Model: Mdean77/finetuned_arctic
Author: Mdean77
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:502, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss, arxiv:1908.10084, arxiv:2205.13147, arxiv:1705.00652, base_model:Snowflake/snowflake-arctic-embed-m, base_model:finetune:Snowflake/snowflake-arctic-embed-m, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: Snowflake/snowflake-arctic-embed-m
Card metadata: base_model: Snowflake/snowflake-arctic-embed-m
library_name: sentence-transformers
metrics: cosine_accuracy@1, cosine_accuracy@3, cosine_accuracy@5, cosine_accuracy@10, cosine_precision@1, cosine_precision@3, cosine_precision@5, cosine_precision@10, cosine_recall@1, cosine_recall@3, cosine_recall@5, cosine_recall@10, cosine_ndcg@10, cosine_mrr@10, cosine_map@100, dot_accuracy@1, dot_accuracy@3, dot_accuracy@5, dot_accuracy@10, dot_precision@1, dot_precision@3, dot_precision@5, dot_precision@10, dot_recall@1, dot_recall@3, dot_recall@5, dot_recall@10, dot_ndcg@10, dot_mrr@10, dot_map@100
pipeline_tag: sentence-similarity
tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:502, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
widget: source_sentence: How can the manipulation of prompts, known as "jailbreaking," lead to harmful recommendations from GAI systems?
sentences: but this approach may still produce harmful recommendations in response to other less-explicit, novel 
prompts (also relevant to CBRN Information or Capabilities, Data Privacy, Information Security, and 
Obscene, Degrading and/or Abusive Content). Crafting such prompts deliberately is known as 
“jailbreaking,” or, manipulating prompts to circumvent output controls. Limitations of GAI systems can be 
harmful or dangerous in certain contexts. Studies have observed that users may disclose mental health 
issues in conversations with chatbots – and that users exhibit negative reactions to unhelpful responses 
from these chatbots during situations of distress. 
This risk encompasses diﬃculty controlling creation of and public exposure to oﬀensive or hateful 
language, and denigrating or stereotypical content generated by AI. This kind of speech may contribute 
to downstream harm such as fueling dangerous or violent behaviors. The spread of denigrating or 
stereotypical content can also further exacerbate representational harms (see Harmful Bias and 
Homogenization below).  
Trustworthy AI Characteristics: Safe, Secure and Resilient 
2.4. Data Privacy 
GAI systems raise several risks to privacy. GAI system training requires large volumes of data, which in 
some cases may include personal data. The use of personal data for GAI training raises risks to widely, communities and using it to reinforce inequality. Various panelists suggested that these harms could be 
mitigated by ensuring community input at the beginning of the design process, providing ways to opt out of 
these systems and use associated human-driven mechanisms instead, ensuring timeliness of benefit payments, 
and providing clear notice about the use of these systems and clear explanations of how and what the 
technologies are doing. Some panelists suggested that technology should be used to help people receive 
benefits, e.g., by pushing benefits to those in need and ensuring automated decision-making systems are only 
used to provide a positive outcome; technology shouldn't be used to take supports away from people who need 
them. 
Panel 6: The Healthcare System. This event explored current and emerging uses of technology in the 
healthcare system and consumer products related to health. 
