Model: svb01/fine-tuned-embedding-model
Author: svb01
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:555, loss:MultipleNegativesRankingLoss, arxiv:2311.07590, arxiv:2210.05791, arxiv:2305.15324, arxiv:2305.17493, arxiv:2306.03809, arxiv:2309.05196, arxiv:2308.14752, arxiv:2305.13873, arxiv:1908.10084, arxiv:1705.00652, base_model:sentence-transformers/all-MiniLM-L6-v2, base_model:finetune:sentence-transformers/all-MiniLM-L6-v2, endpoints_compatible, deploy:azure, 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
pipeline_tag: sentence-similarity
tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:555, loss:MultipleNegativesRankingLoss
widget: source_sentence: What does this text say about unclassified?
sentences: these sources. 
Errors in third-party GAI components can also have downstream impacts on accuracy and robustness. 
For example, test datasets commonly used to benchmark or validate models can contain label errors. 
Inaccuracies in these labels can impact the “stability” or robustness of these benchmarks, which many 
GAI practitioners consider during the model selection process.  
Trustworthy AI Characteristics: Accountable and Transparent, Explainable and Interpretable, Fair with 
Harmful Bias Managed, Privacy Enhanced, Safe, Secure and Resilient, Valid and Reliable 
3. 
Suggested Actions to Manage GAI Risks 
The following suggested actions target risks unique to or exacerbated by GAI. 
In addition to the suggested actions below, AI risk management activities and actions set forth in the AI 
RMF 1.0 and Playbook are already applicable for managing GAI risks. Organizations are encouraged to, and hardware vulnerabilities; labor practices; data privacy and localization 
compliance; geopolitical alignment). 
Data Privacy; Information Security; 
Value Chain and Component 
Integration; Harmful Bias and 
Homogenization 
MG-3.1-003 
Re-assess model risks after ﬁne-tuning or retrieval-augmented generation 
implementation and for any third-party GAI models deployed for applications 
and/or use cases that were not evaluated in initial testing. 
Value Chain and Component 
Integration 
MG-3.1-004 
Take reasonable measures to review training data for CBRN information, and 
intellectual property, and where appropriate, remove it. Implement reasonable 
measures to prevent, ﬂag, or take other action in response to outputs that 
reproduce particular training data (e.g., plagiarized, trademarked, patented, 
licensed content or trade secret material). 
Intellectual Property; CBRN 
Information or Capabilities 
 
43, • 
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 
GAI model itself, stemming in some cases from GAI model or system inputs, and in other cases, 
from GAI system outputs. Many GAI risks, however, originate from human behavior, including 
 
 
3 “Algorithmic monocultures” refers to the phenomenon in which repeated use of the same model or algorithm in, source_sentence: What does this text say about unclassified?
sentences: Security; Dangerous, Violent, or 
Hateful Content 
 
34 
MS-2.7-009 Regularly assess and verify that security measures remain eﬀective and have not 
been compromised. 
Information Security 
AI Actor Tasks: AI Deployment, AI Impact Assessment, Domain Experts, Operation and Monitoring, TEVV 
 
MEASURE 2.8: Risks associated with transparency and accountability – as identiﬁed in the MAP function – are examined and 
documented. 
Action ID 
Suggested Action 
GAI Risks 
MS-2.8-001 
Compile statistics on actual policy violations, take-down requests, and intellectual 
property infringement for organizational GAI systems: Analyze transparency 
reports across demographic groups, languages groups. 
Intellectual Property; Harmful Bias 
and Homogenization 
MS-2.8-002 Document the instructions given to data annotators or AI red-teamers. 
Human-AI Conﬁguration 
MS-2.8-003 
Use digital content transparency solutions to enable the documentation of each, information during GAI training and maintenance. 
Human-AI Conﬁguration; Obscene, 
Degrading, and/or Abusive 
Content; Value Chain and 
Component Integration; 
Dangerous, Violent, or Hateful 
Content 
MS-2.6-002 
Assess existence or levels of harmful bias, intellectual property infringement, 
data privacy violations, obscenity, extremism, violence, or CBRN information in 
system training data. 
Data Privacy; Intellectual Property; 
Obscene, Degrading, and/or 
Abusive Content; Harmful Bias and 
Homogenization; Dangerous, 
Violent, or Hateful Content; CBRN 
Information or Capabilities 
MS-2.6-003 Re-evaluate safety features of ﬁne-tuned models when the negative risk exceeds 
organizational risk tolerance. 
Dangerous, Violent, or Hateful 
Content 
MS-2.6-004 Review GAI system outputs for validity and safety: Review generated code to 
assess risks that may arise from unreliable downstream decision-making. 
Value Chain and Component 
Integration; Dangerous, Violent, or 
Hateful Content, Information Integrity; Harmful Bias 
and Homogenization 
AI Actor Tasks: AI Deployment, AI Impact Assessment, Domain Experts, End-Users, Operation and Monitoring, TEVV 
 
