Model: jimmydzj2006/snowflake-arctic-embed-xs_finetuned_aipolicy
Author: jimmydzj2006
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:2730, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss, arxiv:1908.10084, arxiv:2205.13147, arxiv:1705.00652, base_model:Snowflake/snowflake-arctic-embed-xs, base_model:finetune:Snowflake/snowflake-arctic-embed-xs, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: Snowflake/snowflake-arctic-embed-xs
Card metadata: base_model: Snowflake/snowflake-arctic-embed-xs
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:2730, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
widget: source_sentence: What steps can be taken to mitigate the risks associated with GAI systems?
sentences: Action ID: GV-4.3-003
Suggested Action: Verify information sharing and feedback mechanisms among individuals and
organizations regarding any negative impact from GAI systems.
GAI Risks: Information Integrity; Data
Privacy, 48. The definitions of 'equity' and 'underserved communities' can be found in the Definitions section of this framework as well as in Section 2 of The Executive Order On Advancing Racial Equity and Support [for Underserved Communities Through the Federal Government. https://www.whitehouse.gov/](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/

49. Id., Action ID: GV-6.1-001
Suggested Action: Categorize different types of GAI content with associated third-party rights (e.g.,
copyright, intellectual property, data privacy).
GAI Risks: Data Privacy; Intellectual
Property; Value Chain and
Component Integration, source_sentence: What tasks are associated with AI Actor governance and oversight?
sentences: GOVERN 1.1: Legal and regulatory requirements involving AI are understood, managed, and documented.: MANAGE 4.2: Measurable activities for continual improvements are integrated into AI system updates and include regular
engagement with interested parties, including relevant AI Actors.
AI Actor Tasks: Governance and Oversight: AI Actor Tasks: AI Deployment, AI Design, AI Development, Affected Individuals and Communities, End-Users, Operation and
Monitoring, TEVV, Beyond harms from information exposure (such as extortion or dignitary harm), wrong or inappropriate inferences of PII can contribute to downstream or secondary harmful impacts. For example, predictive inferences made by GAI models based on PII or protected attributes can contribute to adverse decisions, leading to representational or allocative harms to individuals or groups (see Harmful Bias and Homogenization below).  #### Trustworthy AI Characteristics: Accountable and Transparent, Privacy Enhanced, Safe, Secure and Resilient

 2.5. Environmental Impacts

 Training, maintaining, and operating (running inference on) GAI systems are resource-intensive activities, with potentially large energy and environmental footprints. Energy and carbon emissions vary based on what is being done with the GAI model (i.e., pre-training, fine-tuning, inference), the modality of the content, hardware used, and type of task or application.

 Current estimates suggest that training a single transformer LLM can emit as much carbon as 300 round- trip flights between San Francisco and New York. In a study comparing energy consumption and carbon emissions for LLM inference, generative tasks (e.g., text summarization) were found to be more energy- and carbon-intensive than discriminative or non-generative tasks (e.g., text classification). 

 Methods for creating smaller versions of trained models, such as model distillation or compression, could reduce environmental impacts at inference time, but training and tuning such models may still contribute to their environmental impacts. Currently there is no agreed upon method to estimate environmental impacts from GAI. 

 Trustworthy AI Characteristics: Accountable and Transparent, Safe

 2.6. Harmful Bias and Homogenization, #### • Accessibility and reasonable accommodations

 • AI actor credentials and qualifications  • Alignment to organizational values

#### • Auditing and assessment  • Change-management controls  • Commercial use  • Data provenance #### • Data protection  • Data retention  • Consistency in use of defining key terms  • Decommissioning  • Discouraging anonymous use  • Education  • Impact assessments  • Incident response  • Monitoring  • Opt-outs

#### • Risk-based controls  • Risk mapping and measurement  • Science-backed TEVV practices  • Secure software development practices  • Stakeholder engagement  • Synthetic content detection and labeling tools and techniques

 • Whistleblower protections  • Workforce diversity and interdisciplinary teams

#### Establishing acceptable use policies and guidance for the use of GAI in formal human-AI teaming settings as well as different levels of human-AI configurations can help to decrease risks arising from misuse, abuse, inappropriate repurpose, and misalignment between systems and users. These practices are just one example of adapting existing governance protocols for GAI contexts. 

 A.1.3. Third-Party Considerations

 Organizations may seek to acquire, embed, incorporate, or use open-source or proprietary third-party GAI models, systems, or generated data for various applications across an enterprise. Use of these GAI tools and inputs has implications for all functions of the organization – including but not limited to acquisition, human resources, legal, compliance, and IT services – regardless of whether they are carried out by employees or third parties. Many of the actions cited above are relevant and options for addressing third-party considerations., source_sentence: What specific topic is covered in Chapter 3 of the AI Risk Management Framework by NIST?
sentences: Liang, W. et al. (2023) GPT detectors are biased against non-native English writers. arXiv. https://arxiv.org/abs/2304.02819

 Luccioni, A. et al. (2023) Power Hungry Processing: Watts Driving the Cost of AI Deployment? arXiv. https://arxiv.org/pdf/2311.16863

 Mouton, C. et al. (2024) The Operational Risks of AI in Large-Scale Biological Attacks. RAND. https://www.rand.org/pubs/research_reports/RRA2977-2.html.

