Model: lw2134/policy_gte_large_2plus
Author: lw2134
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, onnx, safetensors, new, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:586, loss:MultipleNegativesRankingLoss, custom_code, arxiv:1908.10084, arxiv:1705.00652, base_model:Alibaba-NLP/gte-large-en-v1.5, base_model:quantized:Alibaba-NLP/gte-large-en-v1.5, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (quantized): id: Alibaba-NLP/gte-large-en-v1.5
Card metadata: base_model: Alibaba-NLP/gte-large-en-v1.5
library_name: sentence-transformers
metrics: cosine_accuracy@1, cosine_accuracy@3, cosine_accuracy@5, cosine_accuracy@10, cosine_precision@1, cosine_precision@3, cosine_precision@5, cosine_precision@10, cosine_recall@1, cosine_recall@3, cosine_recall@5, cosine_recall@10, cosine_ndcg@10, cosine_mrr@10, cosine_map@100, dot_accuracy@1, dot_accuracy@3, dot_accuracy@5, dot_accuracy@10, dot_precision@1, dot_precision@3, dot_precision@5, dot_precision@10, dot_recall@1, dot_recall@3, dot_recall@5, dot_recall@10, dot_ndcg@10, dot_mrr@10, dot_map@100
pipeline_tag: sentence-similarity
tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size: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
critical AI priorities.
 Released a  for designing safe, secure, and trustworthy AI tools
for use in education. The Department of Education’s guide discusses
how developers of educational technologies can design AI that beneﬁts
students and teachers while advancing equity, civil rights, trust, and
transparency. This work builds on the Department’s 2023 
outlining recommendations for the use of AI in teaching and learning.
 Published guidance on evaluating the eligibility of patent claims
involving inventions related to AI technology, as well as other
emerging technologies. The guidance by the U.S. Patent and Trademark
Oﬃce will guide those inventing in the AI space to protect their AI
inventions and assist patent examiners reviewing applications for
patents on AI inventions.
 Issued a  on federal research and development (R&D) to
advance trustworthy AI over the past four years. The report by the
National Science and Technology Council examines an annual federal AI
R&D budget of nearly $3 billion.
 Launched a $23 million initiative to promote the use of privacy-
enhancing technologies to solve real-world problems, including
related to AI. Working with industry and agency partners, NSF will
invest through its new Privacy-preserving Data Sharing in Practice
program in eﬀorts to apply, mature, and scale privacy-enhancing
technologies for speciﬁc use cases and establish testbeds to accelerate
their adoption.
 Announced millions of dollars in further investments to advance
responsible AI development and use throughout our society. These
include $30 million invested through NSF’s Experiential Learning in
Emerging and Novel Technologies program—which supports inclusive
experiential learning in ﬁelds like AI—and $10 million through NSF’s
ExpandAI program, which helps build capacity in AI research at
minority-serving institutions while fostering the development of a
diverse, AI-ready workforce.
Advancing U.S. Leadership Abroad
President Biden’s Executive Order emphasized that the United States lead
global eﬀorts to unlock AI’s potential and meet its challenges. To advance
U.S. leadership on AI, agencies have:guide
report
report05/10/2024, 16:35 FACT SHEET: Biden-Harris Administration Announces New AI Actions and Receives Additional Major Voluntary Commitment on AI | The…
https://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 
on the risks of advanced AI commissioned by 
the United Kingdom,25 and relevant regional 
organizations.
e. A steering committee would develop a research 
agenda ensuring the inclusivity of views and 
incorporation of ethical considerations, oversee 
the allocation of resources, foster collaboration 
with a network of academic institutions and 
other stakeholders, and review the panel’s 
activities and deliverables.100 By drawing on the unique convening power of the 
United Nations and inclusive global reach across 
stakeholder groups, an international scientific panel 
can deliver trusted scientific collaboration processes 
and outputs and correct information asymmetries 
in ways that address the representation and 
coordination gaps identified in paragraphs 66 and 
73, thereby promoting equitable and effective 
international AI governance.