MEASURE 2.10: Privacy risk of the AI system – as identiﬁed in the MAP function – is examined and documented. 
Action ID 
Suggested Action 
GAI Risks 
MS-2.10-001 
Conduct AI red-teaming to assess issues such as: Outputting of training data 
samples, and subsequent reverse engineering, model extraction, and 
membership inference risks; Revealing biometric, conﬁdential, copyrighted, 
licensed, patented, personal, proprietary, sensitive, or trade-marked information; 
Tracking or revealing location information of users or members of training 
datasets. 
Human-AI Conﬁguration; 
Information Integrity; Intellectual 
Property 
MS-2.10-002 
Engage directly with end-users and other stakeholders to understand their 
expectations and concerns regarding content provenance. Use this feedback to, source_sentence: What does this text say about risk management?
sentences: robust watermarking techniques and corresponding detectors to identify the source of content or 
metadata recording techniques and metadata management tools and repositories to trace content 
origins and modiﬁcations. Further narrowing of GAI task deﬁnitions to include provenance data can 
enable organizations to maximize the utility of provenance data and risk management eﬀorts. 
A.1.7. Enhancing Content Provenance through Structured Public Feedback 
While indirect feedback methods such as automated error collection systems are useful, they often lack 
the context and depth that direct input from end users can provide. Organizations can leverage feedback 
approaches described in the Pre-Deployment Testing section to capture input from external sources such 
as through AI red-teaming.  
Integrating pre- and post-deployment external feedback into the monitoring process for GAI models and, tools for monitoring third-party GAI risks; Consider policy adjustments across GAI 
modeling libraries, tools and APIs, ﬁne-tuned models, and embedded tools; 
Assess GAI vendors, open-source or proprietary GAI tools, or GAI service 
providers against incident or vulnerability databases. 
Data Privacy; Human-AI 
Conﬁguration; Information 
Security; Intellectual Property; 
Value Chain and Component 
Integration; Harmful Bias and 
Homogenization 
GV-6.1-010 
Update GAI acceptable use policies to address proprietary and open-source GAI 
technologies and data, and contractors, consultants, and other third-party 
personnel. 
Intellectual Property; Value Chain 
and Component Integration 
AI Actor Tasks: Operation and Monitoring, Procurement, Third-party entities 
 
GOVERN 6.2: Contingency processes are in place to handle failures or incidents in third-party data or AI systems deemed to be 
high-risk. 
Action ID 
Suggested Action 
GAI Risks 
GV-6.2-001, MEASURE 2.3: AI system performance or assurance criteria are measured qualitatively or quantitatively and demonstrated for 
conditions similar to deployment setting(s). Measures are documented. 
Action ID 
Suggested Action 
GAI Risks 
MS-2.3-001 Consider baseline model performance on suites of benchmarks when selecting a 
model for ﬁne tuning or enhancement with retrieval-augmented generation. 
Information Security; 
Confabulation 
MS-2.3-002 Evaluate claims of model capabilities using empirically validated methods. 
Confabulation; Information 
Security 
MS-2.3-003 Share results of pre-deployment testing with relevant GAI Actors, such as those 
with system release approval authority. 
Human-AI Conﬁguration 
 
31 
MS-2.3-004 
Utilize a purpose-built testing environment such as NIST Dioptra to empirically 
evaluate GAI trustworthy characteristics. 
CBRN Information or Capabilities; 
Data Privacy; Confabulation; 
Information Integrity; Information 
Security; Dangerous, Violent, or, source_sentence: What does this text say about unclassified?
sentences: techniques such as re-sampling, re-ranking, or adversarial training to mitigate 
biases in the generated content. 
Information Security; Harmful Bias 
and Homogenization 
MG-2.2-005 
Engage in due diligence to analyze GAI output for harmful content, potential 
misinformation, and CBRN-related or NCII content. 
CBRN Information or Capabilities; 
Obscene, Degrading, and/or 
Abusive Content; Harmful Bias and 
Homogenization; Dangerous, 
Violent, or Hateful Content 
 