 Nicoletti, L. et al. (2023) Humans Are Biased. Generative Ai Is Even Worse. Bloomberg. https://www.bloomberg.com/graphics/2023-generative-ai-bias/.

 National Institute of Standards and Technology (2024) Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations https://csrc.nist.gov/pubs/ai/100/2/e2023/final

 National Institute of Standards and Technology (2023) AI Risk Management Framework. https://www.nist.gov/itl/ai-risk-management-framework

 National Institute of Standards and Technology (2023) AI Risk Management Framework, Chapter 3: AI Risks and Trustworthiness. https://airc.nist.gov/AI_RMF_Knowledge_Base/AI_RMF/Foundational_Information/3-sec-characteristics

 National Institute of Standards and Technology (2023) AI Risk Management Framework, Chapter 6: AI RMF Profiles. https://airc.nist.gov/AI_RMF_Knowledge_Base/AI_RMF/Core_And_Profiles/6-sec-profile, ###### 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.

**Equitable.** Consideration should be given to ensuring outcomes of the fallback and escalation system are equitable when compared to those of the automated system and such that the fallback and escalation system provides equitable access to underserved communities.[105]

**Timely. Human consideration and fallback are only useful if they are conducted and concluded in a** timely manner. The determination of what is timely should be made relative to the specific automated system, and the review system should be staffed and regularly assessed to ensure it is providing timely consideration and fallback. In time-critical systems, this mechanism should be immediately available or, where possible, available before the harm occurs. Time-critical systems include, but are not limited to, voting-related systems, automated building access and other access systems, systems that form a critical component of healthcare, and systems that have the ability to withhold wages or otherwise cause immediate financial penalties.

**Effective.** The organizational structure surrounding processes for consideration and fallback should be designed so that if the human decision-maker charged with reassessing a decision determines that it should be overruled, the new decision will be effectively enacted. This includes ensuring that the new decision is entered into the automated system throughout its components, any previous repercussions from the old decision are also overturned, and safeguards are put in place to help ensure that future decisions do not result in the same errors.

**Maintained. The human consideration and fallback process and any associated automated processes** should be maintained and supported as long as the relevant automated system continues to be in use.

**Institute training, assessment, and oversight to combat automation bias and ensure any** **human-based components of a system are effective.**, **Institute training, assessment, and oversight to combat automation bias and ensure any** **human-based components of a system are effective.**

**Training and assessment. Anyone administering, interacting with, or interpreting the outputs of an auto­** mated system should receive training in that system, including how to properly interpret outputs of a system in light of its intended purpose and in how to mitigate the effects of automation bias. The training should reoc­ cur regularly to ensure it is up to date with the system and to ensure the system is used appropriately. Assess­ ment should be ongoing to ensure that the use of the system with human involvement provides for appropri­ ate results, i.e., that the involvement of people does not invalidate the system's assessment as safe and effective or lead to algorithmic discrimination.

**Oversight. Human-based systems have the potential for bias, including automation bias, as well as other** concerns that may limit their effectiveness. The results of assessments of the efficacy and potential bias of such human-based systems should be overseen by governance structures that have the potential to update the operation of the human-based system in order to mitigate these effects. **HUMAN ALTERNATIVES,** **CONSIDERATION, AND** **FALLBACK**

###### 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.

**Implement additional human oversight and safeguards for automated systems related to** **sensitive domains**

Automated systems used within sensitive domains, including criminal justice, employment, education, and health, should meet the expectations laid out throughout this framework, especially avoiding capricious, inappropriate, and discriminatory impacts of these technologies. Additionally, automated systems used within sensitive domains should meet these expectations:, source_sentence: What is the primary goal of protecting the public from algorithmic discrimination?
sentences: Action ID: MS-1.3-001
Suggested Action: Define relevant groups of interest (e.g., demographic groups, subject matter
experts, experience with GAI technology) within the context of use as part of
plans for gathering structured public feedback.
GAI Risks: Human-AI Configuration; Harmful
Bias and Homogenization; CBRN
Information or Capabilities, Action ID: GV-6.1-001
Suggested Action: Categorize different types of GAI content with associated third-party rights (e.g.,
copyright, intellectual property, data privacy).
GAI Risks: Data Privacy; Intellectual
Property; Value Chain and
Component Integration, **Protect the public from algorithmic discrimination in a proactive and ongoing manner**

**Proactive assessment of equity in design. Those responsible for the development, use, or oversight of** automated systems should conduct proactive equity assessments in the design phase of the technology research and development or during its acquisition to review potential input data, associated historical context, accessibility for people with disabilities, and societal goals to identify potential discrimination and effects on equity resulting from the introduction of the technology. The assessed groups should be as inclusive as possible of the underserved communities mentioned in the equity definition: Black, Latino, and Indigenous and Native American 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. Assessment could include both qualitative and quantitative evaluations of the system. This equity assessment should also be considered a core part of the goals of the consultation conducted as part of the safety and efficacy review.

