Model: ldldld/snowflake-arctic-embed-m-finetuned
Author: ldldld
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:600, 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:600, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
widget: source_sentence: What is the purpose of the Artificial Intelligence Ethics for the Intelligence Community as mentioned in the context?
sentences: 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 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, points to numerous examples of effective and proactive stakeholder engagement, including the Community-
Based Participatory Research Program developed by the National Institutes of Health and the participatory 
technology assessments developed by the National Oceanic and Atmospheric Administration.18
The National Institute of Standards and Technology (NIST) is developing a risk 
management framework to better manage risks posed to individuals, organizations, and 
society by AI.19 The NIST AI Risk Management Framework, as mandated by Congress, is intended for 
voluntary use to help incorporate trustworthiness considerations into the design, development, use, and 
evaluation of AI products, services, and systems. The NIST framework is being developed through a consensus-
driven, open, transparent, and collaborative process that includes workshops and other opportunities to provide 
input. The NIST framework aims to foster the development of innovative approaches to address, of Artificial Intelligence Ethics for the Intelligence Community to guide personnel on whether and how to 
develop and use AI in furtherance of the IC's mission, as well as an AI Ethics Framework to help implement 
these principles.22
The National Science Foundation (NSF) funds extensive research to help foster the 
development of automated systems that adhere to and advance their safety, security and 
effectiveness. Multiple NSF programs support research that directly addresses many of these principles: 
the National AI Research Institutes23 support research on all aspects of safe, trustworthy, fair, and explainable 
AI algorithms and systems; the Cyber Physical Systems24 program supports research on developing safe 
autonomous and cyber physical systems with AI components; the Secure and Trustworthy Cyberspace25 
program supports research on cybersecurity and privacy enhancing technologies in automated systems; the, source_sentence: How does the Department of Defense's approach to AI ethics differ from that of the Department of Energy?
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., SAFE AND EFFECTIVE 
SYSTEMS 
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. ­
Some U.S government agencies have developed specific frameworks for ethical use of AI 
systems. The Department of Energy (DOE) has activated the AI Advancement Council that oversees coordina-
tion and advises on implementation of the DOE AI Strategy and addresses issues and/or escalations on the 
ethical use and development of AI systems.20 The Department of Defense has adopted Artificial Intelligence 
Ethical Principles, and tenets for Responsible Artificial Intelligence specifically tailored to its national 
security and defense activities.21 Similarly, the U.S. Intelligence Community (IC) has developed the Principles, Formal Methods in the Field26 program supports research on rigorous formal verification and analysis of 
automated systems and machine learning, and the Designing Accountable Software Systems27 program supports 
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, source_sentence: What are the expectations for automated systems intended to serve as a blueprint for?
sentences: help to mitigate biases and potential harms. 
Guarding against proxies.  Directly using demographic information in the design, development, or 
deployment of an automated system (for purposes other than evaluating a system for discrimination or using 
a system to counter discrimination) runs a high risk of leading to algorithmic discrimination and should be 
avoided. In many cases, attributes that are highly correlated with demographic features, known as proxies, can 
contribute to algorithmic discrimination. In cases where use of the demographic features themselves would 
lead to illegal algorithmic discrimination, reliance on such proxies in decision-making (such as that facilitated 
by an algorithm) may also be prohibited by law. Proactive testing should be performed to identify proxies by 
testing for correlation between demographic information and attributes in any data used as part of system, 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, SAFE AND EFFECTIVE 
SYSTEMS 
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. 
Derived data sources tracked and reviewed carefully. Data that is derived from other data through 
the use of algorithms, such as data derived or inferred from prior model outputs, should be identified and 
tracked, e.g., via a specialized type in a data schema. Derived data should be viewed as potentially high-risk 
inputs that may lead to feedback loops, compounded harm, or inaccurate results. Such sources should be care­
fully validated against the risk of collateral consequences. 
Data reuse limits in sensitive domains. Data reuse, and especially data reuse in a new context, can result 
in the spreading and scaling of harms. Data from some domains, including criminal justice data and data indi­, source_sentence: What should individuals have access to regarding their data decisions and the impact of surveillance technologies?
sentences: •
Searches for “Black girls,” “Asian girls,” or “Latina girls” return predominantly39 sexualized content, rather
than role models, toys, or activities.40 Some search engines have been working to reduce the prevalence of
these results, but the problem remains.41
•
Advertisement delivery systems that predict who is most likely to click on a job advertisement end up deliv-
ering ads in ways that reinforce racial and gender stereotypes, such as overwhelmingly directing supermar-
ket cashier ads to women and jobs with taxi companies to primarily Black people.42­
•
Body scanners, used by TSA at airport checkpoints, require the operator to select a “male” or “female”
scanning setting based on the passenger’s sex, but the setting is chosen based on the operator’s perception of
the passenger’s gender identity. These scanners are more likely to flag transgender travelers as requiring
extra screening done by a person. Transgender travelers have described degrading experiences associated, 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, tect privacy and civil liberties. Continuous surveillance and monitoring 
should not be used in education, work, housing, or in other contexts where the 
use of such surveillance technologies is likely to limit rights, opportunities, or 
access. Whenever possible, you should have access to reporting that confirms 
your data decisions have been respected and provides an assessment of the 
potential impact of surveillance technologies on your rights, opportunities, or 
access. 