41 
MG-2.2-006 
Use feedback from internal and external AI Actors, users, individuals, and 
communities, to assess impact of AI-generated content. 
Human-AI Conﬁguration 
MG-2.2-007 
Use real-time auditing tools where they can be demonstrated to aid in the 
tracking and validation of the lineage and authenticity of AI-generated data. 
Information Integrity 
MG-2.2-008 
Use structured feedback mechanisms to solicit and capture user input about AI-
generated content to detect subtle shifts in quality or alignment with, Human-AI Conﬁguration; Value 
Chain and Component Integration 
MP-5.2-002 
Plan regular engagements with AI Actors responsible for inputs to GAI systems, 
including third-party data and algorithms, to review and evaluate unanticipated 
impacts. 
Human-AI Conﬁguration; Value 
Chain and Component Integration 
AI Actor Tasks: AI Deployment, AI Design, AI Impact Assessment, Aﬀected Individuals and Communities, Domain Experts, End-
Users, Human Factors, Operation and Monitoring  
 
MEASURE 1.1: Approaches and metrics for measurement of AI risks enumerated during the MAP function are selected for 
implementation starting with the most signiﬁcant AI risks. The risks or trustworthiness characteristics that will not – or cannot – be 
measured are properly documented. 
Action ID 
Suggested Action 
GAI Risks 
MS-1.1-001 Employ methods to trace the origin and modiﬁcations of digital content. 
Information Integrity 
MS-1.1-002, input them directly to a GAI system, with a variety of downstream negative consequences to 
interconnected systems. Indirect prompt injection attacks occur when adversaries remotely (i.e., without 
a direct interface) exploit LLM-integrated applications by injecting prompts into data likely to be 
retrieved. Security researchers have already demonstrated how indirect prompt injections can exploit 
vulnerabilities by stealing proprietary data or running malicious code remotely on a machine. Merely 
querying a closed production model can elicit previously undisclosed information about that model. 
Another cybersecurity risk to GAI is data poisoning, in which an adversary compromises a training 
dataset used by a model to manipulate its outputs or operation. Malicious tampering with data or parts 
of the model could exacerbate risks associated with GAI system outputs. 
Trustworthy AI Characteristics: Privacy Enhanced, Safe, Secure and Resilient, Valid and Reliable 
2.10., source_sentence: What does this text say about data privacy?
sentences: Property. We also note that some risks are cross-cutting between these categories.  
 
4 
1. CBRN Information or Capabilities: Eased access to or synthesis of materially nefarious 
information or design capabilities related to chemical, biological, radiological, or nuclear (CBRN) 
weapons or other dangerous materials or agents. 
2. Confabulation: The production of conﬁdently stated but erroneous or false content (known 
colloquially as “hallucinations” or “fabrications”) by which users may be misled or deceived.6 
3. Dangerous, Violent, or Hateful Content: Eased production of and access to violent, inciting, 
radicalizing, or threatening content as well as recommendations to carry out self-harm or 
conduct illegal activities. Includes diﬃculty controlling public exposure to hateful and disparaging 
or stereotyping content. 
4. Data Privacy: Impacts due to leakage and unauthorized use, disclosure, or de-anonymization of, information during GAI training and maintenance. 
Human-AI Conﬁguration; Obscene, 
Degrading, and/or Abusive 
Content; Value Chain and 
Component Integration; 
Dangerous, Violent, or Hateful 
Content 
MS-2.6-002 
Assess existence or levels of harmful bias, intellectual property infringement, 
data privacy violations, obscenity, extremism, violence, or CBRN information in 
system training data. 
Data Privacy; Intellectual Property; 
Obscene, Degrading, and/or 
Abusive Content; Harmful Bias and 
Homogenization; Dangerous, 
Violent, or Hateful Content; CBRN 
Information or Capabilities 
MS-2.6-003 Re-evaluate safety features of ﬁne-tuned models when the negative risk exceeds 
organizational risk tolerance. 
Dangerous, Violent, or Hateful 
Content 
MS-2.6-004 Review GAI system outputs for validity and safety: Review generated code to 
assess risks that may arise from unreliable downstream decision-making. 
Value Chain and Component 
Integration; Dangerous, Violent, or 
Hateful Content, Scheurer, J. et al. (2023) Technical report: Large language models can strategically deceive their users 
when put under pressure. arXiv. https://arxiv.org/abs/2311.07590 
Shelby, R. et al. (2023) Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm 
Reduction. arXiv. https://arxiv.org/pdf/2210.05791 
Shevlane, T. et al. (2023) Model evaluation for extreme risks. arXiv. https://arxiv.org/pdf/2305.15324 
Shumailov, I. et al. (2023) The curse of recursion: training on generated data makes models forget. arXiv. 
https://arxiv.org/pdf/2305.17493v2 
Smith, A. et al. (2023) Hallucination or Confabulation? Neuroanatomy as metaphor in Large Language 
Models. PLOS Digital Health. 
https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000388 
Soice, E. et al. (2023) Can large language models democratize access to dual-use biotechnology? arXiv. 
https://arxiv.org/abs/2306.03809
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Created: 2024-09-23 05:47:46