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{
"metadata": {
"Name": "Model C",
"Provider": "BigCode",
"URL": "https://huggingface.co/bigcode/starcoder2-15b",
"Type": "Large Language Model",
"Modalities": [
"Text-to-Text"
]
},
"scores": {
"1. Bias, Stereotypes, and Representational Harms Evaluation": {
"1.1 Bias Detection Overview": {
"status": "Yes",
"sources": [
{
"type": "🌐",
"detail": "https://arxiv.org/abs/2402.19173",
"name": "BOLD - Bias in Open-ended Language Generation Dataset"
},
{
"type": "🌐",
"detail": "https://arxiv.org/abs/2402.19173",
"name": "WinoBias"
}
],
"questions": {
"Evaluations at various stages (data collection, preprocessing, AI system architecture, training, deployment)": false,
"Have intrinsic properties of the AI system been evaluated for bias (e.g., embedding analysis)": false,
"Have extrinsic bias evaluations been run (e.g., downstream task performance)": true,
"Have evaluations been run across all applicable modalities": true,
"Have bias evaluations been run that take the form of automatic quantitative evaluation": true,
"Have bias evaluations been run with human participants?": false
}
},
"1.2 Protected Classes and Intersectional Measures": {
"status": "No",
"sources": [],
"questions": {
"Do evaluations cover all applicable legal protected categories for in-scope uses of the system?": false,
"Do evaluations cover additional subgroups that are likely to be harmed based on other personal characteristics": false,
"Evaluation of how different aspects of identity interact and compound in AI system behavior": false,
"Evaluation of AI system biases for legal protected categories and additional relevant subgroups": false
}
},
"1.3 Measurement of Stereotypes and Harmful Associations": {
"status": "Yes",
"sources": [
{
"type": "🌐",
"detail": "https://arxiv.org/abs/2402.19173",
"name": "HONEST - Hurtful Sentence Completion in English Language Models"
},
{
"type": "🌐",
"detail": "https://arxiv.org/abs/2402.19173",
"name": "RealToxicityPrompts"
}
],
"questions": {
"Measurement of known stereotypes in AI system outputs": true,
"Measurement of other negative associations and assumptions regarding specific groups": true,
"Measurement of stereotypes and negative associations across in-scope contexts": false
}
},
"1.4 Bias Evaluation Transparency and Documentation": {
"status": "Yes",
"sources": [
{
"type": "🌐",
"detail": "https://arxiv.org/abs/2402.19173",
"name": "Evaluation Documentation"
}
],
"questions": {
"Sufficient documentation of evaluation methods (including code and datasets) to replicate findings": true,
"Sufficient documentation of evaluation results (including intermediary statistics) to support comparison to other AI systems": true,
"Documentation of bias mitigation measures, including their secondary impacts": false,
"Documentation of bias monitoring approaches post-release/deployment if applicable": false
}
}
},
"2. Cultural Values and Sensitive Content Evaluation": {
"2.1 Cultural Variation Overview": {
"status": "N/A",
"sources": [],
"questions": {
"Evaluations at various stages (data collection, preprocessing, AI system architecture, training, deployment)": false,
"Have intrinsic properties of the AI system been evaluated for cultural variation(e.g., embedding analysis)": false,
"Have extrinsic cultural variation evaluations been run (e.g., downstream task performance)": false,
"Have evaluations been run across all applicable modalities": false,
"Have cultural variation evaluations been run that take the form of automatic quantitative evaluation": false,
"Have cultural variation evaluations been run with human participants?": false
}
},
"2.2 Cultural Diversity and Representation": {
"status": "N/A",
"sources": [],
"questions": {
"Use of evaluation methods developed in the cultural contexts in scope": false,
"Respect of indigenous sovereignty, protected rights, and cultural norms in AI system-generated content": false,
"Evaluation of cultural variation across geographic dimensions": false,
"Evaluation of cultural variation representing communities' perspectives within geographical contexts": false,
"Analysis of how cultural context affects AI system performance": false
}
},
"2.3 Generated Sensitive Content across Cultural Contexts": {
"status": "Yes",
"sources": [
{
"type": "🌐",
"detail": "https://arxiv.org/abs/2402.19173",
"name": "HONEST - Hurtful Sentence Completion in English Language Models"
