Add new SentenceTransformer model.

197d3cd verified about 1 month ago

93.5 kB

	---
	base_model: Alibaba-NLP/gte-large-en-v1.5
	library_name: sentence-transformers
	metrics:
	- cosine_accuracy@1
	- cosine_accuracy@3
	- cosine_accuracy@5
	- cosine_accuracy@10
	- cosine_precision@1
	- cosine_precision@3
	- cosine_precision@5
	- cosine_precision@10
	- cosine_recall@1
	- cosine_recall@3
	- cosine_recall@5
	- cosine_recall@10
	- cosine_ndcg@10
	- cosine_mrr@10
	- cosine_map@100
	- dot_accuracy@1
	- dot_accuracy@3
	- dot_accuracy@5
	- dot_accuracy@10
	- dot_precision@1
	- dot_precision@3
	- dot_precision@5
	- dot_precision@10
	- dot_recall@1
	- dot_recall@3
	- dot_recall@5
	- dot_recall@10
	- dot_ndcg@10
	- dot_mrr@10
	- dot_map@100
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:586
	- loss:MultipleNegativesRankingLoss
	widget:
	- source_sentence: Explain the spectrum of openness in AI systems as described in
	the document. How do open-source AI systems differ from fully closed AI systems
	in terms of accessibility and innovation?
	sentences:
	- 'targets of cyber attacks; or

	(iii) permitting the evasion of human control or oversight through

	means of deception or obfuscation.

	Models meet this deﬁnition even if they are provided to end users with

	technical safeguards that attempt to prevent users from taking advantage of

	the relevant unsafe capabilities.

	(l) The term “Federal law enforcement agency” has the meaning set forth

	in section 21(a) of Executive Order 14074 of May 25, 2022 (Advancing

	Eﬀective, Accountable Policing and Criminal Justice Practices To Enhance

	Public Trust and Public Safety).

	(m) The term “ﬂoating-point operation” means any mathematical

	operation or assignment involving ﬂoating-point numbers, which are a

	subset of the real numbers typically represented on computers by an integer

	of ﬁxed precision scaled by an integer exponent of a ﬁxed base.

	(n) The term “foreign person” has the meaning set forth in section 5(c)
	of

	Executive Order 13984 of January 19, 2021 (Taking Additional Steps To

	Address the National Emergency With Respect to Signiﬁcant Malicious

	Cyber-Enabled Activities).

	(o) The terms “foreign reseller” and “foreign reseller of United States

	Infrastructure as a Service Products” mean a foreign person who has

	established an Infrastructure as a Service Account to provide Infrastructure

	as a Service Products subsequently, in whole or in part, to a third party.

	(p) The term “generative AI” means the class of AI models that emulate

	the structure and characteristics of input data in order to generate derived

	synthetic content. This can include images, videos, audio, text, and other

	digital content.

	(q) The terms “Infrastructure as a Service Product,” “United States

	Infrastructure as a Service Product,” “United States Infrastructure as a

	Service Provider,” and “Infrastructure as a Service Account” each have the

	respective meanings given to those terms in section 5 of Executive Order

	13984.

	(r) The term “integer operation” means any mathematical operation or

	assignment involving only integers, or whole numbers expressed without a

	decimal point.05/10/2024, 16:36 Executive Order on the Safe, Secure, and Trustworthy
	Development and Use of Artiﬁcial Intelligence \| The White House

