Sold by: aequumAI LLC
Deployed on AWS
STRATA estimates 5-class race/ethnicity probabilities from name and US address for aggregate fair-lending and disparity analysis. Deploys in your AWS account with per-record uncertainty scores.
Overview
What STRATA Does
STRATA estimates the most likely race/ethnicity of individuals from name and US address for aggregate, population-level analysis such as HMDA/ECOA/CRA fair-lending monitoring, housing-equity research, and disparity studies. It is not licensed for decisions about identified or identifiable individuals - see the EULA for permitted-use and prohibited-use terms.
The model uses a published char-LSTM + geography architecture refined by gradient-boosted soft-voting, improving on traditional BISG/BIFSG approaches with lower false-positive bias. A geography waterfall selects the best-supported tier per record (block-group+tract, tract, ZCTA, or name-only) and reports the lookup level used.
Output Fields
Each scored record returns:
- Calibrated probabilities for five classes: White, Black, Hispanic, Asian, Other
- Argmax label (most likely class)
- Per-record uncertainty index (IU) - addresses a known BISG limitation where no per-record confidence is available
- Privacy-preserving cohort tokens (cohort_tract, cohort_zcta) for geographic grouping without exposing census identifiers
- Lookup level and quality codes indicating which geographic tier was resolved
Resolved geography is withheld from output to protect privacy.
How It Improves on BISG/BIFSG
- Character-level deep learning captures name-origin signals that surname-list approaches miss
- Geography waterfall uses the most granular available tier rather than a single fixed level
- Per-record uncertainty (IU) lets you flag low-confidence predictions for review - standard BISG provides no such signal
- Lower false-positive bias as documented in the published methodology the model reproduces
Privacy and Security
STRATA deploys as a SageMaker real-time endpoint or Batch Transform entirely within your AWS account. Your input PII never egresses to the vendor - the vendor never sees your data. The model inherits your existing AWS security posture including encryption in transit (TLS) and at rest (S3 server-side encryption for model artifacts). You control VPC configuration, IAM permissions, and network isolation.
Deployment Notes
- Model package size: approximately 1.45 GB
- Cold-start time: approximately 3 minutes for real-time endpoints
- Once InService: inference is fast
- Batch Transform: recommended for one-off or batch scoring to avoid standing hourly endpoint costs
Getting Started
After subscribing, deploy the model package to a SageMaker endpoint or configure a Batch Transform job. Input records require name and US address fields. Contact the aequumAI team at the support email to request a methodology walkthrough or discuss a pilot scoring engagement for your use case.
About aequumAI
aequumAI builds privacy-preserving demographic estimation tools for regulated industries. STRATA reproduces a published methodology designed specifically for fair-lending compliance workflows where accuracy, calibration, and auditability are critical requirements. Methodology reference: S. Chalavadi, A. Pastor, and T. Leitch, arXiv:2504.21259 (https://arxiv.org/abs/2504.21259 ).
Highlights
- Beyond BISG/BIFSG: STRATA's char-LSTM + geography waterfall captures name-origin signals that surname-list approaches miss, delivering lower false-positive bias as documented in the published methodology it reproduces. Each record includes a per-record uncertainty index (IU) - a confidence signal that standard BISG cannot provide - so you can flag low-confidence predictions for review rather than treating all estimates equally.
- Privacy by architecture: Deploys entirely in your AWS account as a SageMaker real-time endpoint or Batch Transform. Your input PII never leaves your account and the vendor never sees your data. Resolved geography is withheld from output; privacy-preserving anonymous cohort tokens (cohort_tract, cohort_zcta) let you group results by geography without exposing census identifiers.
- Licensed for aggregate fair-lending and disparity analysis ONLY (HMDA/ECOA/CRA monitoring, housing-equity research) - not for individual decisions. Output includes calibrated 5-class probabilities (White, Black, Hispanic, Asian, Other), argmax label, uncertainty index, cohort tokens, and lookup-level quality codes. See EULA for permitted-use and prohibited-use terms.