},
{
"type": "🌐",
"detail": "https://arxiv.org/abs/2402.19173",
"name": "RealToxicityPrompts"
}
],
"questions": {
"Has the AI system been evaluated for its likelihood of facilitating generation of threatening or violent content": true,
"Has the AI system been evaluated for its likelihood of facilitating generation of targeted harassment or discrimination": false,
"Has the AI system been evaluated for its likelihood of facilitating generation of hate speech": false,
"Has the AI system been evaluated for its likelihood of exposing its direct users to content embedding values and assumptions not reflective of their cultural context": false,
"Has the AI system been evaluated for its likelihood of exposing its direct users to inappropriate content for their use context": true,
"Has the AI system been evaluated for its likelihood of exposing its direct users to content with negative psychological impacts": false,
"Has the evaluation of the AI system's behaviors explicitly considered cultural variation in their definition": false
}
},
"2.4 Cultural Variation Transparency and Documentation": {
"status": "N/A",
"sources": [],
"questions": {
"Documentation of cultural contexts considered during development": false,
"Documentation of the range of cultural contexts covered by evaluations": false,
"Sufficient documentation of evaluation method to understand the scope of the findings": false,
"Construct validity, documentation of strengths, weaknesses, and assumptions": false,
"Domain shift between evaluation development and AI system development settings": false,
"Sufficient documentation of evaluation methods to replicate findings": false,
"Sufficient documentation of evaluation results to support comparison": false,
"Document of psychological impact on evaluators reviewing harmful content": false,
"Documentation of measures to protect evaluator well-being": false
}
}
},
"3. Disparate Performance Evaluation": {
"3.1 Disparate Performance Overview": {
"status": "N/A",
"sources": [],
"questions": {
"Have development choices and intrinsic properties of the AI system been evaluated for their contribution to disparate performance?": false,
"Have extrinsic disparate performance evaluations been run": false,
"Have evaluations been run across all applicable modalities": false,
"Have disparate performance evaluations been run that take the form of automatic quantitative evaluation": false,
"Have disparate performance evaluations been run with human participants": false
}
},
"3.2 Identifying Target Groups for Disparate Performance Evaluation": {
"status": "N/A",
"sources": [],
"questions": {
"Identification of mandated target group based on legal nondiscrimination frameworks": false,
"Identification of further target groups that are likely to be harmed by disparate performance": false,
"Assessment of systemic barriers in dataset collection methods for different groups": false,
"Consideration of historical disparities in the task in which the AI system is deployed": false,
"Identification of both implicit and explicit markers for the target groups": false
}
},
"3.3 Subgroup Performance Analysis": {
"status": "N/A",
"sources": [],
"questions": {
"Non-aggregated evaluation results across subpopulations, including feature importance and consistency analysis": false,
"Metrics to measure performance in decision-making tasks": false,
"Metrics to measure disparate performance in other tasks including generative tasks": false,
"Worst-case subgroup performance analysis, including performance on rare or underrepresented cases": false,
"Intersectional analysis examining performance across combinations of subgroup": false,
"Do evaluations of disparate performance account for implicit social group markers": false
}
},
"3.4 Disparate Performance Evaluation Transparency and Documentation": {
"status": "N/A",
"sources": [],
"questions": {
"Sufficient documentation of evaluation method to understand the scope of the findings": false,
"Documentation of strengths, weaknesses, and assumptions about the context": false,
"Documentation of domain shift between evaluation and deployment settings": false,
"Sufficient documentation of evaluation methods to replicate findings": false,
"Sufficient documentation of evaluation results to support comparison": false,
"Documentation of disparate performance mitigation measures": false,
"Documentation of disparate performance monitoring approaches": false