	https://www.whitehouse.gov/brieﬁng-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artiﬁc…
	7/59'
	- "AI safety, enable next-generation medical diagnoses and further other\ncritical\
	\ AI priorities.\n\0\0 Released a for designing safe, secure, and trustworthy\
	\ AI tools\nfor use in education. The Department of Education’s guide discusses\n\
	how developers of educational technologies can design AI that beneﬁts\nstudents\
	\ and teachers while advancing equity, civil rights, trust, and\ntransparency.\
	\ This work builds on the Department’s 2023 \noutlining recommendations for the\
	\ use of AI in teaching and learning.\n\0\0 Published guidance on evaluating the\
	\ eligibility of patent claims\ninvolving inventions related to AI technology, as\
	\ well as other\nemerging technologies. The guidance by the U.S. Patent and Trademark\n\
	Oﬃce will guide those inventing in the AI space to protect their AI\ninventions\
	\ and assist patent examiners reviewing applications for\npatents on AI inventions.\n\
	\0\0 Issued a on federal research and development (R&D) to\nadvance trustworthy\
	\ AI over the past four years. The report by the\nNational Science and Technology\
	\ Council examines an annual federal AI\nR&D budget of nearly $3 billion.\n\0\0\
	\ Launched a $23 million initiative to promote the use of privacy-\nenhancing\
	\ technologies to solve real-world problems, including\nrelated to AI. Working\
	\ with industry and agency partners, NSF will\ninvest through its new Privacy-preserving\
	\ Data Sharing in Practice\nprogram in eﬀorts to apply, mature, and scale privacy-enhancing\n\
	technologies for speciﬁc use cases and establish testbeds to accelerate\ntheir\
	\ adoption.\n\0\0 Announced millions of dollars in further investments to advance\n\
	responsible AI development and use throughout our society. These\ninclude $30\
	\ million invested through NSF’s Experiential Learning in\nEmerging and Novel\
	\ Technologies program—which supports inclusive\nexperiential learning in ﬁelds\
	\ like AI—and $10 million through NSF’s\nExpandAI program, which helps build capacity\
	\ in AI research at\nminority-serving institutions while fostering the development\
	\ of a\ndiverse, AI-ready workforce.\nAdvancing U.S. Leadership Abroad\nPresident\
	\ Biden’s Executive Order emphasized that the United States lead\nglobal eﬀorts\
	\ to unlock AI’s potential and meet its challenges. To advance\nU.S. leadership\
	\ on AI, agencies have:guide\nreport\nreport05/10/2024, 16:35 FACT SHEET: Biden-Harris\
	\ Administration Announces New AI Actions and Receives Additional Major Voluntary\
	\ Commitment on AI \| The…\nhttps://www.whitehouse.gov/brieﬁng-room/statements-releases/2024/07/26/fact-sheet-biden-harris-administration-announces-new-ai-actions-and-receives-addit…\
	\ 4/10"
	- "50 Governing AI for Humanity processes such as the recent scientific report\
	\ \non the risks of advanced AI commissioned by \nthe United Kingdom,25 and relevant\
	\ regional \norganizations.\ne. A steering committee would develop a research\
	\ \nagenda ensuring the inclusivity of views and \nincorporation of ethical considerations,\
	\ oversee \nthe allocation of resources, foster collaboration \nwith a network\
	\ of academic institutions and \nother stakeholders, and review the panel’s \n\
	activities and deliverables.100 By drawing on the unique convening power of the\
	\ \nUnited Nations and inclusive global reach across \nstakeholder groups, an\
	\ international scientific panel \ncan deliver trusted scientific collaboration\
	\ processes \nand outputs and correct information asymmetries \nin ways that address\
	\ the representation and \ncoordination gaps identified in paragraphs 66 and \n\
	73, thereby promoting equitable and effective \ninternational AI governance.\n\
	Among the topics discussed in our consultations was the ongoing debate over open\
	\ versus closed AI systems. \nAI systems that are open in varying degrees are\
	\ often referred to as “open-source AI”, but this is somewhat of a \nmisnomer\
	\ when compared with open-source software (code). It is important to recognize\
	\ that openness in AI \nsystems is more of a spectrum than a single attribute.\n\
	One article explained that a “fully closed AI system is only accessible to a particular\
	\ group. It could be an AI \ndeveloper company or a specific group within it,\
	\ mainly for internal research and development purposes. On the \nother hand,\
	\ more open systems may allow public access or make available certain parts, such\
	\ as data, code, or \nmodel characteristics, to facilitate external AI development.”a\n\
	Open-source AI systems in the generative AI field present both risks and opportunities.\
	\ Companies often cite “AI \nsafety” as a reason for not disclosing system specifications,\
	\ reflecting the ongoing tension between open and \nclosed approaches in the industry.\
	\ Debates typically revolve around two extremes: full openness, which entails\
	\ \nsharing all model components and data sets; and partial openness, which involves\
	\ disclosing only model weights. \nOpen-source AI systems encourage innovation\
	\ and are often a requirement for public funding. On the open \nextreme of the\
	\ spectrum, when the underlying code is made freely available, developers around\
	\ the world can \nexperiment, improve and create new applications. This fosters\
	\ a collaborative environment where ideas and \nexpertise are readily shared.\
	\ Some industry leaders argue that this openness is vital to innovation and economic\
	\ \ngrowth.\nHowever, in most cases, open-source AI models are available as application\
	\ programming interfaces. In this case, \nthe original code is not shared, the\
	\ original weights are never changed and model updates become new models. \nAdditionally,\
	\ open-source models tend to be smaller and more transparent. This transparency\
	\ can build trust, \nallow for ethical considerations to be proactively addressed,\
	\ and support validation and replication because users \ncan examine the inner\
	\ workings of the AI system, understand its decision-making process and identify\
	\ potential \nbiases.Box 9: Open versus closed AI systems\na Angela Luna, “The\
	\ open or closed AI dilemma”, 2 May 2024. Available at https://bipartisanpolicy.org/blog/the-open-or-closed-ai-dilemma\
	\ .\n25 International Scientific Report on the Safety of Advanced AI: Interim\
	\ Report. Available at https://gov.uk/government/publications/international-scientific-report-\n\
	on-the-safety-of-advanced-ai ."
	- source_sentence: What role does the report propose for the United Nations in establishing
	a governance regime for AI, and how does it envision this regime contributing
	to a new social contract that protects vulnerable populations?
	sentences:
	- "HUMAN ALTERNATIVES, \nCONSIDERATION, AND \nFALLBACK \nHOW 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. \nHealthcare “navigators” help\
	\ people find their way through online signup forms to choose \nand obtain healthcare.\
	\ A Navigator is “an individual or organization that's trained and able to help\
	\ \nconsumers, small businesses, and their employees as they look for health coverage\
	\ options through the \nMarketplace (a government web site), including completing\
	\ eligibility and enrollment forms.”106 For \nthe 2022 plan year, the Biden-Harris\
	\ Administration increased funding so that grantee organizations could \n“train\
	\ and certify more than 1,500 Navigators to help uninsured consumers find affordable\
	\ and comprehensive \nhealth coverage. ”107\nThe customer service industry has\
	\ successfully integrated automated services such as \nchat-bots and AI-driven\
	\ call response systems with escalation to a human support team.\n108 Many businesses\
	\ now use partially automated customer service platforms that help answer customer\
	\ \nquestions and compile common problems for human agents to review. These integrated\
	\ human-AI \nsystems allow companies to provide faster customer care while maintaining\
	\ human agents to answer \ncalls or otherwise respond to complicated requests.\
