Model: checkthisout/finetuned_arctic
Author: checkthisout
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:800, 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:800, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
widget: source_sentence: How have algorithms in hiring and credit decisions been shown to impact existing inequities, according to the context?
sentences: Shoshana Zuboff. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of
Power. Public Affairs. 2019.
64. Angela Chen. Why the Future of Life Insurance May Depend on Your Online Presence. The Verge. Feb.
7, 2019.
https://www.theverge.com/2019/2/7/18211890/social-media-life-insurance-new-york-algorithms-big­
data-discrimination-online-records
68, SECTION TITLE­
FOREWORD
Among the great challenges posed to democracy today is the use of technology, data, and automated systems in 
ways that threaten the rights of the American public. Too often, these tools are used to limit our opportunities and 
prevent our access to critical resources or services. These problems are well documented. In America and around 
the world, systems supposed to help with patient care have proven unsafe, ineffective, or biased. Algorithms used 
in hiring and credit decisions have been found to reflect and reproduce existing unwanted inequities or embed 
new harmful bias and discrimination. Unchecked social media data collection has been used to threaten people’s, ways and to the greatest extent possible; where not possible, alternative privacy by design safeguards should be 
used. Systems should not employ user experience and design decisions that obfuscate user choice or burden 
users with defaults that are privacy invasive. Consent should only be used to justify collection of data in cases 
where it can be appropriately and meaningfully given. Any consent requests should be brief, be understandable 
in plain language, and give you agency over data collection and the specific context of use; current hard-to­
understand notice-and-choice practices for broad uses of data should be changed. Enhanced protections and, source_sentence: What factors should be considered when tailoring the extent of explanation provided by a system based on risk level?
sentences: ENDNOTES
96. National Science Foundation. NSF Program on Fairness in Artificial Intelligence in Collaboration
with Amazon (FAI). Accessed July 20, 2022.
https://www.nsf.gov/pubs/2021/nsf21585/nsf21585.htm
97. Kyle Wiggers. Automatic signature verification software threatens to disenfranchise U.S. voters.
VentureBeat. Oct. 25, 2020.
https://venturebeat.com/2020/10/25/automatic-signature-verification-software-threatens-to­
disenfranchise-u-s-voters/
98. Ballotpedia. Cure period for absentee and mail-in ballots. Article retrieved Apr 18, 2022.
https://ballotpedia.org/Cure_period_for_absentee_and_mail-in_ballots
99. Larry Buchanan and Alicia Parlapiano. Two of these Mail Ballot Signatures are by the Same Person., data. “Sensitive domains” are those in which activities being conducted can cause material harms, including signifi­
cant adverse effects on human rights such as autonomy and dignity, as well as civil liberties and civil rights. Domains 
that have historically been singled out as deserving of enhanced data protections or where such enhanced protections 
are reasonably expected by the public include, but are not limited to, health, family planning and care, employment, 
education, criminal justice, and personal finance. In the context of this framework, such domains are considered 
sensitive whether or not the specifics of a system context would necessitate coverage under existing law, and domains, 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, source_sentence: How do the five principles of the Blueprint for an AI Bill of Rights function as backstops against potential harms?
sentences: 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. 
A list of examples of automated systems for which these principles should be considered is provided in the 
Appendix. The Technical Companion, which follows, offers supportive guidance for any person or entity that 
creates, deploys, or oversees automated systems. 
Considered together, the five principles and associated practices of the Blueprint for an AI Bill of 
Rights form an overlapping set of backstops against potential harms. This purposefully overlapping, those laws beyond providing them as examples, where appropriate, of existing protective measures. This 
framework instead shares a broad, forward-leaning vision of recommended principles for automated system 
development and use to inform private and public involvement with these systems where they have the poten­
tial to meaningfully impact rights, opportunities, or access. Additionally, this framework does not analyze or 
take a position on legislative and regulatory proposals in municipal, state, and federal government, or those in 
other countries. 
We have seen modest progress in recent years, with some state and local governments responding to these prob­, 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, source_sentence: What should be documented to justify the use of each data attribute and source in an automated system?
sentences: hand and errors from data entry or other sources should be measured and limited. Any data used as the target 
of a prediction process should receive particular attention to the quality and validity of the predicted outcome 
or label to ensure the goal of the automated system is appropriately identified and measured. Additionally, 
justification should be documented for each data attribute and source to explain why it is appropriate to use 
that data to inform the results of the automated system and why such use will not violate any applicable laws. 