Among the topics discussed in our consultations was the ongoing debate over open versus closed AI systems. 
AI systems that are open in varying degrees are often referred to as “open-source AI”, but this is somewhat of a 
misnomer when compared with open-source software (code). It is important to recognize that openness in AI 
systems is more of a spectrum than a single attribute.
One article explained that a “fully closed AI system is only accessible to a particular group. It could be an AI 
developer company or a specific group within it, mainly for internal research and development purposes. On the 
other hand, more open systems may allow public access or make available certain parts, such as data, code, or 
model characteristics, to facilitate external AI development.”a
Open-source AI systems in the generative AI field present both risks and opportunities. Companies often cite “AI 
safety” as a reason for not disclosing system specifications, reflecting the ongoing tension between open and 
closed approaches in the industry. Debates typically revolve around two extremes: full openness, which entails 
sharing all model components and data sets; and partial openness, which involves disclosing only model weights. 
Open-source AI systems encourage innovation and are often a requirement for public funding. On the open 
extreme of the spectrum, when the underlying code is made freely available, developers around the world can 
experiment, improve and create new applications. This fosters a collaborative environment where ideas and 
expertise are readily shared. Some industry leaders argue that this openness is vital to innovation and economic 
growth.
However, in most cases, open-source AI models are available as application programming interfaces. In this case, 
the original code is not shared, the original weights are never changed and model updates become new models. 
Additionally, open-source models tend to be smaller and more transparent. This transparency can build trust, 
allow for ethical considerations to be proactively addressed, and support validation and replication because users 
can examine the inner workings of the AI system, understand its decision-making process and identify potential 
biases.Box 9: Open versus closed AI systems
a Angela Luna, “The open or closed AI dilemma”, 2 May 2024. Available at https://bipartisanpolicy.org/blog/the-open-or-closed-ai-dilemma .
25 International Scientific Report on the Safety of Advanced AI: Interim Report. Available at https://gov.uk/government/publications/international-scientific-report-
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, 
CONSIDERATION, AND 
FALLBACK 
HOW THESE PRINCIPLES CAN MOVE INTO PRACTICE
Real-life examples of how these principles can become reality, through laws, policies, and practical 
technical and sociotechnical approaches to protecting rights, opportunities, and access. 
Healthcare “navigators” help people find their way through online signup forms to choose 
and obtain healthcare. A Navigator is “an individual or organization that's trained and able to help 
consumers, small businesses, and their employees as they look for health coverage options through the 
Marketplace (a government web site), including completing eligibility and enrollment forms.”106 For 
the 2022 plan year, the Biden-Harris Administration increased funding so that grantee organizations could 
“train and certify more than 1,500 Navigators to help uninsured consumers find affordable and comprehensive 
health coverage. ”107
The customer service industry has successfully integrated automated services such as 
chat-bots and AI-driven call response systems with escalation to a human support team.
108 Many businesses now use partially automated customer service platforms that help answer customer 
questions and compile common problems for human agents to review. These integrated human-AI 
systems allow companies to provide faster customer care while maintaining human agents to answer 
calls or otherwise respond to complicated requests. Using both AI and human agents is viewed as key to 
successful customer service.109
Ballot curing laws in at least 24 states require a fallback system that allows voters to 
correct 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.
110 Ballot 
curing processes vary among states, and include direct phone calls, emails, or mail contact by election 
officials.111 Voters are asked to provide alternative information or a new signature to verify the validity of their 
ballot. 
52, SECTION  TITLE
HUMAN  ALTERNATIVES , C ONSIDERATION , AND FALLBACK
You should be able to opt out, where appropriate, and have access to a person who can quickly 
consider and remedy problems you encounter. You should be able to opt out from automated systems in 
favor 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
-
tive domains, including, but not limited to, criminal justice, employment, education, and health, should additional -
ly 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. 
Definitions 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. 
Accompanying analysis and tools for actualizing each principle can be found in the Technical Companion. 