	\ Using both AI and human agents is viewed as key to \nsuccessful customer service.109\n\
	Ballot curing laws in at least 24 states require a fallback system that allows\
	\ voters to \ncorrect their ballot and have it counted in the case that a voter\
	\ signature matching algorithm incorrectly flags their ballot as invalid or there\
	\ is another issue with their ballot, and review by an election official does\
	\ not rectify the problem. Some federal courts have found that such cure procedures\
	\ are constitutionally required.\n110 Ballot \ncuring processes vary among states,\
	\ and include direct phone calls, emails, or mail contact by election \nofficials.111\
	\ Voters are asked to provide alternative information or a new signature to verify\
	\ the validity of their \nballot. \n52"
	- "SECTION TITLE\nHUMAN ALTERNATIVES , C ONSIDERATION , AND FALLBACK\nYou should\
	\ be able to opt out, where appropriate, and have access to a person who can quickly\
	\ \nconsider and remedy problems you encounter. You should be able to opt out\
	\ from automated systems in \nfavor of a human alternative, where appropriate.\
	\ Appropriateness should be determined based on reasonable expectations in a given\
	\ context and with a focus on ensuring broad accessibility and protecting the\
	\ public from especially harmful impacts. In some cases, a human or other alternative\
	\ may be required by law. You should have access to timely human consideration\
	\ and remedy by a fallback and escalation process if an automated system fails,\
	\ it produces an error, or you would like to appeal or contest its impacts on\
	\ you. Human consideration and fallback should be accessible, equitable, effective,\
	\ maintained, accompanied by appropriate operator training, and should not impose\
	\ an unreasonable burden on the public. Automated systems with an intended use\
	\ within sensi\n-\ntive domains, including, but not limited to, criminal justice,\
	\ employment, education, and health, should additional -\nly be tailored to the\
	\ purpose, provide meaningful access for oversight, include training for any people\
	\ interacting with the system, and incorporate human consideration for adverse\
	\ or high-risk decisions. Reporting that includes a description of these human\
	\ governance processes and assessment of their timeliness, accessibility, outcomes,\
	\ and effectiveness should be made public whenever possible. \nDefinitions for\
	\ key terms in The Blueprint for an AI Bill of Rights can be found in Applying\
	\ the Blueprint for an AI Bill of Rights. \nAccompanying analysis and tools for\
	\ actualizing each principle can be found in the Technical Companion. \n7"
	- "Final Report 21E. Reflections on institutional \nmodels\nlxiv Discussions\
	\ about AI often resolve into extremes. \nIn our consultations around the world,\
	\ we engaged \nwith those who see a future of boundless goods \nprovided by ever-cheaper,\
	\ ever-more-helpful AI \nsystems. We also spoke with those wary of darker \nfutures,\
	\ of division and unemployment, and even \nextinction.8\nlxv We do not know whether\
	\ the utopian or dystopian \nfuture is more likely. Equally, we are mindful that\
	\ \nthe technology may go in a direction that does \naway with this duality. This\
	\ report focuses on \nthe near-term opportunities and risks, based on \nscience\
	\ and grounded in fact. \nlxvi The seven recommendations outlined above offer\
	\ \nour best hope for reaping the benefits of AI, while \nminimizing and mitigating\
	\ the risks, as AI continues \nevolving. We are also mindful of the practical\
	\ \nchallenges to international institution-building \non a larger scale. This\
	\ is why we are proposing a \nnetworked institutional approach, with light and\
	\ \nagile support. If or when risks become more acute \nand the stakes for opportunities\
	\ escalate, such \ncalculations may change. \nlxvii The world wars led to the\
	\ modern international \nsystem; the development of ever-more-powerful \nchemical,\
	\ biological and nuclear weapons led \nto regimes limiting their spread and promoting\
	\ \npeaceful uses of the underlying technologies. \nEvolving understanding of\
	\ our common humanity \nled to the modern human rights system and our \nongoing\
	\ commitment to the SDGs for all. Climate \nchange evolved from a niche concern\
	\ to a global \nchallenge.lxviii AI may similarly rise to a level that requires\
	\ more \nresources and more authority than is proposed \nin the above-mentioned\
	\ recommendations, \ninto harder functions of norm elaboration, \nimplementation,\
	\ monitoring, verification and \nvalidation, enforcement, accountability, remedies\
	\ \nfor harm and emergency responses. Reflecting on \nsuch institutional models,\
	\ therefore, is prudent. The \nfinal section of this report seeks to contribute\
	\ to \nthat effort.\n4. A call to action\nlxix We remain optimistic about the\
	\ future with AI and \nits positive potential. That optimism depends, \nhowever,\
	\ on realism about the risks and the \ninadequacy of structures and incentives\
	\ currently \nin place. The technology is too important, and the \nstakes are\
	\ too high, to rely only on market forces \nand a fragmented patchwork of national\
	\ and \nmultilateral action.\nlxx The United Nations can be the vehicle for a\
	\ new \nsocial contract for AI that ensures global buy-\nin for a governance regime\
	\ which protects and \nempowers us all. Such a social contract will ensure \n\
	that opportunities are fairly distributed, and the \nrisks are not loaded on to\
	\ the most vulnerable – or \npassed on to future generations, as we have seen,\
	\ \ntragically, with climate change.\nlxxi As a group and as individuals from\
	\ across many \nfields of expertise, organizations and parts of the \nworld, we\
	\ look forward to continuing this crucial \nconversation. Together with the many\
	\ others we \nhave connected with on this journey, and the global \ncommunity\
	\ they represent, we hope that this report \ncontributes to our combined efforts\
	\ to govern AI \nfor humanity.\n8 See https://safe.ai/work/statement-on-ai-risk\
	\ ."
	- source_sentence: What are the potential consequences of coordination gaps between
	various AI governance initiatives, as highlighted in the context information?
	sentences:
	- "44 Governing AI for Humanity B. Coordination gaps\n72 The ongoing emergence\
	\ and evolution of AI \ngovernance initiatives are not guaranteed to \nwork together\
	\ effectively for humanity. Instead, \ncoordination gaps have appeared. Effective\
	\ \nhandshaking between the selective plurilateral \ninitiatives (see fig. 8)\
	\ and other regional initiatives is \nnot assured, risking incompatibility between\
	\ regions.\n73 Nor are there global mechanisms for all international \nstandards\
	\ development organizations (see fig. 7), \ninternational scientific research\
	\ initiatives or AI \ncapacity-building initiatives to coordinate with each \n\
	other, undermining interoperability of approaches \nand resulting in fragmentation.\
	\ The resulting \ncoordination gaps between various sub-global \ninitiatives are\
	\ in some cases best addressed at the \nglobal level.\n74 A separate set of coordination\
	\ gaps arise within \nthe United Nations system, reflected in the array of \n\
	diverse United Nations documents and initiatives \nin relation to AI. Figure 9\
	\ shows 27 United Nations-\nrelated instruments in specific domains that may \n\
	apply to AI – 23 of them are binding and will require \ninterpretation as they\
	\ pertain to AI. A further 29 \ndomain-level documents from the United Nations\
	\ \nand related organizations focus specifically on AI, \nnone of which are binding.17\
	\ In some cases, these \ncan address AI risks and harness AI benefits in \nspecific\
	\ domains.75 The level of activity shows the importance of AI \nto United Nations\
	\ programmes. As AI expands to \naffect ever-wider aspects of society, there will\
	\ be \ngrowing calls for diverse parts of the United Nations \nsystem to act,\
