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AI FactSheet Please provide details regarding your Artificial Intelligence (AI) product by filling out the FactSheet template below.
Vendor Name
System Name
| Overview |
| Brief summary of the AI system. |
| Purpose |
| What function does the AI system perform, and for what purpose? If the system performs multiple functions, list each discretely and reference below. For features that are configurable, please describe all configuration options and default settings. |
| Intended Domain |
| What domain is the AI system intended to be applied in? |
| Training Data |
| How was the AI system trained? What data was used? How often is data added to the training set? Was all training data legally obtained and its use fully licensed? |
| Test Data |
| What data was used to test system performance? Under what conditions has the system been tested? |
| Model Information |
| General description of the model(s) used (e.g., large language model, transformer, deep learning, supervised learning, built on an existing open source model, computer vision) |
| Update procedure |
| In general, how often are the models updated for users? Will the user have a choice in moving to the updated model or staying on the current model? What documentation is available for new versions of the model? |
| Inputs and Outputs |
| What are the inputs to the AI system? What are its outputs? What interfaces and integrations are supported? |
| Performance Metrics |
| What are the performance metrics? What is your current level of performance on these metrics? How can the user monitor performance in the deployment environment? |
| Bias |
| What biases does the tool exhibit and how does it handle that bias? This can include but is not limited to biases on human factors such as gender, race, socioeconomic status, disability, culture, age, or other protected classes, or biases on general factors such as a sampling bias, survivorship bias, detection bias, or observer bias. |
| Robustness |
| How does the AI system handle outliers? Do overwritten decisions feed back into the system to help calibrate it in the future? |
| Optimal Conditions |
| What conditions does the model perform best under? Are there minimum requirements for the quantity of records/observations? |
| Poor Conditions |
| What conditions does the model perform poorly under? What are the limitations of the AI system? What kinds of errors can it make (e.g., hallucinations) and what conditions make those errors more likely? |
| Explanation |
| How does the AI system explain its predictions? Are the outcomes of the AI system understandable by subject matter experts, users, impacted individuals, and others? |
| Jurisdiction-specific Considerations |
| Please describe any considerations relevant to local, state, industry, or other specific jurisdictional regulations. |
Impact Assessment Questionnaire
| How is the AI tool monitored to identify any problems in usage? Can outputs (recommendations, predictions, etc.) be overwritten by a human, and do overwritten outputs help calibrate the system in the future? |
| Problems in usage can include false negatives, false positives, bias, hallucinations, and human-reported quality issues (such as poor translations or poorly generated images). |
How is bias managed effectively?
This can include ways to monitor bias, or abilities to toggle parameters to change observed bias in the model.
| Have the vendors or an independent party conducted a study on the bias, accuracy, or disparate impact of the system? If yes, can the City of San José review the study? Include methodology and results. |
| This can include bias impact reports, algorithmic impact reports, or others.[footnoteRef:2] [2: See “Algorithmic bias detection and mitigation: Best practices and policies to reduce consumer harms” for an example bias impact report template: https://www.brookings.edu/articles/algorithmic-bias-detection-and-mitigation-best-practices-and-policies-to-reduce-consumer-harms/. ] |
| How can the City of San José and its partners flag issues related to bias, discrimination, or poor performance of the AI system? |
| This can include ways to report inaccurate or concerning decisions/classifications made by the AI system, or ways to retroactively review past system actions. |
| How has the Human-Computer Interaction aspect of the AI tool been made accessible, such as to people with disabilities? |
| Has it been assessed against any usability standards, and if so what was the result? |
| Please share any relevant information, links, or resources regarding your organization’s responsible AI strategy. |
| URL to any broad AI policy or strategy. |