Model: jet-taekyo/mpnet_finetuned_recursive
Author: jet-taekyo
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, mpnet, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:714, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss, arxiv:1908.10084, arxiv:2205.13147, arxiv:1705.00652, base_model:sentence-transformers/all-mpnet-base-v2, base_model:finetune:sentence-transformers/all-mpnet-base-v2, model-index, endpoints_compatible, region:us
Base models (finetune): id: sentence-transformers/all-mpnet-base-v2
Card metadata: base_model: sentence-transformers/all-mpnet-base-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:714, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
widget: source_sentence: What does the term 'rights, opportunities, or access' encompass in this framework?
sentences: 10 
GAI systems can ease the unintentional production or dissemination of false, inaccurate, or misleading 
content (misinformation) at scale, particularly if the content stems from confabulations.  
GAI systems can also ease the deliberate production or dissemination of false or misleading information 
(disinformation) at scale, where an actor has the explicit intent to deceive or cause harm to others. Even 
very subtle changes to text or images can manipulate human and machine perception. 
Similarly, GAI systems could enable a higher degree of sophistication for malicious actors to produce 
disinformation that is targeted towards speciﬁc demographics. Current and emerging multimodal models 
make it possible to generate both text-based disinformation and highly realistic “deepfakes” – that is, 
synthetic audiovisual content and photorealistic images.12 Additional disinformation threats could be 
enabled by future GAI models trained on new data modalities., 74. See, e.g., Heather Morrison. Virtual Testing Puts Disabled Students at a Disadvantage. Government
Technology. May 24, 2022.
https://www.govtech.com/education/k-12/virtual-testing-puts-disabled-students-at-a-disadvantage;
Lydia X. Z. Brown, Ridhi Shetty, Matt Scherer, and Andrew Crawford. Ableism And Disability
Discrimination In New Surveillance Technologies: How new surveillance technologies in education,
policing, health care, and the workplace disproportionately harm disabled people. Center for Democracy
and Technology Report. May 24, 2022.
https://cdt.org/insights/ableism-and-disability-discrimination-in-new-surveillance-technologies-how­
new-surveillance-technologies-in-education-policing-health-care-and-the-workplace­
disproportionately-harm-disabled-people/
69, persons, Asian Americans and Pacific Islanders and other persons of color; members of religious minorities; 
women, girls, and non-binary people; lesbian, gay, bisexual, transgender, queer, and intersex (LGBTQI+) 
persons; older adults; persons with disabilities; persons who live in rural areas; and persons otherwise adversely 
affected by persistent poverty or inequality. 
RIGHTS, OPPORTUNITIES, OR ACCESS: “Rights, opportunities, or access” is used to indicate the scoping 
of this framework. It describes the set of: civil rights, civil liberties, and privacy, including freedom of speech, 
voting, and protections from discrimination, excessive punishment, unlawful surveillance, and violations of 
privacy and other freedoms in both public and private sector contexts; equal opportunities, including equitable 
access to education, housing, credit, employment, and other programs; or, access to critical resources or, source_sentence: What are some broad negative risks associated with GAI design, development, and deployment?
sentences: actually occurring, or large-scale risks could occur); and broad GAI negative risks, 
including: Immature safety or risk cultures related to AI and GAI design, 
development and deployment, public information integrity risks, including impacts 
on democratic processes, unknown long-term performance characteristics of GAI. 
Information Integrity; Dangerous, 
Violent, or Hateful Content; CBRN 
Information or Capabilities 
GV-1.3-007 Devise a plan to halt development or deployment of a GAI system that poses 
unacceptable negative risk. 
CBRN Information and Capability; 
Information Security; Information 
Integrity 
AI Actor Tasks: Governance and Oversight 
 
GOVERN 1.4: The risk management process and its outcomes are established through transparent policies, procedures, and other 
controls based on organizational risk priorities. 