	\ including through binding norms. \nIt also shows the ad hoc nature of the responses,\
	\ \nwhich have largely developed organically in specific \ndomains and without\
	\ an overarching strategy. The \nresulting coordination gaps invite overlaps and\
	\ \nhinder interoperability and impact.\n76 The number and diversity of approaches\
	\ are a sign \nthat the United Nations system is responding to \nan emerging issue.\
	\ With proper orchestration, and \nin combination with processes taking a holistic\
	\ \napproach, these efforts can offer an efficient and \nsustainable pathway to\
	\ inclusive international AI \ngovernance in specific domains. This could enable\
	\ \nmeaningful, harmonized and coordinated impacts \non areas such as health,\
	\ education, technical \nstandards and ethics, instead of merely contributing\
	\ \nto the proliferation of initiatives and institutions \nin this growing field.\
	\ International law, including \ninternational human rights law, provides a shared\
	\ \nnormative foundation for all AI-related efforts, \nthereby facilitating coordination\
	\ and coherence."
	- "\0\0 Issued a comprehensive plan for U.S. engagement on global AI\nstandards. The\
	\ plan, developed by the NIST, incorporates broad public\nand private-sector input,\
	\ identiﬁes objectives and priority areas for AI\nstandards work, and lays out\
	\ actions for U.S. stakeholders including U.S.\nagencies. NIST and others agencies\
	\ will report on priority actions in 180\ndays. \n\0\0 Developed for managing\
	\ risks to human rights posed by AI.\nThe Department of State’s “Risk Management\
	\ Proﬁle for AI and Human\nRights”—developed in close coordination with NIST and\
	\ the U.S. Agency\nfor International Development—recommends actions based on the\
	\ NIST\nAI Risk Management Framework to governments, the private sector, and\n\
	civil society worldwide, to identify and manage risks to human rights\narising\
	\ from the design, development, deployment, and use of AI. \n\0\0 Launched a global\
	\ network of AI Safety Institutes and other\ngovernment-backed scientiﬁc oﬃces\
	\ to advance AI safety at a technical\nlevel. This network will accelerate critical\
	\ information exchange and\ndrive toward common or compatible safety evaluations\
	\ and policies.\n\0\0 Launched a landmark United Nations General Assembly resolution.\n\
	The unanimously adopted resolution, with more than 100 co-sponsors,\nlays out\
	\ a common vision for countries around the world to promote the\nsafe and secure\
	\ use of AI to address global challenges.\n\0\0 Expanded global support for the\
	\ U.S.-led Political Declaration on the\nResponsible Military Use of Artiﬁcial\
	\ Intelligence and\nAutonomy. Fifty-ﬁve nations now endorse the political declaration,\n\
	which outlines a set of norms for the responsible development,\ndeployment, and\
	\ use of military AI capabilities.\nThe Table below summarizes many of the activities\
	\ that federal agencies\nhave completed in response to the Executive Order:guidance05/10/2024,\
	\ 16:35 FACT SHEET: Biden-Harris Administration Announces New AI Actions and Receives\
	\ Additional Major Voluntary Commitment on AI \| The…\nhttps://www.whitehouse.gov/brieﬁng-room/statements-releases/2024/07/26/fact-sheet-biden-harris-administration-announces-new-ai-actions-and-receives-addit…\
	\ 5/10"
	- "Final Report 55f. In addition, diverse stakeholders – in particular \ntechnology\
	\ companies and civil society \nrepresentatives – could be invited to engage \n\
	through existing institutions detailed below, as \nwell as policy workshops on\
	\ particular aspects \nof AI governance such as limits (if any) of open-\nsource\
	\ approaches to the most advanced forms \nof AI, thresholds for tracking and reporting\
	\ of \nAI incidents, application of human rights law to \nnovel use cases, or\
	\ the use of competition law/\nantitrust to address concentrations of power \n\
	among technology companies.30\ng. The proposed AI office could also curate a \n\
	repository of AI governance examples, including \nlegislation, policies and institutions\
	\ from \naround the world for consideration of the policy \ndialogue, working\
	\ with existing efforts, such as \nOECD.\n109 Notwithstanding the two General\
	\ Assembly \nresolutions on AI in 2024, there is currently \nno mandated institutionalized\
	\ dialogue on \nAI governance at the United Nations that \ncorresponds to the\
	\ reliably inclusive vision of this \nrecommendation. Similar processes do exist\
	\ at \nthe international level, but primarily in regional or \nplurilateral constellations\
	\ (para. 57), which are not \nreliably inclusive and global.\n110 Complementing\
	\ a fluid process of plurilateral and \nregional AI summits,31 the United Nations\
	\ can \noffer a stable home for dialogue on AI governance. \nInclusion by design\
	\ – a crucial requirement for \nplaying a stabilizing role in geopolitically delicate\
	\ \ntimes – can also address representation and \ncoordination gaps identified\
	\ in paragraphs 64 and \n72, promoting more effective collective action on AI\
	\ \ngovernance in the common interest of all countries. AI standards exchange\
	\ \n \nRecommendation 3: AI standards exchange \n \nWe recommend the creation\
	\ of an AI standards \nexchange, bringing together representatives from \nnational\
	\ and international standard-development \norganizations, technology companies,\
	\ civil society \nand representatives from the international scientific \npanel.\
	\ It would be tasked with:\na. Developing and maintaining a register of \ndefinitions\
	\ and applicable standards for \nmeasuring and evaluating AI systems;\nb. Debating\
	\ and evaluating the standards and the \nprocesses for creating them; and\nc.\
	\ Identifying gaps where new standards are \nneeded.\n111 When AI systems were\
	\ first explored, few standards \nexisted to help to navigate or measure this\
	\ new \nfrontier. The Turing Test – of whether a machine can \nexhibit behaviour\
	\ equivalent to (or indistinguishable \nfrom) a human being – captured the popular\
	\ \nimagination, but is of more cultural than scientific \nsignificance. Indeed,\
	\ it is telling that some of \nthe greatest computational advances have been \n\
	measured by their success in games, such as when \na computer could beat humans\
	\ at chess, Go, poker \nor Jeopardy. Such measures were easily understood \nby\
	\ non-specialists, but were neither rigorous nor \nparticularly scientific.\n\
	112 More recently, there has been a proliferation of \nstandards. Figure 13 illustrates\
	\ the increasing \nnumber of relevant standards adopted by ITU, the \nInternational\
	\ Organization for Standardization (ISO), \nthe International Electrotechnical\
	\ Commission \n(IEC) and the Institute of Electrical and Electronics \nEngineers\
	\ (IEEE).32\n30 Such a gathering could also provide an opportunity for multi-stakeholder\
	\ debate of any hardening of the global governance of AI. These might include,\
	\ for \nexample, prohibitions on the development of uncontainable or uncontrollable\
	\ AI systems, or requirements that all AI systems be sufficiently transparent\
	\ so that \ntheir consequences can be traced back to a legal actor that can assume\
	\ responsibility for them.\n31 Although multiple AI summits have helped a subset\
	\ of 20–30 countries to align on AI safety issues, participation has been inconsistent:\
	\ Brazil, China and \nIreland endorsed the Bletchley Declaration in November 2023,\
	\ but not the Seoul Ministerial Statement six months later (see fig. 12). Conversely,\
	\ Mexico and \nNew Zealand endorsed the Seoul Ministerial Statement, but did not\
	\ endorse the Bletchley Declaration.\n32 Many new standards are also emerging\
	\ at the national and multinational levels, such as the United States White House\
	\ Voluntary AI Commitments and the \nEuropean Union Codes of Practice for the\
	\ AI Act."
	- source_sentence: Describe the minimum set of criteria that should be included in
	the incident reporting process for GAI systems, according to the organizational
	practices established for identifying incidents.
