Sold by: MphasisÂ
Deployed on AWS
Explainable AI solution that identifies algorithmic bias and thereby incorporate AI fairness
Overview
Algorithmic bias is a major concern in models predicting outcomes for people such as loan approval, hiring, committing crime in the future etc. Algorithmic Bias needs to be detected and corrected to ensure that historically disadvantaged groups are not being discriminated against. The solution utilizes bias metrics at different levels of strictness to identify demographic and statistical disparities in groups results.
Highlights
- The solution ascertains whether there is demographic disparity between outcomes for different groups and whether this disparate impact is higher than the legal threshold. The solution also identifies bias from a statistical parity point of view through the two most important metrics of bias: Equal Opportunity difference and Equalized odds difference. This informs whether there is parity in classification of deserving, and undeserving cases.
- The solution helps organizations incorporate Fairness in AI so they can ensure that predictions from AI algorithms do not give undue advantages or disadvantages to any group based on sensitive characteristics such as race, sex, income, age, religious beliefs etc. It also helps in adherence to fairness and equitable treatment requirements of regulatory authorities.
- PACE - ML is Mphasis Framework and Methodology for end-to-end machine learning development and deployment. PACE-ML enables organizations to improve the quality & reliability of the machine learning solutions in production and helps automate, scale, and monitor them. Need customized Machine Learning and Deep Learning solutions? Get in touch!
Details
Sold by
Delivery method
Latest version
Deployed on AWS
Unlock automation with AI agent solutions
Fast-track AI initiatives with agents, tools, and solutions from AWS Partners.
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
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.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $8.00 |
ml.t2.medium Inference (Real-Time) Recommended | Model inference on the ml.t2.medium instance type, real-time mode | $16.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $8.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $8.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $8.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $8.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $8.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $8.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $8.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $8.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA)Â .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
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
It is version 1.4 of the solution
Additional details
Inputs
- Summary
The input should be CSV file, with 3 mandatory columns called
- Pred_y (predicted outcome values)
- Org_y (Ground truth values for outcome)
- Privileged (Sensitive attribute )
- Limitations for input type
- Only binary classification results can be used to detect bias.
- Input MIME type
- text/csv
https://github.com/Mphasis-ML-Marketplace/Explainable-AI-Algorithm-Bias-Detection/tree/main/Input
https://github.com/Mphasis-ML-Marketplace/Explainable-AI-Algorithm-Bias-Detection/tree/main/Input
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Pred_y | Predicted outcome values from the model for which Bias needs to be detected | Type: Integer
Minimum: 0
Maximum: 1 | Yes |
Org_y | Ground truth values used to train the model for which Bias needs to be detected | Type: Integer
Minimum: 0
Maximum: 1 | Yes |
Privileged | Sensitive attribute with one group assigned as privileged for instance white/male and another group assigned as underprivileged for instance black/female) the underprivileged group is given value 0, and privileged group value of 1 | Type: Integer
Minimum: 0
Maximum: 1 | Yes |
Resources
Vendor resources
Support
Vendor support
For any assistance reach out to us at:
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.
Similar products
Mphasis Responsible AI is a comprehensive framework that enables companies to develop and deploy robust, interpretable, explainable, bias-free, auditable and privacy preserving AI through a unique user experience and design thinking engagement. This makes the system trustworthy, thereby improving customer experience, reducing liability risk and ensuring regulatory compliance. We leverage our solution to engage with clients through assessments, workshops and implementations. We focus on specific parts of the most relevant use cases and help define the exact benefits to be derived from the initiative such as greater fairness, accountability, privacy, or algorithmic understanding and interpretability.
An explainable AI solution based on tree based models providing global as well as local explanations
![xCOMET [XL]](https://d7umqicpi7263.cloudfront.net/img/product/342eaa7a-c03a-4d85-b448-4445f7dbced4.png)
xCOMET provides explainable quality estimation for machine translations, offering detailed error detection alongside quality scores.
An explainable AI solution for providing global explanation for structured data models
A software solution to identify opportunities and test for weaknesses in your AI
Customer reviews
No customer reviews yet
Be the first to review this product . We've partnered with PeerSpot to gather customer feedback. You can share your experience by writing or recording a review, or scheduling a call with a PeerSpot analyst.