and \n\ clearly state that audience. An explanation provided to the subject of a decision\ \ might differ from one provided \nto an advocate, or to a domain expert or decision\ \ maker. Tailoring should be assessed (e.g., via user experience \nresearch).\ \ \n43" - "APPENDIX\nSummaries of Additional Engagements: \n• OSTP created an email address\ \ (ai-equity@ostp.eop.gov) to solicit comments from the public on the use of\n\ artificial intelligence and other data-driven technologies in their lives.\n•\ \ OSTP issued a Request For Information (RFI) on the use and governance of biometric\ \ technologies.113 The\npurpose of this RFI was to understand the extent and variety\ \ of biometric technologies in past, current, or\nplanned use; the domains in\ \ which these technologies are being used; the entities making use of them; current\n\ principles, practices, or policies governing their use; and the stakeholders that\ \ are, or may be, impacted by their\nuse or regulation. The 130 responses to this\ \ RFI are available in full online114 and were submitted by the below\nlisted\ \ organizations and individuals:\nAccenture \nAccess Now \nACT | The App Association\ \ \nAHIP \nAIethicist.org \nAirlines for America \nAlliance for Automotive Innovation\ \ \nAmelia Winger-Bearskin \nAmerican Civil Liberties Union \nAmerican Civil Liberties\ \ Union of \nMassachusetts \nAmerican Medical Association \nARTICLE19 \nAttorneys\ \ General of the District of \nColumbia, Illinois, Maryland, \nMichigan, Minnesota,\ \ New York, \nNorth Carolina, Oregon, Vermont, \nand Washington \nAvanade \nAware\ \ \nBarbara Evans \nBetter Identity Coalition \nBipartisan Policy Center \nBrandon\ \ L. Garrett and Cynthia \nRudin \nBrian Krupp \nBrooklyn Defender Services \n\ BSA | The Software Alliance \nCarnegie Mellon University \nCenter for Democracy\ \ & \nTechnology \nCenter for New Democratic \nProcesses \nCenter for Research\ \ and Education \non Accessible Technology and \nExperiences at University of\ \ \nWashington, Devva Kasnitz, L Jean \nCamp, Jonathan Lazar, Harry \nHochheiser\ \ \nCenter on Privacy & Technology at \nGeorgetown Law \nCisco Systems \nCity\ \ of Portland Smart City PDX \nProgram \nCLEAR \nClearview AI \nCognoa \nColor\ \ of Change \nCommon Sense Media \nComputing Community Consortium \nat Computing\ \ Research Association \nConnected Health Initiative \nConsumer Technology Association\ \ \nCourtney Radsch \nCoworker \nCyber Farm Labs \nData & Society Research Institute\ \ \nData for Black Lives \nData to Actionable Knowledge Lab \nat Harvard University\ \ \nDeloitte \nDev Technology Group \nDigital Therapeutics Alliance \nDigital\ \ Welfare State & Human \nRights Project and Center for \nHuman Rights and Global\ \ Justice at \nNew York University School of \nLaw, and Temple University \nInstitute\ \ for Law, Innovation & \nTechnology \nDignari \nDouglas Goddard \nEdgar Dworsky\ \ \nElectronic Frontier Foundation \nElectronic Privacy Information \nCenter,\ \ Center for Digital \nDemocracy, and Consumer \nFederation of America \nFaceTec\ \ \nFight for the Future \nGanesh Mani \nGeorgia Tech Research Institute \nGoogle\ \ \nHealth Information Technology \nResearch and Development \nInteragency Working\ \ Group \nHireVue \nHR Policy Association \nID.me \nIdentity and Data Sciences\ \ \nLaboratory at Science Applications \nInternational Corporation \nInformation\ \ Technology and \nInnovation Foundation \nInformation Technology Industry \n\ Council \nInnocence Project \nInstitute for Human-Centered \nArtificial Intelligence\ \ at Stanford \nUniversity \nIntegrated Justice Information \nSystems Institute\ \ \nInternational Association of Chiefs \nof Police \nInternational Biometrics\ \ + Identity \nAssociation \nInternational Business Machines \nCorporation \n\ International Committee of the Red \nCross \nInventionphysics \niProov \nJacob\ \ Boudreau \nJennifer K. Wagner, Dan Berger, \nMargaret Hu, and Sara Katsanis\ \ \nJonathan Barry-Blocker \nJoseph Turow \nJoy Buolamwini \nJoy Mack \nKaren\ \ Bureau \nLamont Gholston \nLawyers’ Committee for Civil \nRights Under Law \n\ 60" - "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 \nbe built into the system design so that the\ \ system’s full behavior can be explained in advance (i.e., only fully \ntransparent\ \ models should be used), rather than as an after-the-decision interpretation.