Welcome:
•
Alondra Nelson, Deputy Director for Science and Society, White House Office of Science and Technology
Policy
•
Patrick Gaspard, President and CEO, Center for American Progress
Moderator: Micky Tripathi, National Coordinator for Health Information Technology, U.S Department of 
Health and Human Services. 
Panelists: 
•
Mark Schneider, Health Innovation Advisor, ChristianaCare
•
Ziad Obermeyer, Blue Cross of California Distinguished Associate Professor of Policy and Management,, 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 rea­
sonable expectations in a given context and with a focus on ensuring 
broad accessibility and protecting the public from especially harm­
ful impacts. In some cases, a human or other alternative may be re­
quired by law. You should have access to timely human consider­
ation and remedy by a fallback and escalation process if an automat­
ed 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 unrea­
sonable burden on the public. Automated systems with an intended 
use within sensitive domains, including, but not limited to, criminal 
justice, employment, education, and health, should additionally be 
tailored to the purpose, provide meaningful access for oversight, 
include training for any people interacting with the system, and in­
corporate human consideration for adverse or high-risk decisions. 
Reporting that includes a description of these human governance 
processes and assessment of their timeliness, accessibility, out­, source_sentence: What are the potential consequences of model collapse in AI systems, particularly regarding output homogenization?
sentences: President ordered the full Federal government to work to root out inequity, embed fairness in decision-
making processes, and affirmatively advance civil rights, equal opportunity, and racial justice in America.1 The 
President has spoken forcefully about the urgent challenges posed to democracy today and has regularly called 
on people of conscience to act to preserve civil rights—including the right to privacy, which he has called “the 
basis for so many more rights that we have come to take for granted that are ingrained in the fabric of this 
country.”2
To advance President Biden’s vision, the White House Office of Science and Technology Policy has identified 
five principles that should guide the design, use, and deployment of automated systems to protect the American 
public in the age of artificial intelligence. The Blueprint for an AI Bill of Rights is a guide for a society that 
protects all people from these threats—and uses technologies in ways that reinforce our highest values. 
Responding to the experiences of the American public, and informed by insights from researchers, 
technologists, advocates, journalists, and policymakers, this framework is accompanied by a technical 
companion—a handbook for anyone seeking to incorporate these protections into policy and practice, including 
detailed steps toward actualizing these principles in the technological design process. These principles help 
provide guidance whenever automated systems can meaningfully impact the public’s rights, opportunities,, Synopsis of Responses to OSTP’s Request for Information on the Use and Governance of Biometric
Technologies in the Public and Private Sectors. Science and Technology Policy Institute. Mar. 2022.
https://www.ida.org/-/media/feature/publications/s/sy/synopsis-of-responses-to-request-for­
information-on-the-use-and-governance-of-biometric-technologies/ida-document-d-33070.ashx
73
 