	sentences:
	- "APPENDIX\nSummaries of Additional Engagements: \n•OSTP created an email address\
	\ ( [email protected] ) 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; currentprinciples,\
	\ practices, or policies governing their use; and the stakeholders that are, or\
	\ may be, impacted by theiruse or regulation. The 130 responses to this RFI are\
	\ available in full online\n114 and were submitted by the below\nlisted organizations\
	\ and individuals:\nAccenture \nAccess Now ACT \| The App Association AHIP \nAIethicist.org\
	\ \nAirlines for America Alliance for Automotive Innovation Amelia Winger-Bearskin\
	\ American Civil Liberties Union American Civil Liberties Union of Massachusetts\
	\ American Medical Association ARTICLE19 Attorneys General of the District of\
	\ Columbia, Illinois, Maryland, Michigan, Minnesota, New York, North Carolina,\
	\ Oregon, Vermont, and Washington Avanade Aware Barbara Evans Better Identity\
	\ Coalition Bipartisan Policy Center Brandon L. Garrett and Cynthia Rudin Brian\
	\ Krupp Brooklyn Defender Services BSA \| The Software Alliance Carnegie Mellon\
	\ University Center for Democracy & Technology Center for New Democratic Processes\
	\ Center for Research and Education on Accessible Technology and Experiences at\
	\ University of Washington, Devva Kasnitz, L Jean Camp, Jonathan Lazar, Harry\
	\ Hochheiser Center on Privacy & Technology at Georgetown Law Cisco Systems City\
	\ of Portland Smart City PDX Program CLEAR Clearview AI Cognoa Color of Change\
	\ Common Sense Media Computing Community Consortium at Computing Research Association\
	\ Connected Health Initiative Consumer Technology Association Courtney Radsch\
	\ Coworker Cyber Farm Labs Data & Society Research Institute Data for Black Lives\
	\ Data to Actionable Knowledge Lab at Harvard University Deloitte Dev Technology\
	\ Group Digital Therapeutics Alliance Digital Welfare State & Human Rights Project\
	\ and Center for Human Rights and Global Justice at New York University School\
	\ of Law, and Temple University Institute for Law, Innovation & Technology Dignari\
	\ Douglas Goddard Edgar Dworsky Electronic Frontier Foundation Electronic Privacy\
	\ Information Center, Center for Digital Democracy, and Consumer Federation of\
	\ America FaceTec Fight for the Future Ganesh Mani Georgia Tech Research Institute\
	\ Google Health Information Technology Research and Development Interagency Working\
	\ Group HireVue HR Policy Association ID.me Identity and Data Sciences Laboratory\
	\ at Science Applications International Corporation Information Technology and\
	\ Innovation Foundation Information Technology Industry Council Innocence Project\
	\ Institute for Human-Centered Artificial Intelligence at Stanford University\
	\ Integrated Justice Information Systems Institute International Association of\
	\ Chiefs of Police International Biometrics + Identity Association International\
	\ Business Machines Corporation International Committee of the Red Cross Inventionphysics\
	\ iProov Jacob Boudreau Jennifer K. Wagner, Dan Berger, Margaret Hu, and Sara\
	\ Katsanis Jonathan Barry-Blocker Joseph Turow Joy Buolamwini Joy Mack Karen Bureau\
	\ Lamont Gholston Lawyers’ Committee for Civil Rights Under Law \n60"
	- "19 GV-4.1-003 Establish policies, procedures, and processes for oversight functions\
	\ (e.g., senior \nleadership, legal, compliance, including internal evaluation\
	\ ) across the GAI \nlifecycle, from problem formulation and supply chains to\
	\ system decommission. Value Chain and Component \nIntegration \nAI Actor Tasks:\
	\ AI Deployment, AI Design, AI Development, Operation and Monitoring \n \nGOVERN\
	\ 4.2: Organizational teams document the risks and potential impacts of the AI\
	\ technology they design, develop, deploy, \nevaluate, and use, and they communicate\
	\ about the impacts more broadly. \nAction ID Suggested Action GAI Risks \n\
	GV-4.2-001 Establish terms of use and terms of service for GAI systems . Intellectual\
	\ Property ; Dangerous , \nViolent, or Hateful Content ; \nObscene, Degrading,\
	\ and/or \nAbusive Content \nGV-4.2-002 Include relevant AI Actors in the GAI\
	\ system risk identiﬁcation process. Human -AI Conﬁguration \nGV-4.2-0 03 Verify\
	\ that downstream GAI system impacts (such as the use of third -party \nplugins)\
	\ are included in the impact documentation process. Value Chain and Component\
	\ \nIntegration \nAI Actor Tasks: AI Deployment, AI Design, AI Development,\
	\ Operation and Monitoring \n \nGOVERN 4.3: Organizational practices are in place\
	\ to enable AI testing, identiﬁcation of incidents, and information sharing. \
	\ \nAction ID Suggested Action GAI Risks \nGV4.3-- 001 Establish policies for\
	\ measuring the eﬀectiveness of employed content \nprovenance methodologies (e.g.,\
	\ cryptography, watermarking, steganography, etc.) Information Integrity \nGV-4.3-002\
	\ Establish o rganizational practices to identify the minimum set of criteria\
	\ \nnecessary for GAI system incident reporting such as: System ID (auto -generated\
	\ \nmost likely), Title, Reporter, System/Source, Data Reported, Date of Incident,\
	\ Description, Impact(s), Stakeholder(s) Impacted. Information Security"
	- "72 Governing AI for Humanity Box 15: Possible functions and first-year deliverables\
	\ of the AI office\nThe AI office should have a light structure and aim to be\
	\ agile, trusted and networked. Where necessary, it should \noperate in a “hub\
	\ and spoke” manner to connect to other parts of the United Nations system and\
	\ beyond.\nOutreach could include serving as a key node in a so-called soft coordination\
	\ architecture between Member \nStates, plurilateral networks, civil society organizations,\
	\ academia and technology companies in a regime complex \nthat weaves together\
	\ to solve problems collaboratively through networking, and as a safe, trusted\
	\ place to \nconvene on relevant topics. Ambitiously, it could become the glue\
	\ that helps to hold such other evolving networks \ntogether.\nSupporting the\
	\ various initiatives proposed in this report includes the important function\
	\ of ensuring inclusiveness \nat speed in delivering outputs such as scientific\
	\ reports, governance dialogue and identifying appropriate follow-\nup entities.\n\
	Common understanding :\n• Facilitate recruitment of and support the international\
	\ scientific panel.\nCommon ground :\n• Service policy dialogues with multi-stakeholder\
	\ inputs in support of interoperability and policy learning. \nAn initial priority\
	\ topic is the articulation of risk thresholds and safety frameworks across jurisdictions\n\
	• Support ITU, ISO/IEC and IEEE on setting up the AI standards exchange.\nCommon\
	\ benefits :\n• Support the AI capacity development network with an initial focus\
	\ on building public interest AI capacity \namong public officials and social\
	\ entrepreneurs. Define the initial network vision, outcomes, go vernance \nstructure,\
	\ partnerships and operational mechanisms.\n• Define the vision, outcomes, governance\
	\ structure and operational mechanisms for the global fund for AI, \nand seek\
	\ feedback from Member States, industry and civil society stakeholders on the\
	\ proposal, with a \nview to funding initial projects within six months of establishment.\n\
	• Prepare and publish an annual list of prioritized investment areas to guide\
	\ both the global fund for AI and \ninvestments outside that structure.\nCoherent\
	\ effort :\n• Establish lightweight mechanisms that support Member States and\
	\ other relevant organizations to be \nmore connected, coordinated and effective\
	\ in pursuing their global AI governance efforts.\n• Prepare initial frameworks\
	\ to guide and monitor the AI office’s work, including a global governance risk\
	\ \ntaxonomy, a global AI policy landscape review and a global stakeholder map.\n\
	• Develop and implement quarterly reporting and periodic in-person presentations\
	\ to Member States on \nthe AI office’s progress against its workplan and establish\
