Model: pattonma/AIE4_midterm_tuned_embeddings_2
Author: pattonma
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:600, loss:MatryoshkaLoss, loss:CoSENTLoss, 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:600, loss:MatryoshkaLoss, loss:CoSENTLoss, loss:MultipleNegativesRankingLoss
widget: source_sentence: What is meant by "mission creep" in the context of data collection, and how can it be avoided?
sentences: Moderator: Kathy Pham Evans, Deputy Chief Technology Officer for Product and Engineering, U.S 
Federal Trade Commission. 
Panelists: 
•
Liz O’Sullivan, CEO, Parity AI
•
Timnit Gebru, Independent Scholar
•
Jennifer Wortman Vaughan, Senior Principal Researcher, Microsoft Research, New York City
•
Pamela Wisniewski, Associate Professor of Computer Science, University of Central Florida; Director,
Socio-technical Interaction Research (STIR) Lab
•
Seny Kamara, Associate Professor of Computer Science, Brown University
Each panelist individually emphasized the risks of using AI in high-stakes settings, including the potential for 
biased data and discriminatory outcomes, opaque decision-making processes, and lack of public trust and, HUMAN ALTERNATIVES, 
CONSIDERATION, AND 
FALLBACK 
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. 
•
An unemployment benefits system in Colorado required, as a condition of accessing benefits, that applicants
have a smartphone in order to verify their identity. No alternative human option was readily available,
which denied many people access to benefits.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 tax, collection should be minimized and clearly communicated to the people whose data is collected. Data should 
only be collected or used for the purposes of training or testing machine learning models if such collection and 
use is legal and consistent with the expectations of the people whose data is collected. User experience 
research should be conducted to confirm that people understand what data is being collected about them and 
how it will be used, and that this collection matches their expectations and desires. 
Data collection and use-case scope limits. Data collection should be limited in scope, with specific, 
narrow identified goals, to avoid "mission creep."  Anticipated data collection should be determined to be, source_sentence: How has the public's understanding of sensitive domains changed over time?
sentences: Proportionate. The availability of human consideration and fallback, along with associated training and 
safeguards against human bias, should be proportionate to the potential of the automated system to meaning­
fully impact rights, opportunities, or access. Automated systems that have greater control over outcomes, 
provide input to high-stakes decisions, relate to sensitive domains, or otherwise have a greater potential to 
meaningfully impact rights, opportunities, or access should have greater availability (e.g., staffing) and over­
sight of human consideration and fallback mechanisms. 
Accessible. Mechanisms for human consideration and fallback, whether in-person, on paper, by phone, or, DATA PRIVACY 
EXTRA PROTECTIONS FOR DATA RELATED TO SENSITIVE
DOMAINS
Some domains, including health, employment, education, criminal justice, and personal finance, have long been 
singled out as sensitive domains deserving of enhanced data protections. This is due to the intimate nature of these 
domains as well as the inability of individuals to opt out of these domains in any meaningful way, and the 
historical discrimination that has often accompanied data knowledge.69 Domains understood by the public to be 
sensitive also change over time, including because of technological developments. Tracking and monitoring 
technologies, personal tracking devices, and our extensive data footprints are used and misused more than ever, 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, source_sentence: Why is it important to assess the potential impact of surveillance technologies on your rights, opportunities, or access?
sentences: enforcement or national security restrictions prevent doing so. Care should be taken to balance individual 
privacy with evaluation data access needs; in many cases, policy-based and/or technological innovations and 
controls allow access to such data without compromising privacy. 
Reporting. Entities responsible for the development or use of automated systems should provide 
reporting of an appropriately designed algorithmic impact assessment,50 with clear specification of who 
performs the assessment, who evaluates the system, and how corrective actions are taken (if necessary) in 
response to the assessment. This algorithmic impact assessment should include at least: the results of any, SAFE AND EFFECTIVE 
SYSTEMS 
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. 
While technologies are being deployed to solve problems across a wide array of issues, our reliance on technology can 
also lead to its use in situations where it has not yet been proven to work—either at all or within an acceptable range 
of error. In other cases, technologies do not work as intended or as promised, causing substantial and unjustified harm. 