NIST Trustworthy and Responsible AI  
NIST AI 600-1 
Artificial Intelligence Risk Management 
Framework: Generative Artificial 
Intelligence Profile 
 
 
 
This publication is available free of charge from: 
https://doi.org/10.6028/NIST.AI.600-1 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
NIST Trustworthy and Responsible AI  
NIST AI 600-1 
Artificial Intelligence Risk Management 
Framework: Generative Artificial 
Intelligence Profile 
 
 
 
This publication is available free of charge from: 
https://doi.org/10.6028/NIST.AI.600-1 
 
July 2024 
 
 
 
 
U.S. Department of Commerce, new model’s outputs. In addition to threatening the robustness of the model overall, model collapse 
could lead to homogenized outputs, including by amplifying any homogenization from the model used to 
generate the synthetic training data. 
Trustworthy AI Characteristics: Fair with Harmful Bias Managed, Valid and Reliable 
2.7. Human-AI Conﬁguration 
GAI system use can involve varying risks of misconﬁgurations and poor interactions between a system 
and a human who is interacting with it. Humans bring their unique perspectives, experiences, or domain-
speciﬁc expertise to interactions with AI systems but may not have detailed knowledge of AI systems and 
how they work. As a result, human experts may be unnecessarily “averse” to GAI systems, and thus 
deprive themselves or others of GAI’s beneﬁcial uses.  
Conversely, due to the complexity and increasing reliability of GAI technology, over time, humans may 
over-rely on GAI systems or may unjustiﬁably perceive GAI content to be of higher quality than that 
produced by other sources. This phenomenon is an example of automation bias, or excessive deference 
to automated systems. Automation bias can exacerbate other risks of GAI, such as risks of confabulation 
or risks of bias or homogenization., source_sentence: How is sensitive data defined in relation to individual privacy and potential harm?
sentences: recognized voluntary consensus standard for web content and other information and communications 
technology. 
NIST has released Special Publication 1270, Towards a Standard for Identifying and Managing Bias 
in Artificial Intelligence.59 The special publication: describes the stakes and challenges of bias in artificial 
intelligence and provides examples of how and why it can chip away at public trust; identifies three categories 
of bias in AI – systemic, statistical, and human – and describes how and where they contribute to harms; and 
describes three broad challenges for mitigating bias – datasets, testing and evaluation, and human factors – and 
introduces preliminary guidance for addressing them. Throughout, the special publication takes a socio-
technical perspective to identifying and managing AI bias. 
29
Algorithmic 
Discrimination 
Protections 
You should be protected from abusive data practices via built-in 
protections and you should have agency over how data about 
you is used. You should be protected from violations of privacy through 
design choices that ensure such protections are included by default, including 
ensuring that data collection conforms to reasonable expectations and that 
only data strictly necessary for the specific context is collected. Designers, de­
velopers, and deployers of automated systems should seek your permission 
and respect your decisions regarding collection, use, access, transfer, and de­, of this framework. It describes the set of: civil rights, civil liberties, and privacy, including freedom of speech, 
voting, and protections from discrimination, excessive punishment, unlawful surveillance, and violations of 
privacy and other freedoms in both public and private sector contexts; equal opportunities, including equitable 
access to education, housing, credit, employment, and other programs; or, access to critical resources or 
services, such as healthcare, financial services, safety, social services, non-deceptive information about goods 
and services, and government benefits. 
10
 
 
 
Applying The Blueprint for an AI Bill of Rights 
SENSITIVE DATA: Data and metadata are sensitive if they pertain to an individual in a sensitive domain 
(defined below); are generated by technologies used in a sensitive domain; can be used to infer data from a 
sensitive domain or sensitive data about an individual (such as disability-related data, genomic data, biometric 
data, behavioral data, geolocation data, data related to interaction with the criminal justice system, relationship 
history and legal status such as custody and divorce information, and home, work, or school environmental 
data); or have the reasonable potential to be used in ways that are likely to expose individuals to meaningful 
harm, such as a loss of privacy or financial harm due to identity theft. Data and metadata generated by or about, Generated explicit or obscene AI content may include highly realistic “deepfakes” of real individuals, 
including children. The spread of this kind of material can have downstream negative consequences: in 
the context of CSAM, even if the generated images do not resemble speciﬁc individuals, the prevalence 
of such images can divert time and resources from eﬀorts to ﬁnd real-world victims. Outside of CSAM, 
the creation and spread of NCII disproportionately impacts women and sexual minorities, and can have 
subsequent negative consequences including decline in overall mental health, substance abuse, and 
even suicidal thoughts.  
Data used for training GAI models may unintentionally include CSAM and NCII. A recent report noted 
that several commonly used GAI training datasets were found to contain hundreds of known images of 
 
12 
CSAM. Even when trained on “clean” data, increasingly capable GAI models can synthesize or produce 
synthetic NCII and CSAM. Websites, mobile apps, and custom-built models that generate synthetic NCII 
have moved from niche internet forums to mainstream, automated, and scaled online businesses.  
Trustworthy AI Characteristics: Fair with Harmful Bias Managed, Safe, Privacy Enhanced 
2.12. 
Value Chain and Component Integration 
GAI value chains involve many third-party components such as procured datasets, pre-trained models,, source_sentence: How might GAI facilitate access to CBRN weapons and relevant knowledge for malicious actors in the future?
sentences: https://doi.org/10.6028/NIST.AI.600-1 
 
July 2024 
 
 
 
 
U.S. Department of Commerce  
Gina M. Raimondo, Secretary 
National Institute of Standards and Technology  
Laurie E. Locascio, NIST Director and Under Secretary of Commerce for Standards and Technology  
 