	\ feedback channels to support adjustments as \nneeded.\n• Establish a steering\
	\ committee jointly led by the AI office, ITU, UNC TAD, UNESCO and other relevant\
	\ \nUnited Nations entities and organizations to accelerate the work of the United\
	\ Nations in service of the \nfunctions above, and review progress of the accelerated\
	\ efforts every three months.\n• Promote joint learning and development opportunities\
	\ for Member State representatives to support them \nto carry out their responsibilities\
	\ for global AI governance, in cooperation with relevant United Nations \nentities\
	\ and organizations such as the United Nations Institute for Training and Research\
	\ and the United \nNations University."
	- source_sentence: What are some of the legal frameworks mentioned in the context
	that aim to protect personal information, and how do they relate to data privacy
	concerns?
	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. \nTailored to the level of risk. An assessment should\
	\ be done to determine the level of risk of the auto -\nmated system. In settings\
	\ where the consequences are high as determined by a risk assessment, or extensive\
	\ \noversight is expected (e.g., in criminal justice or some public sector settings),\
	\ explanatory mechanisms should be built into the system design so that the system’s\
	\ full behavior can be explained in advance (i.e., only fully transparent models\
	\ should be used), rather than as an after-the-decision interpretation. In other\
	\ settings, the extent of explanation provided should be tailored to the risk\
	\ level. \nValid. The explanation provided by a system should accurately reflect\
	\ the factors and the influences that led \nto a particular decision, and should\
	\ be meaningful for the particular customization based on purpose, target, and\
	\ level of risk. While approximation and simplification may be necessary for the\
	\ system to succeed based on the explanatory purpose and target of the explanation,\
	\ or to account for the risk of fraud or other concerns related to revealing decision-making\
	\ information, such simplifications should be done in a scientifically supportable\
	\ way. Where appropriate based on the explanatory system, error ranges for the\
	\ explanation should be calculated and included in the explanation, with the choice\
	\ of presentation of such information balanced with usability and overall interface\
	\ complexity concerns. \nDemonstrate protections for notice and explanation \n\
	Reporting. Summary reporting should document the determinations made based on\
	\ the above consider -\nations, including: the responsible entities for accountability\
	\ purposes; the goal and use cases for the system, identified users, and impacted\
	\ populations; the assessment of notice clarity and timeliness; the assessment\
	\ of the explanation's validity and accessibility; the assessment of the level\
	\ of risk; and the account and assessment of how explanations are tailored, including\
	\ to the purpose, the recipient of the explanation, and the level of risk. Individualized\
	\ profile information should be made readily available to the greatest extent\
	\ possible that includes explanations for any system impacts or inferences. Reporting\
	\ should be provided in a clear plain language and machine-readable manner. \n\
	44"
	- "25 MP-2.3-002 Review and document accuracy, representativeness, relevance, suitability\
	\ of data \nused at diﬀerent stages of AI life cycle. Harmful Bias and Homogenization\
	\ ; \nIntellectual Property \nMP-2.3-003 Deploy and document fact -checking techniques\
	\ to verify the accuracy and \nveracity of information generated by GAI systems,\
	\ especially when the \ninformation comes from multiple (or unknown) sources.\
	\ Information Integrity \nMP-2.3-004 Develop and implement testing techniques\
	\ to identify GAI produced content (e.g., synthetic media) that might be indistinguishable\
	\ from human -generated content. Information Integrity \nMP-2.3-005 Implement\
	\ plans for GAI systems to undergo regular adversarial testing to identify \n\
	vulnerabilities and potential manipulation or misuse. Information Security \n\
	AI Actor Tasks: AI Development, Domain Experts, TEVV \n \nMAP 3.4: Processes\
	\ for operator and practitioner proﬁciency with AI system performance and trustworthiness\
	\ – and relevant \ntechnical standards and certiﬁcations – are deﬁned, assessed,\
	\ and documented. \nAction ID Suggested Action GAI Risks \nMP-3.4-001 Evaluate\
	\ whether GAI operators and end -users can accurately understand \ncontent lineage\
	\ and origin. Human -AI Conﬁguration ; \nInformation Integrity \nMP-3.4-002\
	\ Adapt existing training programs to include modules on digital content \ntransparency.\
	\ Information Integrity \nMP-3.4-003 Develop certiﬁcation programs that test\
	\ proﬁciency in managing GAI risks and \ninterpreting content provenance, relevant\
	\ to speciﬁc industry and context. Information Integrity \nMP-3.4-004 Delineate\
	\ human proﬁciency tests from tests of GAI capabilities. Human -AI Conﬁguration\
	\ \nMP-3.4-005 Implement systems to continually monitor and track the outcomes\
	\ of human- GAI \nconﬁgurations for future reﬁnement and improvements . Human\
	\ -AI Conﬁguration ; \nInformation Integrity \nMP-3.4-006 Involve the end -users,\
	\ practitioners, and operators in GAI system in prototyping \nand testing activities.\
	\ Make sure these tests cover various scenarios , such as crisis \nsituations\
	\ or ethically sensitive contexts. Human -AI Conﬁguration ; \nInformation Integrity\
	\ ; Harmful Bias \nand Homogenization ; Dangerous , \nViolent, or Hateful Content\
	\ \nAI Actor Tasks: AI Design, AI Development, Domain Experts, End -Users, Human\
	\ Factors, Operation and Monitoring"
	- '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 andStatistical 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.html72. 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'
	model-index:
	- name: SentenceTransformer based on Alibaba-NLP/gte-large-en-v1.5
	results:
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: Unknown
	type: unknown
	metrics:
	- type: cosine_accuracy@1
	value: 0.71875
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.921875
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.96875
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 1.0
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.71875
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.30729166666666663
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.19374999999999998
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.09999999999999999
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.71875
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.921875
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.96875
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 1.0
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.8727659974381962
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.8304687500000002
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.8304687500000001
	name: Cosine Map@100
	- type: dot_accuracy@1
	value: 0.734375
	name: Dot Accuracy@1
	- type: dot_accuracy@3
	value: 0.921875
	name: Dot Accuracy@3
	- type: dot_accuracy@5
	value: 0.96875
	name: Dot Accuracy@5
	- type: dot_accuracy@10
	value: 1.0
	name: Dot Accuracy@10
	- type: dot_precision@1
	value: 0.734375
	name: Dot Precision@1
	- type: dot_precision@3
	value: 0.30729166666666663
	name: Dot Precision@3
	- type: dot_precision@5
	value: 0.19374999999999998
	name: Dot Precision@5
	- type: dot_precision@10
	value: 0.09999999999999999
	name: Dot Precision@10
	- type: dot_recall@1
	value: 0.734375
	name: Dot Recall@1
	- type: dot_recall@3
	value: 0.921875
	name: Dot Recall@3
	- type: dot_recall@5
	value: 0.96875
	name: Dot Recall@5
	- type: dot_recall@10
	value: 1.0
	name: Dot Recall@10
	- type: dot_ndcg@10
	value: 0.8785327200386421
	name: Dot Ndcg@10
	- type: dot_mrr@10
	value: 0.8382812500000002
	name: Dot Mrr@10
	- type: dot_map@100
	value: 0.8382812500000001
	name: Dot Map@100
	---

