Model: northstaranlyticsma24/artic_ft_midterm
Author: northstaranlyticsma24
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:363, 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:363, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
widget: source_sentence: What are some examples of algorithmic discrimination mentioned in the context, and how do they impact different areas such as hiring and healthcare?
sentences: For example, facial recognition technology that can contribute to wrongful and discriminatory 
arrests,31 hiring algorithms that inform discriminatory decisions, and healthcare algorithms that discount 
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 
launched, preventing harm to the public. Federal government agencies have been developing standards and guidance 
for the use of automated systems in order to help prevent bias. Non-profits and companies have developed best 
practices for audits and impact assessments to help identify potential algorithmic discrimination and provide 
transparency to the public in the mitigation of such biases. But there is much more work to do to protect the public from algorithmic discrimination to use and design 
automated systems in an equitable way. The guardrails protecting the public from discrimination in their daily 
lives should include their digital lives and impacts—basic safeguards against abuse, bias, and discrimination to 
ensure that all people are treated fairly when automated systems are used. This includes all dimensions of their 
lives, from hiring to loan approvals, from medical treatment and payment to encounters with the criminal 
justice system. Ensuring equity should also go beyond existing guardrails to consider the holistic impact that 
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
used by more than 500 universities across the country. The model was found to use race directly as a predictor,
and also shown to have large disparities by race; Black students were as many as four times as likely as their
otherwise similar white peers to be deemed at high risk of dropping out. These risk scores are used by advisors 
to guide students towards or away from majors, and some worry that they are being used to guide
Black students away from math and science subjects.34
•
A risk assessment tool designed to predict the risk of recidivism for individuals in federal custody showed
evidence of disparity in prediction. The tool overpredicts the risk of recidivism for some groups of color on the
general recidivism tools, and underpredicts the risk of recidivism for some groups of color on some of the
violent recidivism tools. The Department of Justice is working to reduce these disparities and has
publicly released a report detailing its review of the tool.35 
24
, SECTION: APPENDIX: EXAMPLES OF AUTOMATED SYSTEMS
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
support decision-makers who adjudicate benefits such as collating or analyzing information or
matching records, systems which similarly assist in the adjudication of administrative or criminal
penalties, fraud detection algorithms, services or benefits access control algorithms, biometric
systems used as access control, and systems which make benefits or services related decisions on a
fully or partially autonomous basis (such as a determination to revoke benefits). 54
, SECTION: SAFE AND EFFECTIVE SYSTEMS
 
 
 
 
 
 
 