**Representative and robust data. Any data used as part of system development or assessment should be** representative of local communities based on the planned deployment setting and should be reviewed for bias based on the historical and societal context of the data. Such data should be sufficiently robust to identify and help to mitigate biases and potential harms., source_sentence: How can human subjects revoke their consent according to the suggested action?
sentences: Disinformation and misinformation – both of which may be facilitated by GAI – may erode public trust in true or valid evidence and information, with downstream effects. For example, a synthetic image of a Pentagon blast went viral and briefly caused a drop in the stock market. Generative AI models can also assist malicious actors in creating compelling imagery and propaganda to support disinformation campaigns, which may not be photorealistic, but could enable these campaigns to gain more reach and engagement on social media platforms. Additionally, generative AI models can assist malicious actors in creating fraudulent content intended to impersonate others.

 Trustworthy AI Characteristics: Accountable and Transparent, Safe, Valid and Reliable, Interpretable and Explainable

 2.9. Information Security

 Information security for computer systems and data is a mature field with widely accepted and standardized practices for offensive and defensive cyber capabilities. GAI-based systems present two primary information security risks: GAI could potentially discover or enable new cybersecurity risks by lowering the barriers for or easing automated exercise of offensive capabilities; simultaneously, it expands the available attack surface, as GAI itself is vulnerable to attacks like prompt injection or data poisoning. 

 Offensive cyber capabilities advanced by GAI systems may augment cybersecurity attacks such as hacking, malware, and phishing. Reports have indicated that LLMs are already able to discover some vulnerabilities in systems (hardware, software, data) and write code to exploit them. Sophisticated threat actors might further these risks by developing GAI-powered security co-pilots for use in several parts of the attack chain, including informing attackers on how to proactively evade threat detection and escalate privileges after gaining system access.

 Information security for GAI models and systems also includes maintaining availability of the GAI system and the integrity and (when applicable) the confidentiality of the GAI code, training data, and model weights. To identify and secure potential attack points in AI systems or specific components of the AI, Action ID: GV-4.2-003
Suggested Action: Verify that downstream GAI system impacts (such as the use of third-party
plugins) are included in the impact documentation process.
GAI Risks: Value Chain and Component
Integration, Action ID: MS-2.2-003
Suggested Action: Provide human subjects with options to withdraw participation or revoke their
consent for present or future use of their data in GAI applications.
GAI Risks: Data Privacy; Human-AI
Configuration; Information
Integrity
model-index: name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-xs
results: task: type: information-retrieval
name: Information Retrieval
dataset: name: Unknown
type: unknown
metrics: type: cosine_accuracy@1
value: 0.6483516483516484
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.7362637362637363
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.7802197802197802
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.8571428571428571
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.6483516483516484
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.2454212454212454
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.15604395604395602
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.0857142857142857
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.6483516483516484
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.7362637362637363
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.7802197802197802
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.8571428571428571
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.7433871430365133
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.7083235217163788
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.71581044355863
name: Cosine Map@100
verified: no, type: dot_accuracy@1
value: 0.6483516483516484
name: Dot Accuracy@1
verified: no, type: dot_accuracy@3
value: 0.7362637362637363
name: Dot Accuracy@3
verified: no, type: dot_accuracy@5
value: 0.7802197802197802
name: Dot Accuracy@5
verified: no, type: dot_accuracy@10
value: 0.8571428571428571
name: Dot Accuracy@10
verified: no, type: dot_precision@1
value: 0.6483516483516484
name: Dot Precision@1
verified: no, type: dot_precision@3
value: 0.2454212454212454
name: Dot Precision@3
verified: no, type: dot_precision@5
value: 0.15604395604395602
name: Dot Precision@5
verified: no, type: dot_precision@10
value: 0.0857142857142857
name: Dot Precision@10
verified: no, type: dot_recall@1
value: 0.6483516483516484
name: Dot Recall@1
verified: no, type: dot_recall@3
value: 0.7362637362637363
name: Dot Recall@3
verified: no, type: dot_recall@5
value: 0.7802197802197802
name: Dot Recall@5
verified: no, type: dot_recall@10
value: 0.8571428571428571
name: Dot Recall@10
verified: no, type: dot_ndcg@10
value: 0.7433871430365133
name: Dot Ndcg@10
verified: no, type: dot_mrr@10
value: 0.7083235217163788
name: Dot Mrr@10
verified: no, type: dot_map@100
value: 0.71581044355863
name: Dot Map@100
verified: no
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Created: 2024-10-12 20:18:22