Details
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $13.58 |
ml.m5.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $13.58 |
ml.m5.2xlarge Inference (Real-Time) | Model inference on the ml.m5.2xlarge instance type, real-time mode | $24.33 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $24.33 |
Dimensions summary
You pay by the hour for each host running the race/ethnicity proxy model. Pricing is billed per host-hour (HostHrs) based on how long the inference runs. You choose from two instance sizes: ml.m5.xlarge and the larger-capacity ml.m5.2xlarge. Each size comes in two run modes. Real-time mode returns results as requests arrive. Batch mode processes grouped records at once. This gives you four combinations to match your compute needs. There is no upfront commitment; you pay only for the hours you use.
Top-of-mind questions for buyers
Both run the same race/ethnicity proxy model. The ml.m5.2xlarge instance offers more compute capacity per host than the ml.m5.xlarge. You pick based on how much throughput you need. Both bill per host-hour, so the larger instance costs more per hour it runs.
Charges accrue per host-hour while the instance runs. If you fully stop a host, software charges stop too. Underlying AWS infrastructure or storage fees may still apply while resources exist. The software meters running time only, not idle or powered-off periods.
Real-time mode keeps a host running to return results as requests arrive, suiting live scoring. Batch mode processes grouped records together, suiting large one-time or scheduled jobs. Both bill per host-hour, so cost depends on how long each mode keeps a host active.
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Vendor refund policy
Software charges are metered per instance-hour through AWS Marketplace. Contact terry@aequum.ai within thirty (30) days of an invoice for a billing adjustment for a verified duplicate charge, metering error, or material inability to use the product caused by a confirmed defect; approved adjustments are issued through AWS Marketplace. AWS infrastructure charges are governed solely by AWS.
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Initial release of STRATA (M8-BGT). National US name+address race/ethnicity probabilities (5-class) with per-record uncertainty (IU), waterfall tier selection, and privacy-preserving anonymous cohort tokens. Real-time endpoint or Batch Transform.
Additional details
Inputs
- Summary
CSV with a header row; one individual per row. Columns: fname,mname,lname,address,city,state,zip5. fname and lname are required; mname optional. The name drives the character-LSTM; the US address adds geographic signal via an offline resolver. Any extra columns (e.g. a record id) pass through unchanged. US scope.
- Input MIME type
- text/csv
customer_id,fname,mname,lname,address,city,state,zip5
A001,MARIA,,GARCIA,1600 Pennsylvania Ave NW,Washington,DC,20500
A002,WEI,,CHEN,1 Infinite Loop,Cupertino,CA,95014
A003,JAMES,R,SMITH,233 S Wacker Dr,Chicago,IL,60606
A004,AISHA,,OKAFOR,1350 Avenue of the Americas,New York,NY,10019
A005,CARLOS,A,RODRIGUEZ,845 N Michigan Ave,Chicago,IL,60611
customer_id,fname,mname,lname,address,city,state,zip5
A001,MARIA,,GARCIA,1600 Pennsylvania Ave NW,Washington,DC,20500
A002,WEI,,CHEN,1 Infinite Loop,Cupertino,CA,95014
A003,JAMES,R,SMITH,233 S Wacker Dr,Chicago,IL,60606
A004,AISHA,,OKAFOR,1350 Avenue of the Americas,New York,NY,10019
A005,CARLOS,A,RODRIGUEZ,845 N Michigan Ave,Chicago,IL,60611
Support
Vendor support
Contact Support
For questions about deployment, model methodology, or permitted use, contact the aequumAI team at terry@aequum.ai .
Support Scope
Support covers assistance with SageMaker endpoint deployment, Batch Transform configuration, input/output format questions, model interpretation guidance, and licensing clarification.
For refund requests, contact the same email address with your AWS account ID and subscription details.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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