\ \ In other settings, the \nextent 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, \nand level of risk. While approximation and simplification may be necessary\ \ for the system to succeed based on \nthe explanatory purpose and target of the\ \ explanation, or to account for the risk of fraud or other concerns \nrelated\ \ to revealing decision-making information, such simplifications should be done\ \ in a scientifically \nsupportable way. Where appropriate based on the explanatory\ \ system, error ranges for the explanation should \nbe calculated and included\ \ in the explanation, with the choice of presentation of such information balanced\ \ \nwith usability and overall interface complexity concerns. \nDemonstrate protections\ \ for notice and explanation \nReporting. 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,\ \ \nidentified users, and impacted populations; the assessment of notice clarity\ \ and timeliness; the assessment of \nthe explanation's validity and accessibility;\ \ the assessment of the level of risk; and the account and assessment \nof how\ \ explanations are tailored, including to the purpose, the recipient of the explanation,\ \ and the level of \nrisk. Individualized profile information should be made readily\ \ available to the greatest extent possible that \nincludes explanations for any\ \ system impacts or inferences. Reporting should be provided in a clear plain\ \ \nlanguage and machine-readable manner. \n44" - source_sentence: How did the lack of explanation in the benefits awarding system contribute to the denial of benefits for individuals? sentences: - "APPENDIX\nSummaries of Additional Engagements: \n• OSTP created an email address\ \ (ai-equity@ostp.eop.gov) to solicit comments from the public on the use of\n\ artificial intelligence and other data-driven technologies in their lives.\n•\ \ OSTP issued a Request For Information (RFI) on the use and governance of biometric\ \ technologies.113 The\npurpose of this RFI was to understand the extent and variety\ \ of biometric technologies in past, current, or\nplanned use; the domains in\ \ which these technologies are being used; the entities making use of them; current\n\ principles, practices, or policies governing their use; and the stakeholders that\ \ are, or may be, impacted by their\nuse or regulation. The 130 responses to this\ \ RFI are available in full online114 and were submitted by the below\nlisted\ \ organizations and individuals:\nAccenture \nAccess Now \nACT | The App Association\ \ \nAHIP \nAIethicist.org \nAirlines for America \nAlliance for Automotive Innovation\ \ \nAmelia Winger-Bearskin \nAmerican Civil Liberties Union \nAmerican Civil Liberties\ \ Union of \nMassachusetts \nAmerican Medical Association \nARTICLE19 \nAttorneys\ \ General of the District of \nColumbia, Illinois, Maryland, \nMichigan, Minnesota,\ \ New York, \nNorth Carolina, Oregon, Vermont, \nand Washington \nAvanade \nAware\ \ \nBarbara Evans \nBetter Identity Coalition \nBipartisan Policy Center \nBrandon\ \ L. Garrett and Cynthia \nRudin \nBrian Krupp \nBrooklyn Defender Services \n\ BSA | The Software Alliance \nCarnegie Mellon University \nCenter for Democracy\ \ & \nTechnology \nCenter for New Democratic \nProcesses \nCenter for Research\ \ and Education \non Accessible Technology and \nExperiences at University of\ \ \nWashington, Devva Kasnitz, L Jean \nCamp, Jonathan Lazar, Harry \nHochheiser\ \ \nCenter on Privacy & Technology at \nGeorgetown Law \nCisco Systems \nCity\ \ of Portland Smart City PDX \nProgram \nCLEAR \nClearview AI \nCognoa \nColor\ \ of Change \nCommon Sense Media \nComputing Community Consortium \nat Computing\ \ Research Association \nConnected Health Initiative \nConsumer Technology Association\ \ \nCourtney Radsch \nCoworker \nCyber Farm Labs \nData & Society Research Institute\ \ \nData for Black Lives \nData to Actionable Knowledge Lab \nat Harvard University\ \ \nDeloitte \nDev Technology Group \nDigital Therapeutics Alliance \nDigital\ \ Welfare State & Human \nRights Project and Center for \nHuman Rights and Global\ \ Justice at \nNew York University School of \nLaw, and Temple University \nInstitute\ \ for Law, Innovation & \nTechnology \nDignari \nDouglas Goddard \nEdgar Dworsky\ \ \nElectronic Frontier Foundation \nElectronic Privacy Information \nCenter,\ \ Center for Digital \nDemocracy, and Consumer \nFederation of America \nFaceTec\ \ \nFight for the Future \nGanesh Mani \nGeorgia Tech Research Institute \nGoogle\ \ \nHealth Information Technology \nResearch and Development \nInteragency Working\ \ Group \nHireVue \nHR Policy Association \nID.me \nIdentity and Data Sciences\ \ \nLaboratory at Science Applications \nInternational Corporation \nInformation\ \ Technology and \nInnovation Foundation \nInformation Technology Industry \n\ Council \nInnocence Project \nInstitute for Human-Centered \nArtificial Intelligence\ \ at Stanford \nUniversity \nIntegrated Justice Information \nSystems Institute\ \ \nInternational Association of Chiefs \nof Police \nInternational Biometrics\ \ + Identity \nAssociation \nInternational Business Machines \nCorporation \n\ International Committee of the Red \nCross \nInventionphysics \niProov \nJacob\ \ Boudreau \nJennifer K. Wagner, Dan Berger, \nMargaret Hu, and Sara Katsanis\ \ \nJonathan Barry-Blocker \nJoseph Turow \nJoy Buolamwini \nJoy Mack \nKaren\ \ Bureau \nLamont Gholston \nLawyers’ Committee for Civil \nRights Under Law \n\ 60" - "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" - "NOTICE & \nEXPLANATION \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•\nA predictive policing system\ \ claimed to identify individuals at greatest risk to commit or become the victim\ \ of\ngun violence (based on automated analysis of social ties to gang members,\ \ criminal histories, previous experi­\nences of gun violence, and other factors)\ \ and led to individuals being placed on a watch list with no\nexplanation or\ \ public transparency regarding how the system came to its conclusions.85 Both\ \ police and\nthe public deserve to understand why and how such a system is making\ \ these determinations.\n•\nA system awarding benefits changed its criteria invisibly.\ \ Individuals were denied benefits due to data entry\nerrors and other system\ \ flaws. These flaws were only revealed when an explanation of the system\nwas\ \ demanded and produced.86 The lack of an explanation made it harder for errors\ \ to be corrected in a\ntimely manner.\n42" - source_sentence: What is the title of the publication related to Artificial Intelligence Risk Management by NIST? sentences: - "8 \nTrustworthy AI Characteristics: Accountable and Transparent, Privacy Enhanced,\ \ Safe, Secure and \nResilient \n2.5. Environmental Impacts \nTraining, maintaining,\ \ and operating (running inference on) GAI systems are resource-intensive activities,\ \ \nwith potentially large energy and environmental footprints. Energy and carbon\ \ emissions vary based on \nwhat is being done with the GAI model (i.e., pre-training,\ \ fine-tuning, inference), the modality of the \ncontent, hardware used, and type\ \ of task or application. \nCurrent estimates suggest that training a single transformer\ \ LLM can emit as much carbon as 300 round-\ntrip flights between San Francisco\ \ and New York. In a study comparing energy consumption and carbon \nemissions\ \ for LLM inference, generative tasks (e.g., text summarization) were found to\ \ be more energy- \nand carbon-intensive than discriminative or non-generative\ \ tasks (e.g., text classification). \nMethods for creating smaller versions of\ \ trained models, such as model distillation or compression, \ncould reduce environmental\ \ impacts at inference time, but training and tuning such models may still \n\ contribute to their environmental impacts. Currently there is no agreed upon method\ \ to estimate \nenvironmental impacts from GAI. \nTrustworthy AI Characteristics:\ \ Accountable and Transparent, Safe \n2.6. Harmful Bias and Homogenization \n\ Bias exists in many forms and can become ingrained in automated systems. AI systems,\ \ including GAI \nsystems, can increase the speed and scale at which harmful biases\ \ manifest and are acted upon, \npotentially perpetuating and amplifying harms\ \ to individuals, groups, communities, organizations, and \nsociety. For example,\ \ when prompted to generate images of CEOs, doctors, lawyers, and judges, current\ \ \ntext-to-image models underrepresent women and/or racial minorities, and people\ \ with disabilities. \nImage generator models have also produced biased or stereotyped\ \ output for various demographic \ngroups and have difficulty producing non-stereotyped\ \ content even when the prompt specifically \nrequests image features that are\ \ inconsistent with the stereotypes. Harmful bias in GAI models, which \nmay stem\ \ from their training data, can also cause representational harms or perpetuate\ \ or exacerbate \nbias based on race, gender, disability, or other protected classes.\ \ \nHarmful bias in GAI systems can also lead to harms via disparities between\ \ how a model performs for \ndifferent subgroups or languages (e.g., an LLM may\ \ perform less well for non-English languages or \ncertain dialects). Such disparities\ \ can contribute to discriminatory decision-making or amplification of \nexisting\ \ societal biases. In addition, GAI systems may be inappropriately trusted to\ \ perform similarly \nacross all subgroups, which could leave the groups facing\ \ underperformance with worse outcomes than \nif no GAI system were used. Disparate\ \ or reduced performance for lower-resource languages also \npresents challenges\ \ to model adoption, inclusion, and accessibility, and may make preservation of\ \ \nendangered languages more difficult if GAI systems become embedded in everyday\ \ processes that would \notherwise have been opportunities to use these languages.\ \ \nBias is mutually reinforcing with the problem of undesired homogenization,\ \ in which GAI systems \nproduce skewed distributions of outputs that are overly\ \ uniform (for example, repetitive aesthetic styles" - "HUMAN 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. \nAn automated system\ \ should provide demonstrably effective mechanisms to opt out in favor of a human\ \ alterna­\ntive, where appropriate, as well as timely human consideration and\ \ remedy by a fallback system, with additional \nhuman oversight and safeguards\ \ for systems used in sensitive domains, and with training and assessment for\ \ any \nhuman-based portions of the system to ensure effectiveness. \nProvide\ \ a mechanism to conveniently opt out from automated systems in favor of a human\ \ \nalternative, where appropriate \nBrief, clear, accessible notice and instructions.\ \ Those impacted by an automated system should be \ngiven a brief, clear notice\ \ that they are entitled to opt-out, along with clear instructions for how to\ \ opt-out. \nInstructions should be provided in an accessible form and should\ \ be easily findable by those impacted by the \nautomated system. The brevity,\ \ clarity, and accessibility of the notice and instructions should be assessed\ \ (e.g., \nvia user experience research). \nHuman alternatives provided when appropriate.\ \ In many scenarios, there is a reasonable expectation \nof human involvement\ \ in attaining rights, opportunities, or access. When automated systems make up\ \ part of \nthe attainment process, alternative timely human-driven processes\ \ should be provided. The use of a human \nalternative should be triggered by\ \ an opt-out process. \nTimely and not burdensome human alternative. Opting out\ \ should be timely and not unreasonably \nburdensome in both the process of requesting\ \ to opt-out and the human-driven alternative provided. \nProvide timely human\ \ consideration and remedy by a fallback and escalation system in the \nevent\ \ that an automated