Model: yinong333/finetuned_MiniLM
Author: yinong333
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:760, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss, arxiv:1908.10084, arxiv:2205.13147, arxiv:1705.00652, base_model:sentence-transformers/all-MiniLM-L6-v2, base_model:finetune:sentence-transformers/all-MiniLM-L6-v2, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: sentence-transformers/all-MiniLM-L6-v2
Card metadata: base_model: sentence-transformers/all-MiniLM-L6-v2
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:760, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
widget: source_sentence: Why is it important to establish clear timelines for data retention, and what should happen to data once those timelines are reached?
sentences: Technology 
Dignari 
Douglas Goddard 
Edgar Dworsky 
Electronic Frontier Foundation 
Electronic Privacy Information 
Center, Center for Digital 
Democracy, and Consumer 
Federation of America 
FaceTec 
Fight for the Future 
Ganesh Mani 
Georgia Tech Research Institute 
Google 
Health Information Technology 
Research and Development 
Interagency Working Group 
HireVue 
HR Policy Association 
ID.me 
Identity and Data Sciences 
Laboratory at Science Applications 
International Corporation 
Information Technology and 
Innovation Foundation 
Information Technology Industry 
Council 
Innocence Project 
Institute for Human-Centered 
Artificial Intelligence at Stanford 
University 
Integrated Justice Information 
Systems Institute 
International Association of Chiefs 
of Police 
International Biometrics + Identity 
Association 
International Business Machines 
Corporation 
International Committee of the Red 
Cross 
Inventionphysics 
iProov 
Jacob Boudreau 
Jennifer K. Wagner, Dan Berger,, new privacy risks and implementing appropriate mitigation measures, which may include express consent. 
Clear timelines for data retention should be established, with data deleted as soon as possible in accordance 
with legal or policy-based limitations. Determined data retention timelines should be documented and justi­
fied. 
Risk identification and mitigation. Entities that collect, use, share, or store sensitive data should 
attempt to proactively identify harms and seek to manage them so as to avoid, mitigate, and respond appropri­
ately to identified risks. Appropriate responses include determining not to process data when the privacy risks 
outweigh the benefits or implementing measures to mitigate acceptable risks. Appropriate responses do not 
include sharing or transferring the privacy risks to users via notice or consent requests where users could not 
reasonably be expected to understand the risks without further support., 55. Data & Trust Alliance. Algorithmic Bias Safeguards for Workforce: Overview. Jan. 2022. https://
dataandtrustalliance.org/Algorithmic_Bias_Safeguards_for_Workforce_Overview.pdf
56. Section 508.gov. IT Accessibility Laws and Policies. Access Board. https://www.section508.gov/
manage/laws-and-policies/
67, source_sentence: What is the purpose of the NIST AI Risk Management Framework?
sentences: TABLE OF CONTENTS
FROM PRINCIPLES TO PRACTICE: A TECHNICAL COMPANION TO THE BLUEPRINT 
FOR AN AI BILL OF RIGHTS 
 