Automated systems sometimes rely on data from other systems, including historical data, allowing irrelevant informa­, 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 is the purpose of the Blueprint for an AI Bill of Rights as described in the context?
sentences: in some cases. Many states have also enacted consumer data privacy protection regimes to address some of these 
harms. 
However, these are not yet standard practices, and the United States lacks a comprehensive statutory or regulatory 
framework governing the rights of the public when it comes to personal data. While a patchwork of laws exists to 
guide the collection and use of personal data in specific contexts, including health, employment, education, and credit, 
it can be unclear how these laws apply in other contexts and in an increasingly automated society. Additional protec­
tions would assure the American public that the automated systems they use are not monitoring their activities,, existing human performance considered as a performance baseline for the algorithm to meet pre-deployment, 
and as a lifecycle minimum performance standard. Decision possibilities resulting from performance testing 
should include the possibility of not deploying the system. 
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, enforcement, and other regulatory contexts may require government actors to protect civil rights, civil liberties, 
and privacy in a manner consistent with, but using alternate mechanisms to, the specific principles discussed in 
this framework. The Blueprint for an AI Bill of Rights is meant to assist governments and the private sector in 
moving principles into practice. 
The expectations given in the Technical Companion 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. While 
existing laws informed the development of the Blueprint for an AI Bill of Rights, this framework does not detail, source_sentence: What are the privacy and civil rights implications of using biometric identification technologies in New York schools?
sentences: (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, and other data-driven automated systems most directly collect data on, make inferences about, and may cause 
harm to individuals. But the overall magnitude of their impacts may be most readily visible at the level of com-
munities. Accordingly, the concept of community is integral to the scope of the Blueprint for an AI Bill of Rights. 
United States law and policy have long employed approaches for protecting the rights of individuals, but exist-
ing frameworks have sometimes struggled to provide protections when effects manifest most clearly at a com-
munity level. For these reasons, the Blueprint for an AI Bill of Rights asserts that the harms of automated, the privacy, civil rights, and civil liberties implications of the use of such technologies be issued before 
biometric identification technologies can be used in New York schools. 
Federal law requires employers, and any consultants they may retain, to report the costs 
of surveilling employees in the context of a labor dispute, providing a transparency 
mechanism to help protect worker organizing. Employers engaging in workplace surveillance "where 
an object there-of, directly or indirectly, is […] to obtain information concerning the activities of employees or a 
labor organization in connection with a labor dispute" must report expenditures relating to this surveillance to
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: 0.82
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.9
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.92
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.97
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.82
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.3
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.18399999999999994
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09699999999999998
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.82
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.9
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.92
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.97
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.8900901972041357
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.8653174603174604
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.866803936952293
name: Cosine Map@100
verified: no, type: dot_accuracy@1
value: 0.82
name: Dot Accuracy@1
verified: no, type: dot_accuracy@3
value: 0.9
name: Dot Accuracy@3
verified: no, type: dot_accuracy@5
value: 0.92
name: Dot Accuracy@5
verified: no, type: dot_accuracy@10
value: 0.97
name: Dot Accuracy@10
verified: no, type: dot_precision@1
value: 0.82
name: Dot Precision@1
verified: no, type: dot_precision@3
value: 0.3
name: Dot Precision@3
verified: no, type: dot_precision@5
value: 0.18399999999999994
name: Dot Precision@5
verified: no, type: dot_precision@10
value: 0.09699999999999998
name: Dot Precision@10
verified: no, type: dot_recall@1
value: 0.82
name: Dot Recall@1
verified: no, type: dot_recall@3
value: 0.9
name: Dot Recall@3
verified: no, type: dot_recall@5
value: 0.92
name: Dot Recall@5
verified: no, type: dot_recall@10
value: 0.97
name: Dot Recall@10
verified: no, type: dot_ndcg@10
value: 0.8900901972041357
name: Dot Ndcg@10
verified: no, type: dot_mrr@10
value: 0.8653174603174604
name: Dot Mrr@10
verified: no, type: dot_map@100
value: 0.866803936952293
name: Dot Map@100
verified: no
Likes: 0 | All-time downloads: 158
Created: 2024-09-23 22:14:59