Model: jeevanions/finetuned_arctic-embedd-l
Author: jeevanions
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:3430, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss, arxiv:1908.10084, arxiv:2205.13147, arxiv:1705.00652, base_model:Snowflake/snowflake-arctic-embed-l, base_model:finetune:Snowflake/snowflake-arctic-embed-l, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: Snowflake/snowflake-arctic-embed-l
Card metadata: base_model: Snowflake/snowflake-arctic-embed-l
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:3430, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
widget: source_sentence: What are some illustrative cases that show the implementation of the AI Bill of Rights?
sentences: SECTION TITLE
APPENDIX
Listening to the American People 
The White House Office of Science and Technology Policy (OSTP) led a yearlong process to seek and distill 
input from people across the country – from impacted communities to industry stakeholders to 
technology developers to other experts across fields and sectors, as well as policymakers across the Federal 
government – on the issue of algorithmic and data-driven harms and potential remedies. Through panel 
discussions, public listening sessions, private meetings, a formal request for information, and input to a 
publicly accessible and widely-publicized email address, people across the United States spoke up about 
both the promises and potential harms of these technologies, and played a central role in shaping the 
Blueprint for an AI Bill of Rights. 
Panel Discussions to Inform the Blueprint for An AI Bill of Rights 
OSTP co-hosted a series of six panel discussions in collaboration with the Center for American Progress,, existing human performance considered as a performance baseline for the algorithm to meet pre-deployment, 
and as a lifecycle minimum performance standard. Decision possibilities resulting from performance testing 
should include the possibility of not deploying the system. 
Risk identification and mitigation. Before deployment, and in a proactive and ongoing manner, poten­
tial risks of the automated system should be identified and mitigated. Identified risks should focus on the 
potential for meaningful impact on people’s rights, opportunities, or access and include those to impacted 
communities that may not be direct users of the automated system, risks resulting from purposeful misuse of 
the system, and other concerns identified via the consultation process. Assessment and, where possible, mea­
surement of the impact of risks should be included and balanced such that high impact risks receive attention, confidence that their rights, opportunities, and access as well as their expectations about technologies are respected. 
3
HOW THESE PRINCIPLES CAN MOVE INTO PRACTICE: 
This section provides real-life examples of how these guiding principles can become reality, through laws, policies, and practices. 
It describes practical technical and sociotechnical approaches to protecting rights, opportunities, and access. 
The examples provided are not critiques or endorsements, but rather are offered as illustrative cases to help 
provide a concrete vision for actualizing the Blueprint for an AI Bill of Rights. Effectively implementing these 
processes require the cooperation of and collaboration among industry, civil society, researchers, policymakers, 
technologists, and the public. 
14, source_sentence: What are the potential impacts of automated systems on data privacy?
sentences: https://arxiv.org/pdf/2305.17493v2 
Smith, A. et al. (2023) Hallucination or Confabulation? Neuroanatomy as metaphor in Large Language 
Models. PLOS Digital Health. 
https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000388 
Soice, E. et al. (2023) Can large language models democratize access to dual-use biotechnology? arXiv. 
https://arxiv.org/abs/2306.03809 
Solaiman, I. et al. (2023) The Gradient of Generative AI Release: Methods and Considerations. arXiv. 
https://arxiv.org/abs/2302.04844 
Staab, R. et al. (2023) Beyond Memorization: Violating Privacy via Inference With Large Language 
Models. arXiv. https://arxiv.org/pdf/2310.07298 
Stanford, S. et al. (2023) Whose Opinions Do Language Models Reﬂect? arXiv. 
https://arxiv.org/pdf/2303.17548 
Strubell, E. et al. (2019) Energy and Policy Considerations for Deep Learning in NLP. arXiv. 
https://arxiv.org/pdf/1906.02243 
The White House (2016) Circular No. A-130, Managing Information as a Strategic Resource., and data that are considered sensitive are understood to change over time based on societal norms and context. 
36, yet foreseeable, uses or impacts of automated systems. You should be 
protected from inappropriate or irrelevant data use in the design, de­
velopment, and deployment of automated systems, and from the 
compounded harm of its reuse. Independent evaluation and report­
ing that confirms that the system is safe and effective, including re­
porting of steps taken to mitigate potential harms, should be per­
formed and the results made public whenever possible. 