USING THIS TECHNICAL COMPANION
 
SAFE AND EFFECTIVE SYSTEMS
 
ALGORITHMIC DISCRIMINATION PROTECTIONS
 
DATA PRIVACY
 
NOTICE AND EXPLANATION
 
HUMAN ALTERNATIVES, CONSIDERATION, AND FALLBACK
APPENDIX
 
EXAMPLES OF AUTOMATED SYSTEMS
 
LISTENING TO THE AMERICAN PEOPLE
ENDNOTES 
12
14
15
23
30
40
46
53
53
55
63
13, health diagnostic systems. 
The Blueprint for an AI Bill of Rights recognizes that law enforcement activities require a balancing of 
equities, for example, between the protection of sensitive law enforcement information and the principle of 
notice; as such, notice may not be appropriate, or may need to be adjusted to protect sources, methods, and 
other law enforcement equities. Even in contexts where these principles may not apply in whole or in part, 
federal departments and agencies remain subject to judicial, privacy, and civil liberties oversight as well as 
existing policies and safeguards that govern automated systems, including, for example, Executive Order 13960, 
Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government (December 2020). 
This white paper recognizes that national security (which includes certain law enforcement and 
homeland security activities) and defense activities are of increased sensitivity and interest to our nation’s, mitigate risks posed by the use of AI to companies’ reputation, legal responsibilities, and other product safety 
and effectiveness concerns. 
The Office of Management and Budget (OMB) has called for an expansion of opportunities 
for meaningful stakeholder engagement in the design of programs and services. OMB also 
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, source_sentence: What were the main topics discussed in the panel focused on consumer rights and protections in an automated society?
sentences: context, or may be more speculative and therefore uncertain. 
AI risks can diﬀer from or intensify traditional software risks. Likewise, GAI can exacerbate existing AI 
risks, and creates unique risks. GAI risks can vary along many dimensions: 
• 
Stage of the AI lifecycle: Risks can arise during design, development, deployment, operation, 
and/or decommissioning. 
• 
Scope: Risks may exist at individual model or system levels, at the application or implementation 
levels (i.e., for a speciﬁc use case), or at the ecosystem level – that is, beyond a single system or 
organizational context. Examples of the latter include the expansion of “algorithmic 
monocultures,3” resulting from repeated use of the same model, or impacts on access to 
opportunity, labor markets, and the creative economies.4 
• 
Source of risk: Risks may emerge from factors related to the design, training, or operation of the, speciﬁc and empirically well-substantiated negative risk to public safety (or has 
already caused harm). 
CBRN Information or Capabilities; 
Dangerous, Violent, or Hateful 
Content 
AI Actor Tasks: Governance and Oversight, theme, exploring current challenges and concerns and considering what an automated society that 
respects democratic values should look like. These discussions focused on the topics of consumer 
rights and protections, the criminal justice system, equal opportunities and civil justice, artificial 
intelligence and democratic values, social welfare and development, and the healthcare system. 
Summaries of Panel Discussions: 
Panel 1: Consumer Rights and Protections. This event explored the opportunities and challenges for 
individual consumers and communities in the context of a growing ecosystem of AI-enabled consumer 
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, source_sentence: How did the input from various stakeholders contribute to the development of the Blueprint for an 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, About this Document 
The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People was 
published by the White House Office of Science and Technology Policy in October 2022. This framework was 
released one year after OSTP announced the launch of a process to develop “a bill of rights for an AI-powered 
world.” Its release follows a year of public engagement to inform this initiative. The framework is available 
online at: https://www.whitehouse.gov/ostp/ai-bill-of-rights 
About the Office of Science and Technology Policy 
The Office of Science and Technology Policy (OSTP) was established by the National Science and Technology 
Policy, Organization, and Priorities Act of 1976 to provide the President and others within the Executive Office 
of the President with advice on the scientific, engineering, and technological aspects of the economy, national, 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
•
Carl Holshouser, Senior Vice President for Operations and Strategic Initiatives, TechNet
•
Surya Mattu, Senior Data Engineer and Investigative Data Journalist, The Markup
•
Mariah Montgomery, National Campaign Director, Partnership for Working Families
55, source_sentence: What legal action did the Federal Trade Commission take against Kochava regarding data tracking?