In cases of high-dimensional and/or derived attributes, such justifications can be provided as overall 
descriptions of the attribute generation process and appropriateness. 
19, 13. National Artificial Intelligence Initiative Office. Agency Inventories of AI Use Cases. Accessed Sept. 8,
2022. https://www.ai.gov/ai-use-case-inventories/
14. National Highway Traffic Safety Administration. https://www.nhtsa.gov/
15. See, e.g., Charles Pruitt. People Doing What They Do Best: The Professional Engineers and NHTSA. Public
Administration Review. Vol. 39, No. 4. Jul.-Aug., 1979. https://www.jstor.org/stable/976213?seq=1
16. The US Department of Transportation has publicly described the health and other benefits of these
“traffic calming” measures. See, e.g.: U.S. Department of Transportation. Traffic Calming to Slow Vehicle, target measure; unobservable targets may result in the inappropriate use of proxies. Meeting these 
standards may require instituting mitigation procedures and other protective measures to address 
algorithmic discrimination, avoid meaningful harm, and achieve equity goals. 
Ongoing monitoring and mitigation. Automated systems should be regularly monitored to assess algo­
rithmic discrimination that might arise from unforeseen interactions of the system with inequities not 
accounted for during the pre-deployment testing, changes to the system after deployment, or changes to the 
context of use or associated data. Monitoring and disparity assessment should be performed by the entity, source_sentence: What are the implications of surveillance technologies on the rights and opportunities of underserved communities?
sentences: manage risks associated with activities or business processes common across sectors, such as the use of 
large language models (LLMs), cloud-based services, or acquisition. 
This document deﬁnes risks that are novel to or exacerbated by the use of GAI. After introducing and 
describing these risks, the document provides a set of suggested actions to help organizations govern, 
map, measure, and manage these risks. 
 
 
1 EO 14110 deﬁnes Generative AI as “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, 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 
of these technologies, various panelists emphasized the need for transparency, data collection, and 
flexible and reactive policy development, analogous to how software is continuously updated and deployed., limits its focus to both government and commercial use of surveillance technologies when juxtaposed with 
real-time or subsequent automated analysis and when such systems have a potential for meaningful impact 
on individuals’ or communities’ rights, opportunities, or access. 
UNDERSERVED COMMUNITIES: The term “underserved communities” refers to communities that have 
been systematically denied a full opportunity to participate in aspects of economic, social, and civic life, as 
exemplified by the list in the preceding definition of “equity.” 
11
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: 0.805
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.925
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.965
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.97
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.805
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.30833333333333335
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.193
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09699999999999999
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.805
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.925
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.965
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.97
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.8920929944400894
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.8662916666666668
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.8680077838827839
name: Cosine Map@100
verified: no, type: dot_accuracy@1
value: 0.805
name: Dot Accuracy@1
verified: no, type: dot_accuracy@3
value: 0.925
name: Dot Accuracy@3
verified: no, type: dot_accuracy@5
value: 0.965
name: Dot Accuracy@5
verified: no, type: dot_accuracy@10
value: 0.97
name: Dot Accuracy@10
verified: no, type: dot_precision@1
value: 0.805
name: Dot Precision@1
verified: no, type: dot_precision@3
value: 0.30833333333333335
name: Dot Precision@3
verified: no, type: dot_precision@5
value: 0.193
name: Dot Precision@5
verified: no, type: dot_precision@10
value: 0.09699999999999999
name: Dot Precision@10
verified: no, type: dot_recall@1
value: 0.805
name: Dot Recall@1
verified: no, type: dot_recall@3
value: 0.925
name: Dot Recall@3
verified: no, type: dot_recall@5
value: 0.965
name: Dot Recall@5
verified: no, type: dot_recall@10
value: 0.97
name: Dot Recall@10
verified: no, type: dot_ndcg@10
value: 0.8920929944400894
name: Dot Ndcg@10
verified: no, type: dot_mrr@10
value: 0.8662916666666668
name: Dot Mrr@10
verified: no, type: dot_map@100
value: 0.8680077838827839
name: Dot Map@100
verified: no
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Created: 2024-09-24 20:29:28