 
 
 
About AI at NIST: The National Institute of Standards and Technology (NIST) develops measurements, 
technology, tools, and standards to advance reliable, safe, transparent, explainable, privacy-enhanced, 
and fair artiﬁcial intelligence (AI) so that its full commercial and societal beneﬁts can be realized without 
harm to people or the planet. NIST, which has conducted both fundamental and applied work on AI for 
more than a decade, is also helping to fulﬁll the 2023 Executive Order on Safe, Secure, and Trustworthy 
AI. NIST established the U.S. AI Safety Institute and the companion AI Safety Institute Consortium to 
continue the eﬀorts set in motion by the E.O. to build the science necessary for safe, secure, and 
trustworthy development and use of AI. 
Acknowledgments: This report was accomplished with the many helpful comments and contributions, the AI lifecycle; or other issues that diminish transparency or accountability for downstream 
users. 
2.1. CBRN Information or Capabilities 
In the future, GAI may enable malicious actors to more easily access CBRN weapons and/or relevant 
knowledge, information, materials, tools, or technologies that could be misused to assist in the design, 
development, production, or use of CBRN weapons or other dangerous materials or agents. While 
relevant biological and chemical threat knowledge and information is often publicly accessible, LLMs 
could facilitate its analysis or synthesis, particularly by individuals without formal scientiﬁc training or 
expertise.  
Recent research on this topic found that LLM outputs regarding biological threat creation and attack 
planning provided minimal assistance beyond traditional search engine queries, suggesting that state-of-
the-art LLMs at the time these studies were conducted do not substantially increase the operational 
likelihood of such an attack. The physical synthesis development, production, and use of chemical or 
biological agents will continue to require both applicable expertise and supporting materials and 
infrastructure. The impact of GAI on chemical or biological agent misuse will depend on what the key 
barriers for malicious actors are (e.g., whether information access is one such barrier), and how well GAI 
can help actors address those barriers., played a central role in shaping the Blueprint for an AI Bill of Rights. The core messages gleaned from these 
discussions include that AI has transformative potential to improve Americans’ lives, and that preventing the 
harms of these technologies is both necessary and achievable. The Appendix includes a full list of public engage-
ments. 
4
 AI BILL OF RIGHTS
FFECTIVE SYSTEMS
ineffective systems. Automated systems should be 
communities, stakeholders, and domain experts to identify 
Systems should undergo pre-deployment testing, risk 
that demonstrate they are safe and effective based on 
including those beyond the intended use, and adherence to 
protective measures should include the possibility of not 
Automated systems should not be designed with an intent 
reasonably foreseeable possibility of endangering your safety or the safety of your community. They should 
stemming from unintended, yet foreseeable, uses or 
 
 
 
 
  
 
 
SECTION TITLE
BLUEPRINT FOR AN
SAFE AND E 
You should be protected from unsafe or 
developed with consultation from diverse 
concerns, risks, and potential impacts of the system. 
identification and mitigation, and ongoing monitoring 
their intended use, mitigation of unsafe outcomes 
domain-specific standards. Outcomes of these 
deploying the system or removing a system from use. 
or, source_sentence: What are some key lessons learned from technological diffusion in urban planning that could inform the integration of AI technologies in communities?
sentences: State University
•
Carl Holshouser, Senior Vice President for Operations and Strategic Initiatives, TechNet
•
Surya Mattu, Senior Data Engineer and Investigative Data Journalist, The Markup
•
Mariah Montgomery, National Campaign Director, Partnership for Working Families
55
 
 
 