7, Final Report    21E. Reflections on institutional 
models
lxiv Discussions about AI often resolve into extremes. 
In our consultations around the world, we engaged 
with those who see a future of boundless goods 
provided by ever-cheaper, ever-more-helpful AI 
systems. We also spoke with those wary of darker 
futures, of division and unemployment, and even 
extinction.8
lxv We do not know whether the utopian or dystopian 
future is more likely. Equally, we are mindful that 
the technology may go in a direction that does 
away with this duality. This report focuses on 
the near-term opportunities and risks, based on 
science and grounded in fact. 
lxvi The seven recommendations outlined above offer 
our best hope for reaping the benefits of AI, while 
minimizing and mitigating the risks, as AI continues 
evolving. We are also mindful of the practical 
challenges to international institution-building 
on a larger scale. This is why we are proposing a 
networked institutional approach, with light and 
agile support. If or when risks become more acute 
and the stakes for opportunities escalate, such 
calculations may change. 
lxvii The world wars led to the modern international 
system; the development of ever-more-powerful 
chemical, biological and nuclear weapons led 
to regimes limiting their spread and promoting 
peaceful uses of the underlying technologies. 
Evolving understanding of our common humanity 
led to the modern human rights system and our 
ongoing commitment to the SDGs for all. Climate 
change evolved from a niche concern to a global 
challenge.lxviii  AI may similarly rise to a level that requires more 
resources and more authority than is proposed 
in the above-mentioned recommendations, 
into harder functions of norm elaboration, 
implementation, monitoring, verification and 
validation, enforcement, accountability, remedies 
for harm and emergency responses. Reflecting on 
such institutional models, therefore, is prudent. The 
final section of this report seeks to contribute to 
that effort.
4. A call to action
lxix We remain optimistic about the future with AI and 
its positive potential. That optimism depends, 
however, on realism about the risks and the 
inadequacy of structures and incentives currently 
in place. The technology is too important, and the 
stakes are too high, to rely only on market forces 
and a fragmented patchwork of national and 
multilateral action.
lxx The United Nations can be the vehicle for a new 
social contract for AI that ensures global buy-
in for a governance regime which protects and 
empowers us all. Such a social contract will ensure 
that opportunities are fairly distributed, and the 
risks are not loaded on to the most vulnerable – or 
passed on to future generations, as we have seen, 
tragically, with climate change.
lxxi As a group and as individuals from across many 
fields of expertise, organizations and parts of the 
world, we look forward to continuing this crucial 
conversation. Together with the many others we 
have connected with on this journey, and the global 
community they represent, we hope that this report 
contributes to our combined efforts to govern AI 
for humanity.
8   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
72 The ongoing emergence and evolution of AI 
governance initiatives are not guaranteed to 
work together effectively for humanity. Instead, 
coordination gaps have appeared. Effective 
handshaking between the selective plurilateral 
initiatives (see fig. 8) and other regional initiatives is 
not assured, risking incompatibility between regions.
73 Nor are there global mechanisms for all international 
standards development organizations (see fig. 7), 
international scientific research initiatives or AI 
capacity-building initiatives to coordinate with each 
other, undermining interoperability of approaches 
and resulting in fragmentation. The resulting 
coordination gaps between various sub-global 
initiatives are in some cases best addressed at the 
global level.
74 A separate set of coordination gaps arise within 
the United Nations system, reflected in the array of 
diverse United Nations documents and initiatives 
in relation to AI. Figure 9 shows 27 United Nations-
related instruments in specific domains that may 
apply to AI – 23 of them are binding and will require 
interpretation as they pertain to AI. A further 29 
domain-level documents from the United Nations 
and related organizations focus specifically on AI, 
none of which are binding.17 In some cases, these 
can address AI risks and harness AI benefits in 
specific domains.75 The level of activity shows the importance of AI 
to United Nations programmes. As AI expands to 
affect ever-wider aspects of society, there will be 
growing calls for diverse parts of the United Nations 
system to act, including through binding norms. 