SAFE AND EFFECTIVE 
SYSTEMS 
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 are tailored for particular sectors and contexts. In order to ensure that an automated system is safe and effective, it should include safeguards to protect the 
public from harm in a proactive and ongoing manner; avoid use of data inappropriate for or irrelevant to the task 
at hand, including reuse that could cause compounded harm; and demonstrate the safety and effectiveness of 
the system. These expectations are explained below. Protect the public from harm in a proactive and ongoing manner 
Consultation. The public should be consulted in the design, implementation, deployment, acquisition, and 
maintenance phases of automated system development, with emphasis on early-stage consultation before a 
system is introduced or a large change implemented. This consultation should directly engage diverse impact­
ed communities to consider concerns and risks that may be unique to those communities, or disproportionate­
ly prevalent or severe for them. The extent of this engagement and the form of outreach to relevant stakehold­
ers may differ depending on the specific automated system and development phase, but should include 
subject matter, sector-specific, and context-specific experts as well as experts on potential impacts such as 
civil rights, civil liberties, and privacy experts. For private sector applications, consultations before product 
launch may need to be confidential. Government applications, particularly law enforcement applications or 
applications that raise national security considerations, may require confidential or limited engagement based 
on system sensitivities and preexisting oversight laws and structures. Concerns raised in this consultation 
should be documented, and the automated system developers were proposing to create, use, or deploy should 
be reconsidered based on this feedback., source_sentence: What are some key needs identified by panelists for the future design of critical AI systems?
sentences: It included discussion of the 
technical aspects 
of 
designing 
non-discriminatory 
technology, 
explainable 
AI, 
human-computer 
interaction with an emphasis on community participation, and privacy-aware design. Welcome:
•
Sorelle Friedler, Assistant Director for Data and Democracy, White House Office of Science and
Technology Policy
•
J. Bob Alotta, Vice President for Global Programs, Mozilla Foundation
•
Navrina Singh, Board Member, Mozilla Foundation
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 
understanding of the algorithmic systems. The interventions and key needs various panelists put forward as 
necessary to the future design of critical AI systems included ongoing transparency, value sensitive and 
participatory design, explanations designed for relevant stakeholders, and public consultation. Various 
panelists emphasized the importance of placing trust in people, not technologies, and in engaging with 
impacted communities to understand the potential harms of technologies and build protection by design into 
future systems. Panel 5: Social Welfare and Development. This event explored current and emerging uses of technology to 
implement or improve social welfare systems, social development programs, and other systems that can impact 
life chances. Welcome:
•
Suresh Venkatasubramanian, Assistant Director for Science and Justice, White House Office of Science
and Technology Policy
•
Anne-Marie Slaughter, CEO, New America
Moderator: Michele Evermore, Deputy Director for Policy, Office of Unemployment Insurance 
Modernization, Office of the Secretary, Department of Labor 
Panelists:
•
Blake Hall, CEO and Founder, ID.Me
•
Karrie Karahalios, Professor of Computer Science, University of Illinois, Urbana-Champaign
•
Christiaan van Veen, Director of Digital Welfare State and Human Rights Project, NYU School of Law's
Center for Human Rights and Global Justice
58
, 20, 2021. https://www.vice.com/en/article/88npjv/amazons-ai-cameras-are-punishing­
drivers-for-mistakes-they-didnt-make
63
, 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
employers to verify resume information, every former Apple employee’s title gets erased and replaced with
a generic title. The Washington Post., source_sentence: How do automated identity controls at airports ensure assistance for individuals facing misidentification?
sentences: SECTION: ALGORITHMIC DISCRIMINATION PROTECTIONS
 ­­­­­­­
ALGORITHMIC DISCRIMINATION Protections
You should not face discrimination by algorithms 
and systems should be used and designed in an 
equitable 
way. Algorithmic 
discrimination 
occurs when 
automated systems contribute to unjustified different treatment or 
impacts disfavoring people based on their race, color, ethnicity, 
sex 
(including 
pregnancy, 
childbirth, 
and 
related 
medical 
conditions, 
gender 
identity, 
intersex 
status, 
and 
sexual 
orientation), religion, age, national origin, disability, veteran status, 
genetic infor-mation, or any other classification protected by law. Depending on the specific circumstances, such algorithmic 
discrimination may violate legal protections. Designers, developers, 
and deployers of automated systems should take proactive and 
continuous measures to protect individuals and communities 
from algorithmic discrimination and to use and design systems in 
an equitable way. This protection should include proactive equity 
assessments as part of the system design, use of representative data 
and protection against proxies for demographic features, ensuring 
accessibility for people with disabilities in design and development, 
pre-deployment and ongoing disparity testing and mitigation, and 
clear organizational oversight. Independent evaluation and plain 
language reporting in the form of an algorithmic impact assessment, 
including disparity testing results and mitigation information, 
should be performed and made public whenever possible to confirm 
these protections., These critical protections have been adopted in some scenarios. Where automated systems have been introduced to 
provide the public access to government benefits, existing human paper and phone-based processes are generally still 
in place, providing an important alternative to ensure access. Companies that have introduced automated call centers 
often retain the option of dialing zero to reach an operator. When automated identity controls are in place to board an 
airplane or enter the country, there is a person supervising the systems who can be turned to for help or to appeal a 
misidentification. The American people deserve the reassurance that such procedures are in place to protect their rights, opportunities, 
and access., SECTION: APPENDIX: EXAMPLES OF AUTOMATED SYSTEMS
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
support decision-makers who adjudicate benefits such as collating or analyzing information or
matching records, systems which similarly assist in the adjudication of administrative or criminal
penalties, fraud detection algorithms, services or benefits access control algorithms, biometric
systems used as access control, and systems which make benefits or services related decisions on a
fully or partially autonomous basis (such as a determination to revoke benefits). 54
, source_sentence: How should the availability of human consideration and fallback mechanisms be determined in relation to the potential impact of automated systems on rights, opportunities, or access?
sentences: In many scenarios, there is a reasonable expectation 
of human involvement in attaining rights, opportunities, or access. When automated systems make up part of 
the attainment process, alternative timely human-driven processes should be provided. The use of a human 
alternative should be triggered by an opt-out process. Timely and not burdensome human alternative. Opting out should be timely and not unreasonably 
burdensome in both the process of requesting to opt-out and the human-driven alternative provided. Provide timely human consideration and remedy by a fallback and escalation system in the 
event that an automated system fails, produces error, or you would like to appeal or con­
test its impacts on you 
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 
otherwise provided, should be easy to find and use. These mechanisms should be tested to ensure that users 
who have trouble with the automated system are able to use human consideration and fallback, with the under­
standing that it may be these users who are most likely to need the human assistance. Similarly, it should be 
tested to ensure that users with disabilities are able to find and use human consideration and fallback and also 
request reasonable accommodations or modifications. Convenient. Mechanisms for human consideration and fallback should not be unreasonably burdensome as 
compared to the automated system’s equivalent. 49
, SECTION: DATA PRIVACY
 
 
 
 
 