15, source_sentence: What is the AI Bill of Rights?
sentences: BLUEPRINT FOR AN 
AI BILL OF 
RIGHTS 
MAKING AUTOMATED 
SYSTEMS WORK FOR 
THE AMERICAN PEOPLE 
OCTOBER 2022, APPENDIX
•
Julia Simon-Mishel, Supervising Attorney, Philadelphia Legal Assistance
•
Dr. Zachary Mahafza, Research & Data Analyst, Southern Poverty Law Center
•
J. Khadijah Abdurahman, Tech Impact Network Research Fellow, AI Now Institute, UCLA C2I1, and
UWA Law School
Panelists separately described the increasing scope of technology use in providing for social welfare, including 
in fraud detection, digital ID systems, and other methods focused on improving efficiency and reducing cost. 
However, various panelists individually cautioned that these systems may reduce burden for government 
agencies by increasing the burden and agency of people using and interacting with these technologies. 
Additionally, these systems can produce feedback loops and compounded harm, collecting data from 
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, safe, secure, and resilient; (e) understandable; (f ) responsible and traceable; (g) regularly monitored; (h) transpar-
ent; and, (i) accountable. The Blueprint for an AI Bill of Rights is consistent with the Executive Order. 
Affected agencies across the federal government have released AI use case inventories13 and are implementing 
plans to bring those AI systems into compliance with the Executive Order or retire them. 
The law and policy landscape for motor vehicles shows that strong safety regulations—and 
measures to address harms when they occur—can enhance innovation in the context of com-
plex technologies. Cars, like automated digital systems, comprise a complex collection of components. 
The National Highway Traffic Safety Administration,14 through its rigorous standards and independent 
evaluation, helps make sure vehicles on our roads are safe without limiting manufacturers’ ability to 
innovate.15 At the same time, rules of the road are implemented locally to impose contextually appropriate, source_sentence: What are the best practices for benchmarking AI system security and resilience?
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. 
An automated system should provide demonstrably clear, timely, understandable, and accessible notice of use, and 
explanations as to how and why a decision was made or an action was taken by the system. These expectations are 
explained below. 
Provide clear, timely, understandable, and accessible notice of use and explanations ­
Generally accessible plain language documentation. The entity responsible for using the automated 
system should ensure that documentation describing the overall system (including any human components) is 
public and easy to find. The documentation should describe, in plain language, how the system works and how, content performance and impact, and work in collaboration with AI Actors 
experienced in user research and experience. 
Human-AI Conﬁguration 
MG-4.1-004 Implement active learning techniques to identify instances where the model fails 
or produces unexpected outputs. 
Confabulation 
MG-4.1-005 
Share transparency reports with internal and external stakeholders that detail 
steps taken to update the GAI system to enhance transparency and 
accountability. 
Human-AI Conﬁguration; Harmful 
Bias and Homogenization 
MG-4.1-006 
Track dataset modiﬁcations for provenance by monitoring data deletions, 
rectiﬁcation requests, and other changes that may impact the veriﬁability of 
content origins. 
Information Integrity, 33 
MEASURE 2.7: AI system security and resilience – as identiﬁed in the MAP function – are evaluated and documented. 
Action ID 
Suggested Action 
GAI Risks 
MS-2.7-001 
Apply established security measures to: Assess likelihood and magnitude of 
vulnerabilities and threats such as backdoors, compromised dependencies, data 
breaches, eavesdropping, man-in-the-middle attacks, reverse engineering, 
autonomous agents, model theft or exposure of model weights, AI inference, 
bypass, extraction, and other baseline security concerns. 
Data Privacy; Information Integrity; 
Information Security; Value Chain 
and Component Integration 
MS-2.7-002 
Benchmark GAI system security and resilience related to content provenance 
against industry standards and best practices. Compare GAI system security 
features and content provenance methods against industry state-of-the-art. 
Information Integrity; Information 
Security 
MS-2.7-003 
Conduct user surveys to gather user satisfaction with the AI-generated content, source_sentence: How should risks or trustworthiness characteristics that cannot be measured be documented?
sentences: MEASURE 1.1: Approaches and metrics for measurement of AI risks enumerated during the MAP function are selected for 
implementation starting with the most signiﬁcant AI risks. The risks or trustworthiness characteristics that will not – or cannot – be 
measured are properly documented. 