It also shows the ad hoc nature of the responses, 
which have largely developed organically in specific 
domains and without an overarching strategy. The 
resulting coordination gaps invite overlaps and 
hinder interoperability and impact.
76 The number and diversity of approaches are a sign 
that the United Nations system is responding to 
an emerging issue. With proper orchestration, and 
in combination with processes taking a holistic 
approach, these efforts can offer an efficient and 
sustainable pathway to inclusive international AI 
governance in specific domains. This could enable 
meaningful, harmonized and coordinated impacts 
on areas such as health, education, technical 
standards and ethics, instead of merely contributing 
to the proliferation of initiatives and institutions 
in this growing field. International law, including 
international human rights law, provides a shared 
normative foundation for all AI-related efforts, 
thereby facilitating coordination and coherence.,  Issued a comprehensive plan for U.S. engagement on global AI
standards. The plan, developed by the NIST, incorporates broad public
and private-sector input, identiﬁes objectives and priority areas for AI
standards work, and lays out actions for U.S. stakeholders including U.S.
agencies. NIST and others agencies will report on priority actions in 180
days. 
 Developed  for managing risks to human rights posed by AI.
The Department of State’s “Risk Management Proﬁle for AI and Human
Rights”—developed in close coordination with NIST and the U.S. Agency
for International Development—recommends actions based on the NIST
AI Risk Management Framework to governments, the private sector, and
civil society worldwide, to identify and manage risks to human rights
arising from the design, development, deployment, and use of AI. 
 Launched a global network of AI Safety Institutes and other
government-backed scientiﬁc oﬃces to advance AI safety at a technical
level. This network will accelerate critical information exchange and
drive toward common or compatible safety evaluations and policies.
 Launched a landmark United Nations General Assembly resolution.
The unanimously adopted resolution, with more than 100 co-sponsors,
lays out a common vision for countries around the world to promote the
safe and secure use of AI to address global challenges.
 Expanded global support for the U.S.-led Political Declaration on the
Responsible Military Use of Artiﬁcial Intelligence and
Autonomy.  Fifty-ﬁve nations now endorse the political declaration,
which outlines a set of norms for the responsible development,
deployment, and use of military AI capabilities.
The Table below summarizes many of the activities that federal agencies
have 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…
https://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 
technology companies and civil society 
representatives – could be invited to engage 
through existing institutions detailed below, as 
well as policy workshops on particular aspects 
of AI governance such as limits (if any) of open-
source approaches to the most advanced forms 
of AI, thresholds for tracking and reporting of 
AI incidents, application of human rights law to 
novel use cases, or the use of competition law/
antitrust to address concentrations of power 
among technology companies.30
g. The proposed AI office could also curate a 
repository of AI governance examples, including 
legislation, policies and institutions from 
around the world for consideration of the policy 
dialogue, working with existing efforts, such as 
OECD.
109 Notwithstanding the two General Assembly 
resolutions on AI in 2024, there is currently 
no mandated institutionalized dialogue on 
AI governance at the United Nations that 
corresponds to the reliably inclusive vision of this 
recommendation. Similar processes do exist at 
the international level, but primarily in regional or 
plurilateral constellations (para. 57), which are not 
reliably inclusive and global.
110 Complementing a fluid process of plurilateral and 
regional AI summits,31 the United Nations can 
offer a stable home for dialogue on AI governance. 
Inclusion by design – a crucial requirement for 
playing a stabilizing role in geopolitically delicate 
times – can also address representation and 
coordination gaps identified in paragraphs 64 and 
72, promoting more effective collective action on AI 
governance in the common interest of all countries.  AI standards exchange  
 
Recommendation 3: AI standards exchange  
 
We recommend the creation of an AI standards 
exchange, bringing together representatives from 
national and international standard-development 
organizations, technology companies, civil society 
and representatives from the international scientific 
panel. It would be tasked with:
a. Developing and maintaining a register of 
definitions and applicable standards for 
measuring and evaluating AI systems;
b. Debating and evaluating the standards and the 
processes for creating them; and
c. Identifying gaps where new standards are 
needed.