}
}
},
"4. Environmental Costs and Carbon Emissions Evaluation": {
"4.1 Environmental Costs Overview": {
"status": "Yes",
"sources": [
{
"type": "🌐",
"detail": "https://mlco2.github.io/impact/#compute",
"name": "Machine Learning Emissions Calculator"
}
],
"questions": {
"Evaluations of different processes within development and deployment": false,
"Have evaluations been run across all applicable modalities?": true,
"Have evaluations been run on standardized benchmarks or metrics?": true,
"Have evaluations taken into account community feedback from regions affected by data center power consumption?": false,
"Do evaluations consider the full supply chain including environmental impact of hardware components and data centers used?": false
}
},
"4.2 Energy Cost and Environmental Impact of Development": {
"status": "Yes",
"sources": [
{
"type": "🌐",
"detail": "https://mlco2.github.io/impact/#compute",
"name": "Machine Learning Emissions Calculator"
}
],
"questions": {
"Accounting of FLOPS across development stages": true,
"Evaluation of energy consumption using standardized tracking tools": true,
"Evaluation of carbon impact accounting for regional energy sources": true,
"Evaluation of hardware lifecycle environmental impact": false
}
},
"4.3 Energy Cost and Environmental Impact of Deployment": {
"status": "N/A",
"sources": [],
"questions": {
"Evaluation of inference FLOPS for the system": false,
"Evaluation of inference energy consumption on most common deployment setting": false,
"Evaluation of inference energy consumption on multiple deployment settings": false,
"Evaluation of task-specific energy consumption variations": false,
"Evaluation of carbon impact for deployment infrastructure": false,
"Evaluation of hardware lifecycle environmental impact for deployment": false
}
},
"4.4 Environmental Costs Transparency and Documentation": {
"status": "Yes",
"sources": [
{
"type": "🌐",
"detail": "https://mlco2.github.io/impact/#compute",
"name": "Machine Learning Emissions Calculator"
}
],
"questions": {
"Documentation about equipment and infrastructure specifications": true,
"Sufficient documentation of evaluation methods including components covered": false,
"Sufficient documentation of evaluation methods to replicate findings": true,
"Sufficient documentation of evaluation results for comparison": true
}
}
},
"5. Privacy and Data Protection Evaluation": {
"5.1 Privacy and Data Protection Overview": {
"status": "Yes",
"sources": [
{
"type": "🏢",
"detail": "PII detection and redaction using an NER model"
},
{
"type": "🌐",
"detail": "https://huggingface.co/spaces/bigcode/in-the-stack",
"name": "Opt-out tool for users"
},
{
"type": "🌐",
"detail": "https://arxiv.org/abs/2402.19173",
"name": "Asleep at the Keyboard Security Benchmark"
}
],
"questions": {
"Evaluations at various stages (data collection, preprocessing, AI system architecture, training, deployment)": true,
"Have intrinsic properties of the AI system been evaluated for privacy vulnerabilities": false,
"Have extrinsic privacy evaluations been run": true,
"Have evaluations been run across all applicable modalities": true,
"Have privacy evaluations been run that take the form of automatic quantitative evaluation": true,
"Have privacy evaluations been run with human participants?": false
}
},
"5.2 Privacy, Likeness, and Publicity Harms": {
"status": "N/A",
"sources": [],
"questions": {
"Has the AI system been evaluated for its likelihood of revealing personal information from its training data?": false,
"Has the AI system been evaluated for its likelihood of facilitating generation of content impersonating an individual?": false,
"Has the AI system been evaluated for its likelihood of providing made up or confabulated personal information about individuals?": false
}
},
"5.3 Intellectual Property and Information Security": {
"status": "Yes",
"sources": [
{
"type": "🏢",
"detail": "Membership test to find if generated code was copied from the training corpus"
},
{
"type": "🏢",
"detail": "Code attribution tool to find the original author and license of the generated code"
},
{
"type": "🌐",
"detail": "https://arxiv.org/abs/2402.19173",
"name": "Asleep at the Keyboard Security Benchmark"
}
],
"questions": {
"Has the AI system been evaluated for its likelihood of reproducing other categories of information from its training data": true,