Action ID 
Suggested Action 
GAI Risks 
GV-1.4-001 
Establish policies and mechanisms to prevent GAI systems from generating, 39 
MS-3.3-004 
Provide input for training materials about the capabilities and limitations of GAI 
systems related to digital content transparency for AI Actors, other 
professionals, and the public about the societal impacts of AI and the role of 
diverse and inclusive content generation. 
Human-AI Conﬁguration; 
Information Integrity; Harmful Bias 
and Homogenization 
MS-3.3-005 
Record and integrate structured feedback about content provenance from 
operators, users, and potentially impacted communities through the use of 
methods such as user research studies, focus groups, or community forums. 
Actively seek feedback on generated content quality and potential biases. 
Assess the general awareness among end users and impacted communities 
about the availability of these feedback channels. 
Human-AI Conﬁguration; 
Information Integrity; Harmful Bias 
and Homogenization 
AI Actor Tasks: AI Deployment, Aﬀected Individuals and Communities, End-Users, Operation and Monitoring, TEVV, NOTICE & 
EXPLANATION 
WHY THIS PRINCIPLE IS IMPORTANT
This section provides a brief summary of the problems which the principle seeks to address and protect 
against, including illustrative examples. 
Automated systems now determine opportunities, from employment to credit, and directly shape the American 
public’s experiences, from the courtroom to online classrooms, in ways that profoundly impact people’s lives. But this 
expansive impact is not always visible. An applicant might not know whether a person rejected their resume or a 
hiring algorithm moved them to the bottom of the list. A defendant in the courtroom might not know if a judge deny­
ing their bail is informed by an automated system that labeled them “high risk.” From correcting errors to contesting 
decisions, people are often denied the knowledge they need to address the impact of automated systems on their lives., source_sentence: Who should conduct the assessment of the impact of surveillance on rights and opportunities?
sentences: APPENDIX
•
Julia Simon-Mishel, Supervising Attorney, Philadelphia Legal Assistance
•
Dr. Zachary Mahafza, Research & Data Analyst, Southern Poverty Law Center
•
J. Khadijah Abdurahman, Tech Impact Network Research Fellow, AI Now Institute, UCLA C2I1, and
UWA Law School
Panelists separately described the increasing scope of technology use in providing for social welfare, including 
in fraud detection, digital ID systems, and other methods focused on improving efficiency and reducing cost. 
However, various panelists individually cautioned that these systems may reduce burden for government 
agencies by increasing the burden and agency of people using and interacting with these technologies. 
Additionally, these systems can produce feedback loops and compounded harm, collecting data from 
communities and using it to reinforce inequality. Various panelists suggested that these harms could be, assessments, including data retention timelines and associated justification, and an assessment of the 
impact of surveillance or data collection on rights, opportunities, and access. Where possible, this 
assessment of the impact of surveillance should be done by an independent party. Reporting should be 
provided in a clear and machine-readable manner.  
35, access to education, housing, credit, employment, and other 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. 
10, source_sentence: How can voting-related systems impact privacy and security?
sentences: as custody and divorce information, and home, work, or school environmental data); or have the reasonable potential 
to be used in ways that are likely to expose individuals to meaningful harm, such as a loss of privacy or financial harm 
due to identity theft. Data and metadata generated by or about those who are not yet legal adults is also sensitive, even 
if not related to a sensitive domain. Such data includes, but is not limited to, numerical, text, image, audio, or video 
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,, agreed upon the importance of advisory boards and compensated community input early in the design process 
(before the technology is built and instituted). Various panelists also emphasized the importance of regulation 
that includes limits to the type and cost of such technologies. 
56, Surveillance and criminal justice system algorithms such as risk assessments, predictive  
    policing, automated license plate readers, real-time facial recognition systems (especially  
    those used in public places or during protected activities like peaceful protests), social media  
    monitoring, and ankle monitoring devices; 
Voting-related systems such as signature matching tools; 
Systems with a potential privacy impact such as smart home systems and associated data,  
    systems that use or collect health-related data, systems that use or collect education-related  
    data, criminal justice system data, ad-targeting systems, and systems that perform big data  
    analytics in order to build profiles or infer personal information about individuals; and 
Any system that has the meaningful potential to lead to algorithmic discrimination. 
