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Scale Your MRR: Subscription Management For Small Business Streamlining Commerce Media Ad Inventory Management 12 AI Sales Strategies for Startups That Actually Work Sell Smarter: Ecommerce Metrics To Track For Your Small Business Shop Apply the Orchestration Density Framework to Your Next Automation Decision Wait, Black Friday Planning In Spring? 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Model Cards for AI Model Transparency
Kathy Baxter, Yoav Schlesinger · 2026-05-29 · via Salesforce

Originally published September 29, 2020
Last updated: May 28, 2026

At Salesforce, we take seriously our mission to create and deliver AI technology that is responsible, accountable, transparent, empowering, and inclusive. These principles ensure that our AI is safe, ethical, and engenders trust. We commit ourselves to:

  • Asking not only can we do something but also should we do it, before building any AI features.
  • Providing clear and understandable explanations of our AI’s predictions or recommendations. When an AI function makes a prediction or recommendation, we will provide explanations of the model.
  • Outlining the intended applications and potential impacts of AI capabilities.

One key element of our commitment to Transparency is the production of model cards. Based on pioneering work done by Google, model cards seek to standardize documentation procedures to communicate the performance characteristics of trained machine learning (ML) and artificial intelligence (AI) models. Think of them as a sort of nutrition label, designed to provide critical information about how our models work — including inputs, outputs, the conditions under which models work best, and ethical considerations in their use.

Providing this level of transparency is intended to help developers, customers, journalists, and industry analysts, policymakers, advocacy groups, and consumers better understand the impact of our AI on individuals, communities, and society.

The following model cards are currently available for reference:

Einstein Platform
Optical Character Recognition (OCR)

Salesforce AI Research
CTRL
Detector for CTRL
AI Economist: Foundation (Simulation Framework)
COVID-19 US Pandemic and Economic Simulation
CodeT5
BLIP2

Marketing Cloud Einstein
Send Time Optimization
Send Time Optimization for Mobile
Messaging Insights
Engagement Frequency
Engagement Scoring for Email
Engagement Scoring for Mobile
Copy Insights Subject Line Tester
Data Cloud Auto Mapping
Content Selection and Content Testing
Email Recommendations and Web Recommendations
Probabilistic Opens
Subject Line Tester

Sales Cloud Einstein
Conversational Insights Model
Opportunity Scoring

Tableau CRM
Detect Sentiment

We now provide a model card generator in Einstein Discovery, which enables our customers to generate their own model cards, as well.

Image of predicted churn model manager with model card option highlighted.

We look forward to sharing these with you and welcome your feedback, reactions and questions.

The most updated version of our model cards can be found on our Compliance page.

As principal architect of ethical AI practice at Salesforce, Kathy develops research-informed best practice to educate Salesforce employees, customers, and the industry on the development of responsible AI. She collaborates and partners with external AI and ethics experts to continuously evolve Read More

More by Kathy

Yoav Schlesinger

Yoav Schlesinger Architect, Responsible AI & Tech, Office of Ethical and Humane Use

I am an Architect of Responsible AI and Tech in Salesforce's Office of Ethical and Humane Use, where I help operationalize ethical principles in both product development and deployment, including risk evaluation and mitigation, testing and red teaming, and governance more broadly.

More by Yoav