system fails, produces error, or you would like to appeal\ \ or con­\ntest its impacts on you \nProportionate. The availability of human\ \ consideration and fallback, along with associated training and \nsafeguards\ \ against human bias, should be proportionate to the potential of the automated\ \ system to meaning­\nfully impact rights, opportunities, or access. Automated\ \ systems that have greater control over outcomes, \nprovide input to high-stakes\ \ decisions, relate to sensitive domains, or otherwise have a greater potential\ \ to \nmeaningfully impact rights, opportunities, or access should have greater\ \ availability (e.g., staffing) and over­\nsight of human consideration and fallback\ \ mechanisms. \nAccessible. Mechanisms for human consideration and fallback, whether\ \ in-person, on paper, by phone, or \notherwise provided, should be easy to find\ \ and use. These mechanisms should be tested to ensure that users \nwho have trouble\ \ with the automated system are able to use human consideration and fallback,\ \ with the under­\nstanding that it may be these users who are most likely to\ \ need the human assistance. Similarly, it should be \ntested to ensure that users\ \ with disabilities are able to find and use human consideration and fallback\ \ and also \nrequest reasonable accommodations or modifications. \nConvenient.\ \ Mechanisms for human consideration and fallback should not be unreasonably burdensome\ \ as \ncompared to the automated system’s equivalent. \n49" - "NIST Trustworthy and Responsible AI \nNIST AI 600-1 \nArtificial Intelligence\ \ Risk Management \nFramework: Generative Artificial \nIntelligence Profile \n\ \ \n \n \nThis publication is available free of charge from: \nhttps://doi.org/10.6028/NIST.AI.600-1" --- # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 384 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': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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("danicafisher/dfisher-sentence-transformer-fine-tuned2") # Run inference sentences = [ 'What is the title of the publication related to Artificial Intelligence Risk Management by NIST?', 'NIST Trustworthy and Responsible AI \nNIST AI 600-1 \nArtificial Intelligence Risk Management \nFramework: Generative Artificial \nIntelligence Profile \n \n \n \nThis publication is available free of charge from: \nhttps://doi.org/10.6028/NIST.AI.600-1', 'HUMAN 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. \nAn automated system should provide demonstrably effective mechanisms to opt out in favor of a human alterna\xad\ntive, where appropriate, as well as timely human consideration and remedy by a fallback system, with additional \nhuman oversight and safeguards for systems used in sensitive domains, and with training and assessment for any \nhuman-based portions of the system to ensure effectiveness. \nProvide a mechanism to conveniently opt out from automated systems in favor of a human \nalternative, where appropriate \nBrief, clear, accessible notice and instructions. Those impacted by an automated system should be \ngiven a brief, clear notice that they are entitled to opt-out, along with clear instructions for how to opt-out. \nInstructions should be provided in an accessible form and should be easily findable by those impacted by the \nautomated system. The brevity, clarity, and accessibility of the notice and instructions should be assessed (e.g., \nvia user experience research). \nHuman alternatives provided when appropriate. In many scenarios, there is a reasonable expectation \nof human involvement in attaining rights, opportunities, or access. When automated systems make up part of \nthe attainment process, alternative timely human-driven processes should be provided. The use of a human \nalternative should be triggered by an opt-out process. \nTimely and not burdensome human alternative. Opting out should be timely and not unreasonably \nburdensome in both the process of requesting to opt-out and the human-driven alternative provided. \nProvide timely human