Model: acpotts/finetuned_arctic
Author: acpotts
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: What is the importance of having a human fallback system in automated systems, especially for the American public?
sentences: ing a system from use. Automated systems should not be designed 
with an intent or reasonably foreseeable possibility of endangering 
your safety or the safety of your community. They should be designed 
to proactively protect you from harms stemming from unintended, 
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, with disabilities. 
In addition to being able to opt out and use a human alternative, the American public deserves a human fallback 
system in the event that an automated system fails or causes harm. No matter how rigorously an automated system is 
tested, there will always be situations for which the system fails. The American public deserves protection via human 
review against these outlying or unexpected scenarios. In the case of time-critical systems, the public should not have 
to wait—immediate human consideration and fallback should be available. In many time-critical systems, such a 
remedy is already immediately available, such as a building manager who can open a door in the case an automated 
card access system fails., 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, source_sentence: What type of information is required to be open to public inspection in relation to risk assessment?
sentences: 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. 
The federal government is working to combat discrimination in mortgage lending. The Depart­
ment of Justice has launched a nationwide initiative to combat redlining, which includes reviewing how 
lenders who may be avoiding serving communities of color are conducting targeted marketing and advertising.51 
This initiative will draw upon strong partnerships across federal agencies, including the Consumer Financial, reuse 
Relevant and high-quality data. Data used as part of any automated system’s creation, evaluation, or 
deployment should be relevant, of high quality, and tailored to the task at hand. Relevancy should be 
established based on research-backed demonstration of the causal influence of the data to the specific use case 
or justified more generally based on a reasonable expectation of usefulness in the domain and/or for the 
system design or ongoing development. Relevance of data should not be established solely by appealing to 
its historical connection to the outcome. High quality and tailored data should be representative of the task at, 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, source_sentence: Who is the Senior Policy Advisor for Data and Democracy at the White House Office of Science and Technology Policy?
sentences: products, advanced platforms and services, “Internet of Things” (IoT) devices, and smart city products and 
services. 
Welcome:
•
Rashida Richardson, Senior Policy Advisor for Data and Democracy, White House Office of Science and
Technology Policy
•
Karen Kornbluh, Senior Fellow and Director of the Digital Innovation and Democracy Initiative, German
Marshall Fund
Moderator: 
Devin E. Willis, Attorney, Division of Privacy and Identity Protection, Bureau of Consumer Protection, Federal 
Trade Commission 
Panelists: 
•
Tamika L. Butler, Principal, Tamika L. Butler Consulting
•
Jennifer Clark, Professor and Head of City and Regional Planning, Knowlton School of Engineering, Ohio
State University
•, ENDNOTES
35. Carrie Johnson. Flaws plague a tool meant to help low-risk federal prisoners win early release. NPR.
Jan. 26, 2022. https://www.npr.org/2022/01/26/1075509175/flaws-plague-a-tool-meant-to-help-low­
risk-federal-prisoners-win-early-release.; Carrie Johnson. Justice Department works to curb racial bias
in deciding who's released from prison. NPR. Apr. 19, 2022. https://
www.npr.org/2022/04/19/1093538706/justice-department-works-to-curb-racial-bias-in-deciding­
whos-released-from-pris; National Institute of Justice. 2021 Review and Revalidation of the First Step Act
Risk Assessment Tool. National Institute of Justice NCJ 303859. Dec., 2021. https://www.ojp.gov/
pdffiles1/nij/303859.pdf, https://themarkup.org/machine-learning/2022/01/11/this-private-equity-firm-is-amassing-companies­
that-collect-data-on-americas-children
77. Reed Albergotti. Every employee who leaves Apple becomes an ‘associate’: In job databases used by
employers to verify resume information, every former Apple employee’s title gets erased and replaced with
a generic title. The Washington Post. Feb. 10, 2022.
https://www.washingtonpost.com/technology/2022/02/10/apple-associate/
78. National Institute of Standards and Technology. Privacy Framework Perspectives and Success
Stories. Accessed May 2, 2022.
https://www.nist.gov/privacy-framework/getting-started-0/perspectives-and-success-stories, source_sentence: What actions has the Consumer Financial Protection Bureau taken regarding black-box credit models?
sentences: under-ecoa-fcra/
91. Federal Trade Commission. Using Consumer Reports for Credit Decisions: What to Know About
Adverse Action and Risk-Based Pricing Notices. Accessed May 2, 2022.
https://www.ftc.gov/business-guidance/resources/using-consumer-reports-credit-decisions-what­
know-about-adverse-action-risk-based-pricing-notices#risk
92. Consumer Financial Protection Bureau. CFPB Acts to Protect the Public from Black-Box Credit
Models Using Complex Algorithms. May 26, 2022.