111 When AI systems were first explored, few standards 
existed to help to navigate or measure this new 
frontier. The Turing Test – of whether a machine can 
exhibit behaviour equivalent to (or indistinguishable 
from) a human being – captured the popular 
imagination, but is of more cultural than scientific 
significance. Indeed, it is telling that some of 
the greatest computational advances have been 
measured by their success in games, such as when 
a computer could beat humans at chess, Go, poker 
or Jeopardy. Such measures were easily understood 
by non-specialists, but were neither rigorous nor 
particularly scientific.
112 More recently, there has been a proliferation of 
standards. Figure 13 illustrates the increasing 
number of relevant standards adopted by ITU, the 
International Organization for Standardization (ISO), 
the International Electrotechnical Commission 
(IEC) and the Institute of Electrical and Electronics 
Engineers (IEEE).32
30 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 
example, prohibitions on the development of uncontainable or uncontrollable AI systems, or requirements that all AI systems be sufficiently transparent so that 
their consequences can be traced back to a legal actor that can assume responsibility for them.
31 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 
Ireland endorsed the Bletchley Declaration in November 2023, but not the Seoul Ministerial Statement six months later (see fig. 12). Conversely, Mexico and 
New Zealand endorsed the Seoul Ministerial Statement, but did not endorse the Bletchley Declaration.
32 Many new standards are also emerging at the national and multinational levels, such as the United States White House Voluntary AI Commitments and the 
European 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
Summaries of Additional Engagements: 
•OSTP created an email address ( ai-equity@ostp.eop.gov ) to solicit comments from the public on the use of
artificial intelligence and other data-driven technologies in their lives.
•OSTP issued a Request For Information (RFI) on the use and governance of biometric technologies.113 The
purpose of this RFI was to understand the extent and variety of biometric technologies in past, current, or
planned 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
114 and were submitted by the below
listed organizations and individuals:
Accenture 
Access Now ACT | The App Association AHIP 
AIethicist.org 
Airlines 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 
60, 19 GV-4.1-003 Establish policies, procedures, and processes for oversight functions (e.g., senior 
leadership, legal, compliance, including internal evaluation ) across the GAI 
lifecycle, from problem formulation and supply chains to system decommission.  Value Chain and Component 
Integration  
AI Actor Tasks:  AI Deployment, AI Design, AI Development, Operation and Monitoring  
 
GOVERN 4.2:  Organizational teams document the risks and potential impacts of the AI technology they design, develop, deploy, 
evaluate, and use, and they communicate about the impacts more broadly.  
Action ID  Suggested Action  GAI Risks  
GV-4.2-001 Establish terms of use and terms of service  for GAI systems . Intellectual Property ; Dangerous , 
Violent, or Hateful Content ; 
Obscene, Degrading, and/or 
Abusive Content  
GV-4.2-002 Include relevant AI Actors in the GAI system risk identiﬁcation process.  Human -AI Conﬁguration  
GV-4.2-0 03 Verify that downstream GAI system impacts (such as the use of third -party 
plugins) are included in the impact documentation process.  Value Chain and Component 
Integration  
AI Actor Tasks:  AI Deployment, AI Design, AI Development, Operation and Monitoring  
 
GOVERN 4.3: Organizational practices are in place to enable AI testing, identiﬁcation of incidents, and information sharing.  
Action ID  Suggested Action  GAI Risks  
GV4.3-- 001 Establish policies for measuring the eﬀectiveness of employed  content 
provenance methodologies (e.g., cryptography, watermarking, steganography, etc.) Information Integrity  
GV-4.3-002 Establish o rganizational  practices to  identify the minimum set of criteria 
necessary for GAI system incident reporting such as: System ID (auto -generated 
most 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
The AI office should have a light structure and aim to be agile, trusted and networked. Where necessary, it should 
operate in a “hub and spoke” manner to connect to other parts of the United Nations system and beyond.