consideration and remedy by a fallback and escalation system in the \nevent that an automated system fails, produces error, or you would like to appeal or con\xad\ntest its impacts on you \nProportionate. The availability of human consideration and fallback, along with associated training and \nsafeguards against human bias, should be proportionate to the potential of the automated system to meaning\xad\nfully impact rights, opportunities, or access. Automated systems that have greater control over outcomes, \nprovide input to high-stakes decisions, relate to sensitive domains, or otherwise have a greater potential to \nmeaningfully impact rights, opportunities, or access should have greater availability (e.g., staffing) and over\xad\nsight of human consideration and fallback mechanisms. \nAccessible. Mechanisms for human consideration and fallback, whether in-person, on paper, by phone, or \notherwise provided, should be easy to find and use. These mechanisms should be tested to ensure that users \nwho have trouble with the automated system are able to use human consideration and fallback, with the under\xad\nstanding that it may be these users who are most likely to need the human assistance. Similarly, it should be \ntested to ensure that users with disabilities are able to find and use human consideration and fallback and also \nrequest reasonable accommodations or modifications. \nConvenient. Mechanisms for human consideration and fallback should not be unreasonably burdensome as \ncompared to the automated system’s equivalent. \n49', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 180 training samples * Columns: sentence_0 and sentence_1 * Approximate statistics based on the first 180 samples: | | sentence_0 | sentence_1 | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence_0 | sentence_1 | |:---------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What concerns have been raised regarding the use of facial recognition technology in public housing? | 65. See, e.g., Scott Ikeda. Major Data Broker Exposes 235 Million Social Media Profiles in Data Lead: Info
Appears to Have Been Scraped Without Permission. CPO Magazine. Aug. 28, 2020. https://
www.cpomagazine.com/cyber-security/major-data-broker-exposes-235-million-social-media-profiles­
in-data-leak/; Lily Hay Newman. 1.2 Billion Records Found Exposed Online in a Single Server. WIRED,
Nov. 22, 2019. https://www.wired.com/story/billion-records-exposed-online/
66. Lola Fadulu. Facial Recognition Technology in Public Housing Prompts Backlash. New York Times.
Sept. 24, 2019.
https://www.nytimes.com/2019/09/24/us/politics/facial-recognition-technology-housing.html
67. Jo Constantz. ‘They Were Spying On Us’: Amazon, Walmart, Use Surveillance Technology to Bust
Unions. Newsweek. Dec. 13, 2021.
https://www.newsweek.com/they-were-spying-us-amazon-walmart-use-surveillance-technology-bust­
unions-1658603
68. See, e.g., enforcement actions by the FTC against the photo storage app Everalbaum
(https://www.ftc.gov/legal-library/browse/cases-proceedings/192-3172-everalbum-inc-matter), and
against Weight Watchers and their subsidiary Kurbo
(https://www.ftc.gov/legal-library/browse/cases-proceedings/1923228-weight-watchersww)
69. See, e.g., HIPAA, Pub. L 104-191 (1996); Fair Debt Collection Practices Act (FDCPA), Pub. L. 95-109
(1977); Family Educational Rights and Privacy Act (FERPA) (20 U.S.C. § 1232g), Children's Online
Privacy Protection Act of 1998, 15 U.S.C. 6501–6505, and Confidential Information Protection and
Statistical Efficiency Act (CIPSEA) (116 Stat. 2899)
70. Marshall Allen. You Snooze, You Lose: Insurers Make The Old Adage Literally True. ProPublica. Nov.
21, 2018.
https://www.propublica.org/article/you-snooze-you-lose-insurers-make-the-old-adage-literally-true
71. Charles Duhigg. How Companies Learn Your Secrets. The New York Times. Feb. 16, 2012.
https://www.nytimes.com/2012/02/19/magazine/shopping-habits.html
72. Jack Gillum and Jeff Kao. Aggression Detectors: The Unproven, Invasive Surveillance Technology
Schools are Using to Monitor Students. ProPublica. Jun. 25, 2019.