"Has the system been evaluated for other information security risks for in-scope uses": false
}
},
"5.4 Privacy Evaluation Transparency and Documentation": {
"status": "Yes",
"sources": [
{
"type": "🏢",
"detail": "Documentation of training data information risk categories and consent status"
}
],
"questions": {
"Documentation of the categories of training data that present information risk": true,
"Documentation of evaluation methods to replicate findings": true,
"Documentation of evaluation results to support comparison": true,
"Documentation of evaluation limitations": false,
"Documentation of deployment considerations": false
}
}
},
"6. Financial Costs Evaluation": {
"6.1 Financial Costs Overview": {
"status": "N/A",
"sources": [],
"questions": {
"Evaluation of costs at various stages": false,
"Have costs been evaluated for different system components": false,
"Have cost evaluations been run across all applicable modalities": false,
"Have cost evaluations included both direct and indirect expenses": false,
"Have cost projections been validated against actual expenses": false
}
},
"6.2 Development and Training Costs": {
"status": "N/A",
"sources": [],
"questions": {
"Assessment of research and development labor costs": false,
"Evaluation of data collection and preprocessing costs": false,
"Assessment of training infrastructure costs": false,
"Assessment of costs associated with different training approaches": false,
"Evaluation of model architecture and size impact on costs": false
}
},
"6.3 Deployment and Operation Costs": {
"status": "N/A",
"sources": [],
"questions": {
"Assessment of inference and serving costs": false,
"Evaluation of storage and hosting expenses": false,
"Assessment of scaling costs based on usage patterns": false,
"Evaluation of costs specific to different deployment contexts": false,
"Assessment of costs for model updates or fine-tuning by end users": false
}
},
"6.4 Financial Cost Documentation and Transparency": {
"status": "N/A",
"sources": [],
"questions": {
"Sufficient documentation of cost evaluation methodology and assumptions": false,
"Sufficient documentation of cost breakdowns and metrics": false,
"Documentation of cost variations across different usage scenarios": false,
"Documentation of long-term cost projections and risk factors": false
}
}
},
"7. Data and Content Moderation Labor Evaluation": {
"7.1 Labor Evaluation Overview": {
"status": "Yes",
"sources": [
{
"type": "🏢",
"detail": "PII annotations by human annotators with fair wage"
}
],
"questions": {
"Evaluation of labor practices at various stages": true,
"Have labor conditions been evaluated for different worker categories": true,
"Have labor evaluations been run across all applicable task types": false,
"Have labor practices been evaluated against established industry standards": true,
"Have labor evaluations included both direct employees and contracted workers": false,
"Have evaluations considered different regional and jurisdictional contexts": true
}
},
"7.2 Working Conditions and Compensation": {
"status": "Yes",
"sources": [
{
"type": "🏢",
"detail": "PII annotations by human annotators with fair wage"
}
],
"questions": {
"Assessment of compensation relative to local living wages and industry standards": true,
"Assessment of job security and employment classification": false,
"Evaluation of workplace safety, worker protections and rights": false,
"Assessment of worker autonomy and task assignment practices": false,
"Evaluation of power dynamics and worker feedback mechanisms": false
}
},
"7.3 Worker Wellbeing and Support": {
"status": "N/A",
"sources": [],
"questions": {
"Assessment of psychological support systems, trauma resources, and other long-term mental health monitoring": false,
"Evaluation of training and preparation for difficult content": false,
"Evaluation of cultural and linguistic support for diverse workforces": false
}
},
"7.4 Labor Practice Documentation and Transparency": {
"status": "Yes",
"sources": [
{
"type": "🏢",
"detail": "PII annotations by human annotators with fair wage"
}
],
"questions": {
"Documentation of labor evaluation methodology and frameworks used": true,
"Documentation of worker demographics and task distribution": false,
"Documentation of support systems, worker protections": false,
"Documentation of incident reporting and resolution procedures": false
}
}
}
}
}
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