 
DATA PRIVACY 
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 are tailored for particular sectors and contexts. Data access and correction. People whose data is collected, used, shared, or stored by automated 
systems should be able to access data and metadata about themselves, know who has access to this data, and 
be able to correct it if necessary. Entities should receive consent before sharing data with other entities and 
should keep records of what data is shared and with whom. Consent withdrawal and data deletion. Entities should allow (to the extent legally permissible) with­
drawal of data access consent, resulting in the deletion of user data, metadata, and the timely removal of 
their data from any systems (e.g., machine learning models) derived from that data.68
Automated system support. Entities designing, developing, and deploying automated systems should 
establish and maintain the capabilities that will allow individuals to use their own automated systems to help 
them make consent, access, and control decisions in a complex data ecosystem. Capabilities include machine 
readable data, standardized data formats, metadata or tags for expressing data processing permissions and 
preferences and data provenance and lineage, context of use and access-specific tags, and training models for 
assessing privacy risk. Demonstrate that data privacy and user control are protected 
Independent evaluation. As described in the section on Safe and Effective Systems, entities should allow 
independent evaluation of the claims made regarding data policies. These independent evaluations should be 
made public whenever possible. Care will need to be taken to balance individual privacy with evaluation data 
access needs., SECTION: NOTICE AND EXPLANATION
 
 
  
 
 
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. •
A predictive policing system claimed to identify individuals at greatest risk to commit or become the victim of
gun violence (based on automated analysis of social ties to gang members, criminal histories, previous experi­
ences of gun violence, and other factors) and led to individuals being placed on a watch list with no
explanation or public transparency regarding how the system came to its conclusions.85 Both police and
the public deserve to understand why and how such a system is making these determinations. •
A system awarding benefits changed its criteria invisibly., source_sentence: What topics were discussed during the meetings related to the development of the Blueprint for an AI Bill of Rights?
sentences:  
GAI systems can produce content that is inciting, radicalizing, or threatening, or that gloriﬁes violence, 
with greater ease and scale than other technologies. LLMs have been reported to generate dangerous or 
violent recommendations, and some models have generated actionable instructions for dangerous or 
 
 
9 Confabulations of falsehoods are most commonly a problem for text-based outputs; for audio, image, or video 
content, creative generation of non-factual content can be a desired behavior. 10 For example, legal confabulations have been shown to be pervasive in current state-of-the-art LLMs. See also, 
e.g.,  
 
7 
unethical behavior., SECTION: LISTENING TO THE AMERICAN PEOPLE
APPENDIX
• OSTP conducted meetings with a variety of stakeholders in the private sector and civil society. Some of these
meetings were specifically focused on providing ideas related to the development of the Blueprint for an AI
Bill of Rights while others provided useful general context on the positive use cases, potential harms, and/or
oversight possibilities for these technologies., Transgender travelers have described degrading experiences associated
with these extra screenings.43 TSA has recently announced plans to implement a gender-neutral algorithm44 
while simultaneously enhancing the security effectiveness capabilities of the existing technology. •
The National Disabled Law Students Association expressed concerns that individuals with disabilities were
more likely to be flagged as potentially suspicious by remote proctoring AI systems because of their disabili-
ty-specific access needs such as needing longer breaks or using screen readers or dictation software.45 
•
An algorithm designed to identify patients with high needs for healthcare systematically assigned lower
scores (indicating that they were not as high need) to Black patients than to those of white patients, even
when those patients had similar numbers of chronic conditions and other markers of health.46 In addition,
healthcare clinical algorithms that are used by physicians to guide clinical decisions may include
sociodemographic variables that adjust or “correct” the algorithm’s output on the basis of a patient’s race or
ethnicity, which can lead to race-based health inequities.47
25
Algorithmic 
Discrimination 
Protections 

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: 0.7608695652173914
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.8695652173913043
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.9130434782608695
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.9782608695652174
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.7608695652173914
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.2898550724637682
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.18260869565217389
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.0978260869565217
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.7608695652173914
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.8695652173913043
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.9130434782608695
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.9782608695652174
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.8567216523715442
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.8190217391304349
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.8203804347826088
name: Cosine Map@100
verified: no, type: dot_accuracy@1
value: 0.7608695652173914
name: Dot Accuracy@1
verified: no, type: dot_accuracy@3
value: 0.8695652173913043
name: Dot Accuracy@3
verified: no, type: dot_accuracy@5
value: 0.9130434782608695
name: Dot Accuracy@5
verified: no, type: dot_accuracy@10
value: 0.9782608695652174
name: Dot Accuracy@10
verified: no, type: dot_precision@1
value: 0.7608695652173914
name: Dot Precision@1
verified: no, type: dot_precision@3
value: 0.2898550724637682
name: Dot Precision@3
verified: no, type: dot_precision@5
value: 0.18260869565217389
name: Dot Precision@5
verified: no, type: dot_precision@10
value: 0.0978260869565217
name: Dot Precision@10
verified: no, type: dot_recall@1
value: 0.7608695652173914
name: Dot Recall@1
verified: no, type: dot_recall@3
value: 0.8695652173913043
name: Dot Recall@3
verified: no, type: dot_recall@5
value: 0.9130434782608695
name: Dot Recall@5
verified: no, type: dot_recall@10
value: 0.9782608695652174
name: Dot Recall@10
verified: no, type: dot_ndcg@10
value: 0.8567216523715442
name: Dot Ndcg@10
verified: no, type: dot_mrr@10
value: 0.8190217391304349
name: Dot Mrr@10
verified: no, type: dot_map@100
value: 0.8203804347826088
name: Dot Map@100
verified: no
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Created: 2024-09-21 18:51:15