https://www.consumerfinance.gov/about-us/newsroom/cfpb-acts-to-protect-the-public-from-black­
box-credit-models-using-complex-algorithms/
93. Anthony Zaller. California Passes Law Regulating Quotas In Warehouses – What Employers Need to, https://www.nytimes.com/2020/12/29/technology/facial-recognition-misidentify-jail.html; Khari
Johnson. How Wrongful Arrests Based on AI Derailed 3 Men's Lives. Wired. Mar. 7, 2022. https://
www.wired.com/story/wrongful-arrests-ai-derailed-3-mens-lives/
32. Student Borrower Protection Center. Educational Redlining. Student Borrower Protection Center
Report. Feb. 2020. https://protectborrowers.org/wp-content/uploads/2020/02/Education-Redlining­
Report.pdf
33. Jeffrey Dastin. Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. Oct.
10, 2018. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps­
secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G, including automated tenant background screening and facial recognition-based controls to enter or exit 
housing complexes. Employment-related concerning uses included discrimination in automated hiring 
screening and workplace surveillance. Various panelists raised the limitations of existing privacy law as a key 
concern, pointing out that students should be able to reinvent themselves and require privacy of their student 
records and education-related data in order to do so. The overarching concerns of surveillance in these 
domains included concerns about the chilling effects of surveillance on student expression, inappropriate, source_sentence: What percentage of racy results did Google cut for searches like 'Latina teenager' in March 2022?
sentences: they've used drugs, or whether they've expressed interest in LGBTQI+ groups, and then use that data to 
forecast student success.76 Parents and education experts have expressed concern about collection of such
sensitive data without express parental consent, the lack of transparency in how such data is being used, and
the potential for resulting discriminatory impacts.
• Many employers transfer employee data to third party job verification services. This information is then used
by potential future employers, banks, or landlords. In one case, a former employee alleged that a
company supplied false data about her job title which resulted in a job offer being revoked.77
37, Software Discriminates Against Disabled Students. Center for Democracy and Technology. Nov. 16, 2020.
https://cdt.org/insights/how-automated-test-proctoring-software-discriminates-against-disabled­
students/
46. Ziad Obermeyer, et al., Dissecting racial bias in an algorithm used to manage the health of
populations, 366 Science (2019), https://www.science.org/doi/10.1126/science.aax2342.
66, 2022. https://www.reuters.com/technology/google-cuts-racy-results-by-30-searches-like-latina­
teenager-2022-03-30/
40. Safiya Umoja Noble. Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press.
Feb. 2018. https://nyupress.org/9781479837243/algorithms-of-oppression/
41. Paresh Dave. Google cuts racy results by 30% for searches like 'Latina teenager'. Reuters. Mar. 30,
2022. https://www.reuters.com/technology/google-cuts-racy-results-by-30-searches-like-latina­
teenager-2022-03-30/
42. Miranda Bogen. All the Ways Hiring Algorithms Can Introduce Bias. Harvard Business Review. May
6, 2019. https://hbr.org/2019/05/all-the-ways-hiring-algorithms-can-introduce-bias
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: 0.815
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.935
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.95
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.965
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.815
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.31166666666666665
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.19
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09649999999999999
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.815
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.935
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.95
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.965
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.8954135083695783
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.8723333333333333
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.8741632101558571
name: Cosine Map@100
verified: no, type: dot_accuracy@1
value: 0.815
name: Dot Accuracy@1
verified: no, type: dot_accuracy@3
value: 0.935
name: Dot Accuracy@3
verified: no, type: dot_accuracy@5
value: 0.95
name: Dot Accuracy@5
verified: no, type: dot_accuracy@10
value: 0.965
name: Dot Accuracy@10
verified: no, type: dot_precision@1
value: 0.815
name: Dot Precision@1
verified: no, type: dot_precision@3
value: 0.31166666666666665
name: Dot Precision@3
verified: no, type: dot_precision@5
value: 0.19
name: Dot Precision@5
verified: no, type: dot_precision@10
value: 0.09649999999999999
name: Dot Precision@10
verified: no, type: dot_recall@1
value: 0.815
name: Dot Recall@1
verified: no, type: dot_recall@3
value: 0.935
name: Dot Recall@3
verified: no, type: dot_recall@5
value: 0.95
name: Dot Recall@5
verified: no, type: dot_recall@10
value: 0.965
name: Dot Recall@10
verified: no, type: dot_ndcg@10
value: 0.8954135083695783
name: Dot Ndcg@10
verified: no, type: dot_mrr@10
value: 0.8723333333333333
name: Dot Mrr@10
verified: no, type: dot_map@100
value: 0.8741632101558571
name: Dot Map@100
verified: no
Likes: 0 | All-time downloads: 165
Created: 2024-09-23 20:35:44