• Equal opportunities, including but not limited to:, source_sentence: What impact do automated systems have on underserved communities?
sentences: generation, summarization, search, and chat. These activities can take place within organizational 
settings or in the public domain. 
Organizations can restrict AI applications that cause harm, exceed stated risk tolerances, or that conﬂict 
with their tolerances or values. Governance tools and protocols that are applied to other types of AI 
systems can be applied to GAI systems. These plans and actions include: 
• Accessibility and reasonable 
accommodations 
• AI actor credentials and qualiﬁcations  
• Alignment to organizational values 
• Auditing and assessment 
• Change-management controls 
• Commercial use 
• Data provenance, automated systems make on underserved communities and to institute proactive protections that support these 
communities. 
•
An automated system using nontraditional factors such as educational attainment and employment history as
part of its loan underwriting and pricing model was found to be much more likely to charge an applicant who
attended a Historically Black College or University (HBCU) higher loan prices for refinancing a student loan
than 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
•
A predictive model marketed as being able to predict whether students are likely to drop out of school was, on a principle of local control, such that those individuals closest to the data subject have more access while 
those who are less proximate do not (e.g., a teacher has access to their students’ daily progress data while a 
superintendent does not). 
Reporting. In addition to the reporting on data privacy (as listed above for non-sensitive data), entities devel-
oping technologies related to a sensitive domain and those collecting, using, storing, or sharing sensitive data 
should, whenever appropriate, regularly provide public reports describing: any data security lapses or breaches 
that resulted in sensitive data leaks; the number, type, and outcomes of ethical pre-reviews undertaken; a 
description of any data sold, shared, or made public, and how that data was assessed to determine it did not pres-
ent a sensitive data risk; and ongoing risk identification and management procedures, and any mitigation added
model-index: name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
results: task: type: information-retrieval
name: Information Retrieval
dataset: name: Unknown
type: unknown
metrics: type: cosine_accuracy@1
value: 0.8881578947368421
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.993421052631579
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.993421052631579
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.8881578947368421
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.331140350877193
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.19868421052631577
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09999999999999999
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.8881578947368421
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.993421052631579
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.993421052631579
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 1
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.9550417755482483
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.9395363408521302
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.9395363408521302
name: Cosine Map@100
verified: no, type: dot_accuracy@1
value: 0.8881578947368421
name: Dot Accuracy@1
verified: no, type: dot_accuracy@3
value: 0.993421052631579
name: Dot Accuracy@3
verified: no, type: dot_accuracy@5
value: 0.993421052631579
name: Dot Accuracy@5
verified: no, type: dot_accuracy@10
value: 1
name: Dot Accuracy@10
verified: no, type: dot_precision@1
value: 0.8881578947368421
name: Dot Precision@1
verified: no, type: dot_precision@3
value: 0.331140350877193
name: Dot Precision@3
verified: no, type: dot_precision@5
value: 0.19868421052631577
name: Dot Precision@5
verified: no, type: dot_precision@10
value: 0.09999999999999999
name: Dot Precision@10
verified: no, type: dot_recall@1
value: 0.8881578947368421
name: Dot Recall@1
verified: no, type: dot_recall@3
value: 0.993421052631579
name: Dot Recall@3
verified: no, type: dot_recall@5
value: 0.993421052631579
name: Dot Recall@5
verified: no, type: dot_recall@10
value: 1
name: Dot Recall@10
verified: no, type: dot_ndcg@10
value: 0.9550417755482483
name: Dot Ndcg@10
verified: no, type: dot_mrr@10
value: 0.9395363408521302
name: Dot Mrr@10
verified: no, type: dot_map@100
value: 0.9395363408521302
name: Dot Map@100
verified: no
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Created: 2024-09-26 01:23:12