Model: JoeNoss1998/Noss
Author: JoeNoss1998
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 can bias testing influence the design and launch of automated systems?
sentences: reinforce those legal protections but extend beyond them to ensure equity for underserved communities48 
even in circumstances where a specific legal protection may not be clearly established. These protections 
should be instituted throughout the design, development, and deployment process and are described below 
roughly in the order in which they would be instituted. 
Protect the public from algorithmic discrimination in a proactive and ongoing manner 
Proactive assessment of equity in design. Those responsible for the development, use, or oversight of, the severity of certain diseases in Black Americans. Instances of discriminatory practices built into and 
resulting from AI and other automated systems exist across many industries, areas, and contexts. While automated 
systems have the capacity to drive extraordinary advances and innovations, algorithmic discrimination 
protections should be built into their design, deployment, and ongoing use. 
Many companies, non-profits, and federal government agencies are already taking steps to ensure the public 
is protected from algorithmic discrimination. Some companies have instituted bias testing as part of their product 
quality assessment and launch procedures, and in some cases this testing has led products to be changed or not, accuracy), and enable human users to understand, appropriately trust, and effectively manage the emerging 
generation of artificially intelligent partners.95 The National Science Foundation’s program on Fairness in 
Artificial Intelligence also includes a specific interest in research foundations for explainable AI.96
45, source_sentence: What is the intended use of the systems mentioned in the context?
sentences: In discussion of technical and governance interventions that that are needed to protect against the harms of these technologies, panelists individually described the importance of: receiving community input into the design and use of technologies, public reporting on crucial elements of these systems, better notice and consent procedures that ensure privacy based on context and use case, ability to opt-out of using these systems and receive a fallback to a human process, providing explanations of decisions and how these systems work, the need for governance including training in using these systems, ensuring the technological use cases are genuinely related to the goal task and are locally validated to work, and the need for institution, part of its loan underwriting and pricing model was found to be much more likely to charge an applicant whoattended a Historically Black College or University (HBCU) higher loan prices for refinancing a student loanthan an applicant who did not attend an HBCU. This was found to be true even when controlling for
other credit-related factors.32
•A hiring tool that learned the features of a company's employees (predominantly men) rejected women appli -
cants for spurious and discriminatory reasons; resumes with the word “women’s,” such as “women’s
chess club captain,” were penalized in the candidate ranking.33, systems with an intended use within sensi, source_sentence: How did the hospital's software error affect the patient's access to pain medication?
sentences: 101
•A fraud detection system for unemployment insurance distribution incorrectly flagged entries as fraudulent,leading to people with slight discrepancies or complexities in their files having their wages withheld and taxreturns seized without any chance to explain themselves or receive a review by a person.
102
•A patient was wrongly denied access to pain medication when the hospital’s software confused her medica -
tion history with that of her dog’s. Even after she tracked down an explanation for the problem, doctorswere afraid to override the system, and she was forced to go without pain relief due to the system’s error.
103, This section provides a brief summary of the problems that the principle seeks to address and protect against, including illustrative examples. 
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 should be tailored for particular sectors and contexts.
•This section outlines practical steps that can be implemented to realize the vision of the Blueprint for an AI Bill of Rights. The, 97 A human
curing process,98 which helps voters to confirm their signatures and correct other voting mistakes, is
important to ensure all votes are counted,99 and it is already standard practice in much of the country for
both an election official and the voter to have the opportunity to review and correct any such issues.100 
47, source_sentence: Which organizations and individuals submitted the documents mentioned in the context?
sentences: 114 and were submitted by the below
listed organizations and individuals:
Accenture 
Access Now ACT | The App Association AHIP 
AIethicist.org, 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 p anelists 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,, 26Algorithmic 
Discrimination 
Protections, source_sentence: What types of risks should be identified and mitigated before the deployment of an automated system?
sentences: APPENDIX
Systems  that impact the safety of communities such as automated traffic control systems, elec 
-ctrical grid controls, smart city technologies, and industrial emissions and environmental
impact control algorithms; and
Systems  related to access to benefits or services or assignment of penalties such as systems that, 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, 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
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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: 0.8
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.925
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.94
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.98
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.8
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.30833333333333335
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.18799999999999997
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09799999999999999
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.8
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.925
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.94
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.98
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.8955920586775068
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.868345238095238
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.8695985052884031
name: Cosine Map@100
verified: no, type: dot_accuracy@1
value: 0.8
name: Dot Accuracy@1
verified: no, type: dot_accuracy@3
value: 0.925
name: Dot Accuracy@3
verified: no, type: dot_accuracy@5
value: 0.94
name: Dot Accuracy@5
verified: no, type: dot_accuracy@10
value: 0.98
name: Dot Accuracy@10
verified: no, type: dot_precision@1
value: 0.8
name: Dot Precision@1
verified: no, type: dot_precision@3
value: 0.30833333333333335
name: Dot Precision@3
verified: no, type: dot_precision@5
value: 0.18799999999999997
name: Dot Precision@5
verified: no, type: dot_precision@10
value: 0.09799999999999999
name: Dot Precision@10
verified: no, type: dot_recall@1
value: 0.8
name: Dot Recall@1
verified: no, type: dot_recall@3
value: 0.925
name: Dot Recall@3
verified: no, type: dot_recall@5
value: 0.94
name: Dot Recall@5
verified: no, type: dot_recall@10
value: 0.98
name: Dot Recall@10
verified: no, type: dot_ndcg@10
value: 0.8955920586775068
name: Dot Ndcg@10
verified: no, type: dot_mrr@10
value: 0.868345238095238
name: Dot Mrr@10
verified: no, type: dot_map@100
value: 0.8695985052884031
name: Dot Map@100
verified: no
Likes: 0 | All-time downloads: 9
Created: 2024-09-27 00:51:20