Outreach could include serving as a key node in a so-called soft coordination architecture between Member 
States, plurilateral networks, civil society organizations, academia and technology companies in a regime complex 
that weaves together to solve problems collaboratively through networking, and as a safe, trusted place to 
convene on relevant topics. Ambitiously, it could become the glue that helps to hold such other evolving networks 
together.
Supporting the various initiatives proposed in this report includes the important function of ensuring inclusiveness 
at speed in delivering outputs such as scientific reports, governance dialogue and identifying appropriate follow-
up entities.
Common understanding :
• Facilitate recruitment of and support the international scientific panel.
Common ground :
• Service policy dialogues with multi-stakeholder inputs in support of interoperability and policy learning. 
An initial priority topic is the articulation of risk thresholds and safety frameworks across jurisdictions
• Support ITU, ISO/IEC and IEEE on setting up the AI standards exchange.
Common benefits :
• Support the AI capacity development network with an initial focus on building public interest AI capacity 
among public officials and social entrepreneurs. Define the initial network vision, outcomes, go vernance 
structure, partnerships and operational mechanisms.
• Define the vision, outcomes, governance structure and operational mechanisms for the global fund for AI, 
and seek feedback from Member States, industry and civil society stakeholders on the proposal, with a 
view to funding initial projects within six months of establishment.
• Prepare and publish an annual list of prioritized investment areas to guide both the global fund for AI and 
investments outside that structure.
Coherent effort :
• Establish lightweight mechanisms that support Member States and other relevant organizations to be 
more connected, coordinated and effective in pursuing their global AI governance efforts.
• Prepare initial frameworks to guide and monitor the AI office’s work, including a global governance risk 
taxonomy, a global AI policy landscape review and a global stakeholder map.
• Develop and implement quarterly reporting and periodic in-person presentations to Member States on 
the AI office’s progress against its workplan and establish feedback channels to support adjustments as 
needed.
• Establish a steering committee jointly led by the AI office, ITU, UNC TAD, UNESCO and other relevant 
United Nations entities and organizations to accelerate the work of the United Nations in service of the 
functions above, and review progress of the accelerated efforts every three months.
• Promote joint learning and development opportunities for Member State representatives to support them 
to carry out their responsibilities for global AI governance, in cooperation with relevant United Nations 
entities and organizations such as the United Nations Institute for Training and Research and the United 
Nations 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 & 
EXPLANATION 
WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS
The expectations for automated systems are meant to serve as a blueprint for the development of additional 
technical standards and practices that are tailored for particular sectors and contexts. 
Tailored to the level of risk. An assessment should be done to determine the level of risk of the auto -
mated system. In settings where the consequences are high as determined by a risk assessment, or extensive 
oversight is expected (e.g., in criminal justice or some public sector settings), explanatory mechanisms should be built into the system design so that the system’s full behavior can be explained in advance (i.e., only fully transparent models should be used), rather than as an after-the-decision interpretation. In other settings, the extent of explanation provided should be tailored to the risk level. 
Valid. The explanation provided by a system should accurately reflect the factors and the influences that led 
to a particular decision, and should be meaningful for the particular customization based on purpose, target, and level of risk. While approximation and simplification may be necessary for the system to succeed based on the explanatory purpose and target of the explanation, or to account for the risk of fraud or other concerns related to revealing decision-making information, such simplifications should be done in a scientifically supportable way. Where appropriate based on the explanatory system, error ranges for the explanation should be calculated and included in the explanation, with the choice of presentation of such information balanced with usability and overall interface complexity concerns. 
Demonstrate protections for notice and explanation 
Reporting. Summary reporting should document the determinations made based on the above consider -
ations, including: the responsible entities for accountability purposes; the goal and use cases for the system, identified users, and impacted populations; the assessment of notice clarity and timeliness; the assessment of the explanation's validity and accessibility; the assessment of the level of risk; and the account and assessment of how explanations are tailored, including to the purpose, the recipient of the explanation, and the level of risk. Individualized profile information should be made readily available to the greatest extent possible that includes explanations for any system impacts or inferences. Reporting should be provided in a clear plain language and machine-readable manner. 