DATA PRIVACY
30, source_sentence: What are the implications of the digital divide highlighted in Andrew Kenney's article regarding unemployment benefits?
sentences: cating adverse outcomes in domains such as finance, employment, and housing, is especially sensitive, and in 
some cases its reuse is limited by law. Accordingly, such data should be subject to extra oversight to ensure 
safety and efficacy. Data reuse of sensitive domain data in other contexts (e.g., criminal data reuse for civil legal 
matters or private sector use) should only occur where use of such data is legally authorized and, after examina­
tion, has benefits for those impacted by the system that outweigh identified risks and, as appropriate, reason­
able measures have been implemented to mitigate the identified risks. Such data should be clearly labeled to 
identify contexts for limited reuse based on sensitivity. Where possible, aggregated datasets may be useful for 
replacing individual-level sensitive data. 
Demonstrate the safety and effectiveness of the system 
Independent evaluation. Automated systems should be designed to allow for independent evaluation (e.g.,, 5. Environmental Impacts: Impacts due to high compute resource utilization in training or 
operating GAI models, and related outcomes that may adversely impact ecosystems.  
6. Harmful Bias or Homogenization: Ampliﬁcation and exacerbation of historical, societal, and 
systemic biases; performance disparities8 between sub-groups or languages, possibly due to 
non-representative training data, that result in discrimination, ampliﬁcation of biases, or 
incorrect presumptions about performance; undesired homogeneity that skews system or model 
outputs, which may be erroneous, lead to ill-founded decision-making, or amplify harmful 
biases.  
7. Human-AI Conﬁguration: Arrangements of or interactions between a human and an AI system 
which can result in the human inappropriately anthropomorphizing GAI systems or experiencing 
algorithmic aversion, automation bias, over-reliance, or emotional entanglement with GAI 
systems., https://bipartisanpolicy.org/blog/the-low-down-on-ballot-curing/
101. Andrew Kenney. 'I'm shocked that they need to have a smartphone': System for unemployment
benefits exposes digital divide. USA Today. May 2, 2021.
https://www.usatoday.com/story/tech/news/2021/05/02/unemployment-benefits-system-leaving­
people-behind/4915248001/
102. Allie Gross. UIA lawsuit shows how the state criminalizes the unemployed. Detroit Metro-Times.
Sep. 18, 2015.
https://www.metrotimes.com/news/uia-lawsuit-shows-how-the-state-criminalizes-the­
unemployed-2369412
103. Maia Szalavitz. The Pain Was Unbearable. So Why Did Doctors Turn Her Away? Wired. Aug. 11,
2021. https://www.wired.com/story/opioid-drug-addiction-algorithm-chronic-pain/
104. Spencer Soper. Fired by Bot at Amazon: "It's You Against the Machine". Bloomberg, Jun. 28, 2021.
https://www.bloomberg.com/news/features/2021-06-28/fired-by-bot-amazon-turns-to-machine­
managers-and-workers-are-losing-out
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: 0.73
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.9
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.935
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.96
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.73
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.3
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.187
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.096
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.73
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.9
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.935
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.96
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.8511693160760204
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.8155396825396827
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.8172228277187864
name: Cosine Map@100
verified: no, type: dot_accuracy@1
value: 0.73
name: Dot Accuracy@1
verified: no, type: dot_accuracy@3
value: 0.9
name: Dot Accuracy@3
verified: no, type: dot_accuracy@5
value: 0.935
name: Dot Accuracy@5
verified: no, type: dot_accuracy@10
value: 0.96
name: Dot Accuracy@10
verified: no, type: dot_precision@1
value: 0.73
name: Dot Precision@1
verified: no, type: dot_precision@3
value: 0.3
name: Dot Precision@3
verified: no, type: dot_precision@5
value: 0.187
name: Dot Precision@5
verified: no, type: dot_precision@10
value: 0.096
name: Dot Precision@10
verified: no, type: dot_recall@1
value: 0.73
name: Dot Recall@1
verified: no, type: dot_recall@3
value: 0.9
name: Dot Recall@3
verified: no, type: dot_recall@5
value: 0.935
name: Dot Recall@5
verified: no, type: dot_recall@10
value: 0.96
name: Dot Recall@10
verified: no, type: dot_ndcg@10
value: 0.8511693160760204
name: Dot Ndcg@10
verified: no, type: dot_mrr@10
value: 0.8155396825396827
name: Dot Mrr@10
verified: no, type: dot_map@100
value: 0.8172228277187864
name: Dot Map@100
verified: no
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Created: 2024-09-25 00:08:50