	# SentenceTransformer based on Alibaba-NLP/gte-large-en-v1.5

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

	## Model Details

	### Model Description
	- Model Type: Sentence Transformer
	- Base model: [Alibaba-NLP/gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) <!-- at revision 104333d6af6f97649377c2afbde10a7704870c7b -->
	- Maximum Sequence Length: 8192 tokens
	- Output Dimensionality: 1024 tokens
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	<!-- - Language: Unknown -->
	<!-- - License: Unknown -->

	### Model Sources

	- Documentation: [Sentence Transformers Documentation](https://sbert.net)
	- Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
	- Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

	### Full Model Architecture

	```
	SentenceTransformer(
	(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NewModel
	(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
	)
	```

	## Usage

	### Direct Usage (Sentence Transformers)

	First install the Sentence Transformers library:

	```bash
	pip install -U sentence-transformers
	```

	Then you can load this model and run inference.
	```python
	from sentence_transformers import SentenceTransformer

	# Download from the 🤗 Hub
	model = SentenceTransformer("sentence_transformers_model_id")
	# Run inference
	sentences = [
	'What are some of the legal frameworks mentioned in the context that aim to protect personal information, and how do they relate to data privacy concerns?',
	"65. See, e.g., Scott Ikeda. Major Data Broker Exposes 235 Million Social Media Profiles in Data Lead: Info\nAppears to Have Been Scraped Without Permission. CPO Magazine. Aug. 28, 2020. https://\nwww.cpomagazine.com/cyber-security/major-data-broker-exposes-235-million-social-media-profiles-\nin-data-leak/; Lily Hay Newman. 1.2 Billion Records Found Exposed Online in a Single Server . WIRED,\nNov. 22, 2019. https://www.wired.com/story/billion-records-exposed-online/\n66.Lola Fadulu. Facial Recognition Technology in Public Housing Prompts Backlash . New York Times.\nSept. 24, 2019.\nhttps://www.nytimes.com/2019/09/24/us/politics/facial-recognition-technology-housing.html\n67. Jo Constantz. ‘They Were Spying On Us’: Amazon, Walmart, Use Surveillance Technology to Bust\nUnions. Newsweek. Dec. 13, 2021.\nhttps://www.newsweek.com/they-were-spying-us-amazon-walmart-use-surveillance-technology-bust-\nunions-1658603\n68. See, e.g., enforcement actions by the FTC against the photo storage app Everalbaum\n(https://www.ftc.gov/legal-library/browse/cases-proceedings/192-3172-everalbum-inc-matter), and\nagainst Weight Watchers and their subsidiary Kurbo(https://www.ftc.gov/legal-library/browse/cases-proceedings/1923228-weight-watchersww)\n69. See, e.g., HIPAA, Pub. L 104-191 (1996); Fair Debt Collection Practices Act (FDCPA), Pub. L. 95-109\n(1977); Family Educational Rights and Privacy Act (FERPA) (20 U.S.C. § 1232g), Children's Online\nPrivacy Protection Act of 1998, 15 U.S.C. 6501–6505, and Confidential Information Protection andStatistical Efficiency Act (CIPSEA) (116 Stat. 2899)\n70. Marshall Allen. You Snooze, You Lose: Insurers Make The Old Adage Literally True . ProPublica. Nov.\n21, 2018.\nhttps://www.propublica.org/article/you-snooze-you-lose-insurers-make-the-old-adage-literally-true\n71.Charles Duhigg. How Companies Learn Your Secrets. The New York Times. Feb. 16, 2012.\nhttps://www.nytimes.com/2012/02/19/magazine/shopping-habits.html72. Jack Gillum and Jeff Kao. Aggression Detectors: The Unproven, Invasive Surveillance Technology\nSchools are Using to Monitor Students. ProPublica. Jun. 25, 2019.\nhttps://features.propublica.org/aggression-detector/the-unproven-invasive-surveillance-technology-\nschools-are-using-to-monitor-students/\n73.Drew Harwell. Cheating-detection companies made millions during the pandemic. Now students are\nfighting back. Washington Post. Nov. 12, 2020.\nhttps://www.washingtonpost.com/technology/2020/11/12/test-monitoring-student-revolt/\n74. See, e.g., Heather Morrison. Virtual Testing Puts Disabled Students at a Disadvantage. Government\nTechnology. May 24, 2022.\nhttps://www.govtech.com/education/k-12/virtual-testing-puts-disabled-students-at-a-disadvantage;\nLydia X. Z. Brown, Ridhi Shetty, Matt Scherer, and Andrew Crawford. Ableism And Disability\nDiscrimination In New Surveillance Technologies: How new surveillance technologies in education,\npolicing, health care, and the workplace disproportionately harm disabled people . Center for Democracy\nand 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/\n69",
	'25 MP-2.3-002 Review and document accuracy, representativeness, relevance, suitability of data \nused at diﬀerent stages of AI life cycle. Harmful Bias and Homogenization ; \nIntellectual Property \nMP-2.3-003 Deploy and document fact -checking techniques to verify the accuracy and \nveracity of information generated by GAI systems, especially when the \ninformation comes from multiple (or unknown) sources. Information Integrity \nMP-2.3-004 Develop and implement testing techniques to identify GAI produced content (e.g., synthetic media) that might be indistinguishable from human -generated content. Information Integrity \nMP-2.3-005 Implement plans for GAI systems to undergo regular adversarial testing to identify \nvulnerabilities and potential manipulation or misuse. Information Security \nAI Actor Tasks: AI Development, Domain Experts, TEVV \n \nMAP 3.4: Processes for operator and practitioner proﬁciency with AI system performance and trustworthiness – and relevant \ntechnical standards and certiﬁcations – are deﬁned, assessed, and documented. \nAction ID Suggested Action GAI Risks \nMP-3.4-001 Evaluate whether GAI operators and end -users can accurately understand \ncontent lineage and origin. Human -AI Conﬁguration ; \nInformation Integrity \nMP-3.4-002 Adapt existing training programs to include modules on digital content \ntransparency. Information Integrity \nMP-3.4-003 Develop certiﬁcation programs that test proﬁciency in managing GAI risks and \ninterpreting content provenance, relevant to speciﬁc industry and context. Information Integrity \nMP-3.4-004 Delineate human proﬁciency tests from tests of GAI capabilities. Human -AI Conﬁguration \nMP-3.4-005 Implement systems to continually monitor and track the outcomes of human- GAI \nconﬁgurations for future reﬁnement and improvements . Human -AI Conﬁguration ; \nInformation Integrity \nMP-3.4-006 Involve the end -users, practitioners, and operators in GAI system in prototyping \nand testing activities. Make sure these tests cover various scenarios , such as crisis \nsituations or ethically sensitive contexts. Human -AI Conﬁguration ; \nInformation Integrity ; Harmful Bias \nand Homogenization ; Dangerous , \nViolent, or Hateful Content \nAI Actor Tasks: AI Design, AI Development, Domain Experts, End -Users, Human Factors, Operation and Monitoring',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 1024]

	# Get the similarity scores for the embeddings
	similarities = model.similarity(embeddings, embeddings)
	print(similarities.shape)
	# [3, 3]
	```

	<!--
	### Direct Usage (Transformers)

	<details><summary>Click to see the direct usage in Transformers</summary>

	</details>
	-->

	<!--
	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

	</details>
	-->

	<!--
	### Out-of-Scope Use

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	## Evaluation

	### Metrics

	#### Information Retrieval

	* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| cosine_accuracy@1 \| 0.7188 \|
	\| cosine_accuracy@3 \| 0.9219 \|
	\| cosine_accuracy@5 \| 0.9688 \|
	\| cosine_accuracy@10 \| 1.0 \|
	\| cosine_precision@1 \| 0.7188 \|
	\| cosine_precision@3 \| 0.3073 \|
	\| cosine_precision@5 \| 0.1937 \|
	\| cosine_precision@10 \| 0.1 \|
	\| cosine_recall@1 \| 0.7188 \|
	\| cosine_recall@3 \| 0.9219 \|
	\| cosine_recall@5 \| 0.9688 \|
	\| cosine_recall@10 \| 1.0 \|
	\| cosine_ndcg@10 \| 0.8728 \|
	\| cosine_mrr@10 \| 0.8305 \|
	\| cosine_map@100 \| 0.8305 \|
	\| dot_accuracy@1 \| 0.7344 \|
	\| dot_accuracy@3 \| 0.9219 \|
	\| dot_accuracy@5 \| 0.9688 \|
	\| dot_accuracy@10 \| 1.0 \|
	\| dot_precision@1 \| 0.7344 \|
	\| dot_precision@3 \| 0.3073 \|
	\| dot_precision@5 \| 0.1937 \|
	\| dot_precision@10 \| 0.1 \|
	\| dot_recall@1 \| 0.7344 \|
	\| dot_recall@3 \| 0.9219 \|
	\| dot_recall@5 \| 0.9688 \|
	\| dot_recall@10 \| 1.0 \|
	\| dot_ndcg@10 \| 0.8785 \|
	\| dot_mrr@10 \| 0.8383 \|
	\| dot_map@100 \| 0.8383 \|