44, 25 MP-2.3-002 Review and document accuracy, representativeness, relevance, suitability of data 
used at diﬀerent stages of AI life cycle.  Harmful Bias and Homogenization ; 
Intellectual Property  
MP-2.3-003 Deploy and document fact -checking techniques to verify the accuracy and 
veracity of information generated by GAI systems, especially when the 
information comes from multiple (or unknown) sources.  Information Integrity  
MP-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  
MP-2.3-005 Implement plans for GAI systems to undergo regular adversarial testing to identify 
vulnerabilities and potential manipulation or misuse.  Information Security  
AI Actor Tasks:  AI Development, Domain Experts, TEVV  
 
MAP 3.4:  Processes for operator and practitioner proﬁciency with AI system performance and trustworthiness – and relevant 
technical standards and certiﬁcations – are deﬁned, assessed, and documented.  
Action ID  Suggested Action  GAI Risks  
MP-3.4-001 Evaluate whether GAI operators and end -users can accurately understand 
content lineage and origin.  Human -AI Conﬁguration ; 
Information Integrity  
MP-3.4-002 Adapt existing training programs to include modules on digital content 
transparency.  Information Integrity  
MP-3.4-003 Develop certiﬁcation programs that test proﬁciency in managing GAI risks and 
interpreting content provenance, relevant to speciﬁc industry and context.  Information Integrity  
MP-3.4-004 Delineate human proﬁciency tests from tests of GAI capabilities.  Human -AI Conﬁguration  
MP-3.4-005 Implement systems to continually monitor and track the outcomes of human- GAI 
conﬁgurations for future reﬁnement and improvements . Human -AI Conﬁguration ; 
Information Integrity  
MP-3.4-006 Involve the end -users, practitioners, and operators in GAI system in prototyping 
and testing activities. Make sure these tests cover various scenarios , such as crisis 
situations or ethically sensitive contexts.  Human -AI Conﬁguration ; 
Information Integrity ; Harmful Bias 
and Homogenization ; Dangerous , 
Violent, or Hateful Content  
AI 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
verified: no, type: cosine_accuracy@3
value: 0.921875
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.96875
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.71875
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.30729166666666663
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.19374999999999998
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09999999999999999
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.71875
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.921875
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.96875
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 1
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.8727659974381962
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.8304687500000002
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.8304687500000001
name: Cosine Map@100
verified: no, type: dot_accuracy@1
value: 0.734375
name: Dot Accuracy@1
verified: no, type: dot_accuracy@3
value: 0.921875
name: Dot Accuracy@3
verified: no, type: dot_accuracy@5
value: 0.96875
name: Dot Accuracy@5
verified: no, type: dot_accuracy@10
value: 1
name: Dot Accuracy@10
verified: no, type: dot_precision@1
value: 0.734375
name: Dot Precision@1
verified: no, type: dot_precision@3
value: 0.30729166666666663
name: Dot Precision@3
verified: no, type: dot_precision@5
value: 0.19374999999999998
name: Dot Precision@5
verified: no, type: dot_precision@10
value: 0.09999999999999999
name: Dot Precision@10
verified: no, type: dot_recall@1
value: 0.734375
name: Dot Recall@1
verified: no, type: dot_recall@3
value: 0.921875
name: Dot Recall@3
verified: no, type: dot_recall@5
value: 0.96875
name: Dot Recall@5
verified: no, type: dot_recall@10
value: 1
name: Dot Recall@10
verified: no, type: dot_ndcg@10
value: 0.8785327200386421
name: Dot Ndcg@10
verified: no, type: dot_mrr@10
value: 0.8382812500000002
name: Dot Mrr@10
verified: no, type: dot_map@100
value: 0.8382812500000001
name: Dot Map@100
verified: no
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Created: 2024-10-05 21:11:29