https://features.propublica.org/aggression-detector/the-unproven-invasive-surveillance-technology­
schools-are-using-to-monitor-students/
73. Drew Harwell. Cheating-detection companies made millions during the pandemic. Now students are
fighting back. Washington Post. Nov. 12, 2020.
https://www.washingtonpost.com/technology/2020/11/12/test-monitoring-student-revolt/
74. See, e.g., Heather Morrison. Virtual Testing Puts Disabled Students at a Disadvantage. Government
Technology. May 24, 2022.
https://www.govtech.com/education/k-12/virtual-testing-puts-disabled-students-at-a-disadvantage;
Lydia X. Z. Brown, Ridhi Shetty, Matt Scherer, and Andrew Crawford. Ableism And Disability
Discrimination In New Surveillance Technologies: How new surveillance technologies in education,
policing, health care, and the workplace disproportionately harm disabled people. Center for Democracy
and Technology Report. May 24, 2022.
https://cdt.org/insights/ableism-and-disability-discrimination-in-new-surveillance-technologies-how­
new-surveillance-technologies-in-education-policing-health-care-and-the-workplace­
disproportionately-harm-disabled-people/
69 | | What are the potential consequences of automated systems making decisions without providing notice or explanations to affected individuals? | NOTICE &
EXPLANATION
WHY THIS PRINCIPLE IS IMPORTANT
This section provides a brief summary of the problems which the principle seeks to address and protect
against, including illustrative examples.
Automated systems now determine opportunities, from employment to credit, and directly shape the American
public’s experiences, from the courtroom to online classrooms, in ways that profoundly impact people’s lives. But this
expansive impact is not always visible. An applicant might not know whether a person rejected their resume or a
hiring algorithm moved them to the bottom of the list. A defendant in the courtroom might not know if a judge deny­
ing their bail is informed by an automated system that labeled them “high risk.” From correcting errors to contesting
decisions, people are often denied the knowledge they need to address the impact of automated systems on their lives.
Notice and explanations also serve an important safety and efficacy purpose, allowing experts to verify the reasonable­
ness of a recommendation before enacting it.
In order to guard against potential harms, the American public needs to know if an automated system is being used.
Clear, brief, and understandable notice is a prerequisite for achieving the other protections in this framework. Like­
wise, the public is often unable to ascertain how or why an automated system has made a decision or contributed to a
particular outcome. The decision-making processes of automated systems tend to be opaque, complex, and, therefore,
unaccountable, whether by design or by omission. These factors can make explanations both more challenging and
more important, and should not be used as a pretext to avoid explaining important decisions to the people impacted
by those choices. In the context of automated systems, clear and valid explanations should be recognized as a baseline
requirement.
Providing notice has long been a standard practice, and in many cases is a legal requirement, when, for example,
making a video recording of someone (outside of a law enforcement or national security context). In some cases, such
as credit, lenders are required to provide notice and explanation to consumers. Techniques used to automate the
process of explaining such systems are under active research and improvement and such explanations can take many
forms. Innovative companies and researchers are rising to the challenge and creating and deploying explanatory
systems that can help the public better understand decisions that impact them.
While notice and explanation requirements are already in place in some sectors or situations, the American public
deserve to know consistently and across sectors if an automated system is being used in a way that impacts their rights,
opportunities, or access. This knowledge should provide confidence in how the public is being treated, and trust in the
validity and reasonable use of automated systems.
•
A lawyer representing an older client with disabilities who had been cut off from Medicaid-funded home
health-care assistance couldn't determine why, especially since the decision went against historical access
practices. In a court hearing, the lawyer learned from a witness that the state in which the older client
lived had recently adopted a new algorithm to determine eligibility.83 The lack of a timely explanation made it
harder to understand and contest the decision.
•
A formal child welfare investigation is opened against a parent based on an algorithm and without the parent
ever being notified that data was being collected and used as part of an algorithmic child maltreatment
risk assessment.84 The lack of notice or an explanation makes it harder for those performing child
maltreatment assessments to validate the risk assessment and denies parents knowledge that could help them
contest a decision.