 
APPENDIX
Panelists discussed the benefits of AI-enabled systems and their potential to build better and more 
innovative infrastructure. They individually noted that while AI technologies may be new, the process of 
technological diffusion is not, and that it was critical to have thoughtful and responsible development and 
integration of technology within communities. Some panelists suggested that the integration of technology 
could benefit from examining how technological diffusion has worked in the realm of urban planning: 
lessons learned from successes and failures there include the importance of balancing ownership rights, use 
rights, and community health, safety and welfare, as well ensuring better representation of all voices, 
especially those traditionally marginalized by technological advances. Some panelists also raised the issue of 
power structures – providing examples of how strong transparency requirements in smart city projects 
helped to reshape power and give more voice to those lacking the financial or political power to effect change. 
In discussion of technical and governance interventions that that are needed to protect against the harms, any mechanism that allows the recipient to build the necessary understanding and intuitions to achieve the 
stated purpose. Tailoring should be assessed (e.g., via user experience research). 
Tailored to the target of the explanation. Explanations should be targeted to specific audiences and 
clearly state that audience. An explanation provided to the subject of a decision might differ from one provided 
to an advocate, or to a domain expert or decision maker. Tailoring should be assessed (e.g., via user experience 
research). 
43
 
 
 
 
 
 
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, research on rigorous and reproducible methodologies for developing software systems with legal and regulatory 
compliance in mind. 
Some state legislatures have placed strong transparency and validity requirements on 
the use of pretrial risk assessments. The use of algorithmic pretrial risk assessments has been a 
cause of concern for civil rights groups.28 Idaho Code Section 19-1910, enacted in 2019,29 requires that any 
pretrial risk assessment, before use in the state, first be "shown to be free of bias against any class of 
individuals protected from discrimination by state or federal law", that any locality using a pretrial risk 
assessment must first formally validate the claim of its being free of bias, that "all documents, records, and 
information used to build or validate the risk assessment shall be open to public inspection," and that assertions 
of trade secrets cannot be used "to quash discovery in a criminal matter by a party to a criminal case." 
22
 ­­­­­­­
ALGORITHMIC DISCRIMINATION Protections
You should not face discrimination by algorithms 
and systems should be used and designed in an 
equitable 
way. 
Algorithmic 
discrimination 
occurs when 
automated systems contribute to unjustified different treatment or 
impacts disfavoring people based on their race, color, ethnicity, 
sex
model-index: name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-m
results: task: type: information-retrieval
name: Information Retrieval
dataset: name: Unknown
type: unknown
metrics: type: cosine_accuracy@1
value: 0.75
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.9
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.96
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.97
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.75
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.3
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.19199999999999995
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09699999999999998
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.75
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.9
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.96
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.97
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.8673712763276756
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.8336111111111113
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.8360959595959596
name: Cosine Map@100
verified: no, type: dot_accuracy@1
value: 0.75
name: Dot Accuracy@1
verified: no, type: dot_accuracy@3
value: 0.9
name: Dot Accuracy@3
verified: no, type: dot_accuracy@5
value: 0.96
name: Dot Accuracy@5
verified: no, type: dot_accuracy@10
value: 0.97
name: Dot Accuracy@10
verified: no, type: dot_precision@1
value: 0.75
name: Dot Precision@1
verified: no, type: dot_precision@3
value: 0.3
name: Dot Precision@3
verified: no, type: dot_precision@5
value: 0.19199999999999995
name: Dot Precision@5
verified: no, type: dot_precision@10
value: 0.09699999999999998
name: Dot Precision@10
verified: no, type: dot_recall@1
value: 0.75
name: Dot Recall@1
verified: no, type: dot_recall@3
value: 0.9
name: Dot Recall@3
verified: no, type: dot_recall@5
value: 0.96
name: Dot Recall@5
verified: no, type: dot_recall@10
value: 0.97
name: Dot Recall@10
verified: no, type: dot_ndcg@10
value: 0.8673712763276756
name: Dot Ndcg@10
verified: no, type: dot_mrr@10
value: 0.8336111111111113
name: Dot Mrr@10
verified: no, type: dot_map@100
value: 0.8360959595959596
name: Dot Map@100
verified: no
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Created: 2024-09-21 20:40:52