sentences: DATA PRIVACY 
EXTRA PROTECTIONS FOR DATA RELATED TO SENSITIVE
DOMAINS
•
Continuous positive airway pressure machines gather data for medical purposes, such as diagnosing sleep
apnea, and send usage data to a patient’s insurance company, which may subsequently deny coverage for the
device based on usage data. Patients were not aware that the data would be used in this way or monitored
by anyone other than their doctor.70 
•
A department store company used predictive analytics applied to collected consumer data to determine that a
teenage girl was pregnant, and sent maternity clothing ads and other baby-related advertisements to her
house, revealing to her father that she was pregnant.71
•
School audio surveillance systems monitor student conversations to detect potential "stress indicators" as
a warning of potential violence.72 Online proctoring systems claim to detect if a student is cheating on an, ENDNOTES
75. See., e.g., Sam Sabin. Digital surveillance in a post-Roe world. Politico. May 5, 2022. https://
www.politico.com/newsletters/digital-future-daily/2022/05/05/digital-surveillance-in-a-post-roe­
world-00030459; Federal Trade Commission. FTC Sues Kochava for Selling Data that Tracks People at
Reproductive Health Clinics, Places of Worship, and Other Sensitive Locations. Aug. 29, 2022. https://
www.ftc.gov/news-events/news/press-releases/2022/08/ftc-sues-kochava-selling-data-tracks-people­
reproductive-health-clinics-places-worship-other
76. Todd Feathers. This Private Equity Firm Is Amassing Companies That Collect Data on America’s
Children. The Markup. Jan. 11, 2022.
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, ENDNOTES
1.The Executive Order On Advancing Racial Equity and Support for Underserved Communities Through the
Federal Government. https://www.whitehouse.gov/briefing-room/presidential-actions/2021/01/20/executive
order-advancing-racial-equity-and-support-for-underserved-communities-through-the-federal-government/
2. The White House. Remarks by President Biden on the Supreme Court Decision to Overturn Roe v. Wade. Jun.
24, 2022. https://www.whitehouse.gov/briefing-room/speeches-remarks/2022/06/24/remarks-by-president­
biden-on-the-supreme-court-decision-to-overturn-roe-v-wade/
3. The White House. Join the Effort to Create A Bill of Rights for an Automated Society. Nov. 10, 2021. https://
www.whitehouse.gov/ostp/news-updates/2021/11/10/join-the-effort-to-create-a-bill-of-rights-for-an­
automated-society/
4. U.S. Dept. of Health, Educ. & Welfare, Report of the Sec’y’s Advisory Comm. on Automated Pers. Data Sys.,
model-index: name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
results: task: type: information-retrieval
name: Information Retrieval
dataset: name: Unknown
type: unknown
metrics: type: cosine_accuracy@1
value: 0.7214285714285714
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.8785714285714286
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.95
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.9714285714285714
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.7214285714285714
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.2928571428571428
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.18999999999999995
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09714285714285713
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.7214285714285714
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.8785714285714286
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.95
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.9714285714285714
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.8514639427234363
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.8122108843537416
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.8142292826221397
name: Cosine Map@100
verified: no, type: dot_accuracy@1
value: 0.7214285714285714
name: Dot Accuracy@1
verified: no, type: dot_accuracy@3
value: 0.8785714285714286
name: Dot Accuracy@3
verified: no, type: dot_accuracy@5
value: 0.95
name: Dot Accuracy@5
verified: no, type: dot_accuracy@10
value: 0.9714285714285714
name: Dot Accuracy@10
verified: no, type: dot_precision@1
value: 0.7214285714285714
name: Dot Precision@1
verified: no, type: dot_precision@3
value: 0.2928571428571428
name: Dot Precision@3
verified: no, type: dot_precision@5
value: 0.18999999999999995
name: Dot Precision@5
verified: no, type: dot_precision@10
value: 0.09714285714285713
name: Dot Precision@10
verified: no, type: dot_recall@1
value: 0.7214285714285714
name: Dot Recall@1
verified: no, type: dot_recall@3
value: 0.8785714285714286
name: Dot Recall@3
verified: no, type: dot_recall@5
value: 0.95
name: Dot Recall@5
verified: no, type: dot_recall@10
value: 0.9714285714285714
name: Dot Recall@10
verified: no, type: dot_ndcg@10
value: 0.8514639427234363
name: Dot Ndcg@10
verified: no, type: dot_mrr@10
value: 0.8122108843537416
name: Dot Mrr@10
verified: no, type: dot_map@100
value: 0.8142292826221397
name: Dot Map@100
verified: no
Likes: 0 | All-time downloads: 186
Created: 2024-09-23 14:38:03