41 | | How has the Supreme Court's decision to overturn Roe v Wade been addressed by President Biden? | ENDNOTES
1.The 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/
2. The White House. Remarks by President Biden on the Supreme Court Decision to Overturn Roe v. Wade. Jun.
24, 2022. https://www.whitehouse.gov/briefing-room/speeches-remarks/2022/06/24/remarks-by-president­
biden-on-the-supreme-court-decision-to-overturn-roe-v-wade/
3. The White House. Join the Effort to Create A Bill of Rights for an Automated Society. Nov. 10, 2021. https://
www.whitehouse.gov/ostp/news-updates/2021/11/10/join-the-effort-to-create-a-bill-of-rights-for-an­
automated-society/
4. U.S. Dept. of Health, Educ. & Welfare, Report of the Sec’y’s Advisory Comm. on Automated Pers. Data Sys.,
Records, Computers, and the Rights of Citizens (July 1973). https://www.justice.gov/opcl/docs/rec-com­
rights.pdf.
5. See, e.g., Office of Mgmt. & Budget, Exec. Office of the President, Circular A-130, Managing Information as a
Strategic Resource, app. II § 3 (July 28, 2016); Org. of Econ. Co-Operation & Dev., Revision of the
Recommendation of the Council Concerning Guidelines Governing the Protection of Privacy and Transborder
Flows of Personal Data, Annex Part Two (June 20, 2013). https://one.oecd.org/document/C(2013)79/en/pdf.
6. Andrew Wong et al. External validation of a widely implemented proprietary sepsis prediction model in
hospitalized patients. JAMA Intern Med. 2021; 181(8):1065-1070. doi:10.1001/jamainternmed.2021.2626
7. Jessica Guynn. Facebook while black: Users call it getting 'Zucked,' say talking about racism is censored as hate
speech. USA Today. Apr. 24, 2019. https://www.usatoday.com/story/news/2019/04/24/facebook-while-black­
zucked-users-say-they-get-blocked-racism-discussion/2859593002/
8. See, e.g., Michael Levitt. AirTags are being used to track people and cars. Here's what is being done about it.
NPR. Feb. 18, 2022. https://www.npr.org/2022/02/18/1080944193/apple-airtags-theft-stalking-privacy-tech;
Samantha Cole. Police Records Show Women Are Being Stalked With Apple AirTags Across the Country.
Motherboard. Apr. 6, 2022. https://www.vice.com/en/article/y3vj3y/apple-airtags-police-reports-stalking­
harassment
9. Kristian Lum and William Isaac. To Predict and Serve? Significance. Vol. 13, No. 5, p. 14-19. Oct. 7, 2016.
https://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.1740-9713.2016.00960.x; Aaron Sankin, Dhruv Mehrotra,
Surya Mattu, and Annie Gilbertson. Crime Prediction Software Promised to Be Free of Biases. New Data Shows
It Perpetuates Them. The Markup and Gizmodo. Dec. 2, 2021. https://themarkup.org/prediction­
bias/2021/12/02/crime-prediction-software-promised-to-be-free-of-biases-new-data-shows-it-perpetuates­
them
10. Samantha Cole. This Horrifying App Undresses a Photo of Any Woman With a Single Click. Motherboard.
June 26, 2019. https://www.vice.com/en/article/kzm59x/deepnude-app-creates-fake-nudes-of-any-woman
11. Lauren Kaori Gurley. Amazon’s AI Cameras Are Punishing Drivers for Mistakes They Didn’t Make.
Motherboard. Sep. 20, 2021. https://www.vice.com/en/article/88npjv/amazons-ai-cameras-are-punishing­
drivers-for-mistakes-they-didnt-make
63 | * 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 - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `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`: 3 - `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
### Framework Versions - Python: 3.11.9 - 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", } ``` #### 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} } ```