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	## Training Details

	### Training Dataset

	#### Unnamed Dataset


	* Size: 586 training samples
	* Columns: <code>sentence_0</code> and <code>sentence_1</code>
	* Approximate statistics based on the first 586 samples:
	\| \| sentence_0 \| sentence_1 \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 20 tokens</li><li>mean: 35.95 tokens</li><li>max: 60 tokens</li></ul> \| <ul><li>min: 8 tokens</li><li>mean: 545.8 tokens</li><li>max: 1018 tokens</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \|
	\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>What are the primary objectives outlined in the "Blueprint for an AI Bill of Rights" as it pertains to the American people?</code> \| <code>BLUEPRINT FOR AN <br>AI B ILL OF <br>RIGHTS <br>MAKING AUTOMATED <br>SYSTEMS WORK FOR <br>THE AMERICAN PEOPLE <br>OCTOBER 2022</code> \|
	\| <code>In what ways does the document propose to ensure that automated systems are designed and implemented to benefit society?</code> \| <code>BLUEPRINT FOR AN <br>AI B ILL OF <br>RIGHTS <br>MAKING AUTOMATED <br>SYSTEMS WORK FOR <br>THE AMERICAN PEOPLE <br>OCTOBER 2022</code> \|
	\| <code>What is the primary purpose of the Blueprint for an AI Bill of Rights as published by the White House Office of Science and Technology Policy in October 2022?</code> \| <code>About this Document <br>The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People was <br>published by the White House Office of Science and Technology Policy in October 2022. This framework was <br>released one year after OSTP announced the launch of a process to develop “a bill of rights for an AI-powered <br>world.” Its release follows a year of public engagement to inform this initiative. The framework is available <br>online at: https://www.whitehouse.gov/ostp/ai-bill-of-rights <br>About the Office of Science and Technology Policy <br>The Office of Science and Technology Policy (OSTP) was established by the National Science and Technology <br>Policy, Organization, and Priorities Act of 1976 to provide the President and others within the Executive Office <br>of the President with advice on the scientific, engineering, and technological aspects of the economy, national <br>security, health, foreign relations, the environment, and the technological recovery and use of resources, among <br>other topics. OSTP leads interagency science and technology policy coordination efforts, assists the Office of <br>Management and Budget (OMB) with an annual review and analysis of Federal research and development in <br>budgets, and serves as a source of scientific and technological analysis and judgment for the President with <br>respect to major policies, plans, and programs of the Federal Government. <br>Legal Disclaimer <br>The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People is a white paper <br>published by the White House Office of Science and Technology Policy. It is intended to support the <br>development of policies and practices that protect civil rights and promote democratic values in the building, <br>deployment, and governance of automated systems. <br>The Blueprint for an AI Bill of Rights is non-binding and does not constitute U.S. government policy. It <br>does not supersede, modify, or direct an interpretation of any existing statute, regulation, policy, or <br>international instrument. It does not constitute binding guidance for the public or Federal agencies and <br>therefore does not require compliance with the principles described herein. It also is not determinative of what <br>the U.S. government’s position will be in any international negotiation. Adoption of these principles may not <br>meet the requirements of existing statutes, regulations, policies, or international instruments, or the <br>requirements of the Federal agencies that enforce them. These principles are not intended to, and do not, <br>prohibit or limit any lawful activity of a government agency, including law enforcement, national security, or <br>intelligence activities. <br>The appropriate application of the principles set forth in this white paper depends significantly on the <br>context in which automated systems are being utilized. In some circumstances, application of these principles <br>in whole or in part may not be appropriate given the intended use of automated systems to achieve government <br>agency missions. Future sector-specific guidance will likely be necessary and important for guiding the use of <br>automated systems in certain settings such as AI systems used as part of school building security or automated <br>health diagnostic systems. <br>The Blueprint for an AI Bill of Rights recognizes that law enforcement activities require a balancing of <br>equities, for example, between the protection of sensitive law enforcement information and the principle of <br>notice; as such, notice may not be appropriate, or may need to be adjusted to protect sources, methods, and <br>other law enforcement equities. Even in contexts where these principles may not apply in whole or in part, <br>federal departments and agencies remain subject to judicial, privacy, and civil liberties oversight as well as <br>existing policies and safeguards that govern automated systems, including, for example, Executive Order 13960, <br>Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government (December 2020). <br>This white paper recognizes that national security (which includes certain law enforcement and <br>homeland security activities) and defense activities are of increased sensitivity and interest to our nation’s <br>adversaries and are often subject to special requirements, such as those governing classified information and <br>other protected data. Such activities require alternative, compatible safeguards through existing policies that <br>govern automated systems and AI, such as the Department of Defense (DOD) AI Ethical Principles and <br>Responsible AI Implementation Pathway and the Intelligence Community (IC) AI Ethics Principles and <br>Framework. The implementation of these policies to national security and defense activities can be informed by <br>the Blueprint for an AI Bill of Rights where feasible.</code> \|
	* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim"
	}
	```

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 5
	- `per_device_eval_batch_size`: 5
	- `num_train_epochs`: 2
	- `multi_dataset_batch_sampler`: round_robin

	#### All Hyperparameters
	<details><summary>Click to expand</summary>

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: steps
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 5
	- `per_device_eval_batch_size`: 5
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 1
	- `eval_accumulation_steps`: None
	- `torch_empty_cache_steps`: None
	- `learning_rate`: 5e-05
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1
	- `num_train_epochs`: 2
	- `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

	</details>

	### Training Logs
	\| Epoch \| Step \| dot_map@100 \|
	\|:------:\|:----:\|:-----------:\|
	\| 0.4237 \| 50 \| 0.8383 \|


	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.1.1
	- Transformers: 4.44.2
	- PyTorch: 2.4.1+cu121
	- Accelerate: 0.34.2
	- Datasets: 3.0.1
	- Tokenizers: 0.19.1

	## Citation

	### BibTeX

	#### Sentence Transformers
	```bibtex
	@inproceedings{reimers-2019-sentence-bert,
	title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
	author = "Reimers, Nils and Gurevych, Iryna",
	booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
	month = "11",
	year = "2019",
	publisher = "Association for Computational Linguistics",
	url = "https://arxiv.org/abs/1908.10084",
	}
	```

	#### MultipleNegativesRankingLoss
	```bibtex
	@misc{henderson2017efficient,
	title={Efficient Natural Language Response Suggestion for Smart Reply},
	author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
	year={2017},
	eprint={1705.00652},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	```

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