Action ID 
Suggested Action 
GAI Risks 
MS-1.1-001 Employ methods to trace the origin and modiﬁcations of digital content. 
Information Integrity 
MS-1.1-002 
Integrate tools designed to analyze content provenance and detect data 
anomalies, verify the authenticity of digital signatures, and identify patterns 
associated with misinformation or manipulation. 
Information Integrity 
MS-1.1-003 
Disaggregate evaluation metrics by demographic factors to identify any 
discrepancies in how content provenance mechanisms work across diverse 
populations. 
Information Integrity; Harmful 
Bias and Homogenization 
MS-1.1-004 Develop a suite of metrics to evaluate structured public feedback exercises, AI technology can produce varied outputs in multiple modalities and present many classes of user 
interfaces. This leads to a broader set of AI Actors interacting with GAI systems for widely diﬀering 
applications and contexts of use. These can include data labeling and preparation, development of GAI 
models, content moderation, code generation and review, text generation and editing, image and video 
generation, summarization, search, and chat. These activities can take place within organizational 
settings or in the public domain. 
Organizations can restrict AI applications that cause harm, exceed stated risk tolerances, or that conﬂict 
with their tolerances or values. Governance tools and protocols that are applied to other types of AI 
systems can be applied to GAI systems. These plans and actions include: 
• Accessibility and reasonable 
accommodations 
• AI actor credentials and qualiﬁcations  
• Alignment to organizational values 
• Auditing and assessment 
• Change-management controls, existing human performance considered as a performance baseline for the algorithm to meet pre-deployment, 
and as a lifecycle minimum performance standard. Decision possibilities resulting from performance testing 
should include the possibility of not deploying the system. 
Risk identification and mitigation. Before deployment, and in a proactive and ongoing manner, poten­
tial risks of the automated system should be identified and mitigated. Identified risks should focus on the 
potential for meaningful impact on people’s rights, opportunities, or access and include those to impacted 
communities that may not be direct users of the automated system, risks resulting from purposeful misuse of 
the system, and other concerns identified via the consultation process. Assessment and, where possible, mea­
surement of the impact of risks should be included and balanced such that high impact risks receive attention
model-index: name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
results: task: type: information-retrieval
name: Information Retrieval
dataset: name: Unknown
type: unknown
metrics: type: cosine_accuracy@1
value: 0.2807017543859649
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.4649122807017544
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.5350877192982456
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.7192982456140351
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.2807017543859649
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.15497076023391812
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.10701754385964912
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.0719298245614035
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.2807017543859649
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.4649122807017544
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.5350877192982456
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.7192982456140351
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.4797086283187805
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.40644667223614606
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.423567506926962
name: Cosine Map@100
verified: no, type: dot_accuracy@1
value: 0.2807017543859649
name: Dot Accuracy@1
verified: no, type: dot_accuracy@3
value: 0.4649122807017544
name: Dot Accuracy@3
verified: no, type: dot_accuracy@5
value: 0.5350877192982456
name: Dot Accuracy@5
verified: no, type: dot_accuracy@10
value: 0.7192982456140351
name: Dot Accuracy@10
verified: no, type: dot_precision@1
value: 0.2807017543859649
name: Dot Precision@1
verified: no, type: dot_precision@3
value: 0.15497076023391812
name: Dot Precision@3
verified: no, type: dot_precision@5
value: 0.10701754385964912
name: Dot Precision@5
verified: no, type: dot_precision@10
value: 0.0719298245614035
name: Dot Precision@10
verified: no, type: dot_recall@1
value: 0.2807017543859649
name: Dot Recall@1
verified: no, type: dot_recall@3
value: 0.4649122807017544
name: Dot Recall@3
verified: no, type: dot_recall@5
value: 0.5350877192982456
name: Dot Recall@5
verified: no, type: dot_recall@10
value: 0.7192982456140351
name: Dot Recall@10
verified: no, type: dot_ndcg@10
value: 0.4797086283187805
name: Dot Ndcg@10
verified: no, type: dot_mrr@10
value: 0.40644667223614606
name: Dot Mrr@10
verified: no, type: dot_map@100
value: 0.423567506926962
name: Dot Map@100
verified: no
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Created: 2024-09-24 02:07:27