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Get Smart with Salesforce Einstein

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  1. Salesforce Einstein Basics

    Get Started with Einstein
  2. Get Started with Einstein
    7 Topics
  3. Learn About Einstein Out-Of-The-Box Applications
    7 Topics
  4. Responsible Creation of Artificial Intelligence
    Use the Einstein Platform
    9 Topics
  5. Understand the Ethical Use of Technology
    8 Topics
  6. Learn the Basics of Artificial Intelligence
    5 Topics
  7. Recognize Bias in Artificial Intelligence
    6 Topics
  8. Einstein Bots Basics
    Remove Bias from Your Data and Algorithms
    6 Topics
  9. Learn About Einstein Bots
    6 Topics
  10. Plan Your Bot Content
    4 Topics
  11. Einstein Next Best Action
    Learn the Prerequisites and Enable Einstein Bots
    3 Topics
  12. Get Started with Einstein Next Best Action
    9 Topics
  13. Sales Cloud Einstein
    Understand How Einstein Next Best Action Works
    7 Topics
  14. Increase Sales Productivity
    5 Topics
  15. Automate Sales Activities
    5 Topics
  16. Target the Best Leads
    3 Topics
  17. Close More Deals
    6 Topics
  18. Connect with Your Customers and Create New Business
    4 Topics
  19. Sales Cloud Einstein Rollout Strategies
    Improve Sales Predictions
    4 Topics
  20. Use AI to Improve Sales
  21. Start with a Plan
  22. Set Goals and Priorities
  23. Get Ready for Einstein
  24. Quick Start: Einstein Prediction Builder
    Start Using Sales Cloud Einstein
  25. Sign Up for an Einstein Prediction Builder Trailhead Playground
  26. Create a Formula Field to Predict
  27. Enrich Your Prediction
  28. Build a Prediction
  29. Quick Start: Einstein Image Classification
    Create a List View for Your Predictions
  30. Get an Einstein Platform Services Account
  31. Get the Code
  32. Create a Remote Site
  33. Create the Apex Classes
  34. Einstein Intent API Basics
    Create the Visualforce Page
  35. Get Started with Einstein Language
  36. Set Up Your Environment
  37. Create the Dataset
  38. Train the Dataset and Create a Model
  39. Put Predictions into Action with Next Best Action
    Use the Model to Make a Prediction
  40. Learn the Basics and Set Up a Custom Playground
  41. Define and Build a Prediction
  42. Customize Your Contact and List Displays
  43. Create Recommendations for Einstein Next Best Action
  44. Create a Next Best Action Strategy
  45. Add Next Best Action to Your Contacts
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Research shows that diverse teams (across the spectrum of experience, race, gender, and ability) are more creative, diligent, and hardworking. An organization that includes more women at all levels, especially top management, typically has higher profits. To learn more, check out the Resources section at the end of this unit.

 Diverse Teams

Everything we create represents our values, experiences, and biases. For example, facial recognition systems often have more difficulty identifying black or brown faces than white faces. If the teams creating such technology had been more diverse, they would have been more likely to have recognized and addressed this bias.

Development teams should strive toward diversity in every area, from age and race to culture, education, and ability. Lack of diversity can create an echo chamber that results in biased products and feature gaps. If you are unable to hire diverse team members, consider seeking feedback from underrepresented groups across your company and user base.

A sense of community is also part of the ethical groundwork at a company. No one person should be solely responsible for acting ethically or promoting ethics. Instead, the company as a whole should be mindful and conscious of ethics. Employees should feel comfortable challenging the status quo and speaking up, which can identify risks for your business. Team members should ask ethical questions specific to their domains, such as:

  • Product managers: What is the business impact of a false positive or false negative in our algorithm?
  • Researchers: Who is impacted by our system and how? How can it be abused? How can people try to break the product or use it in unintended ways? What is the social context in which this is used?
  • Designers: What defaults or assumptions am I building into the product? Am I designing this for transparency and equality?
  • Data scientists: What are the implications for users when I optimize my model this way?
  • Content writers: Can I explain why the system made a prediction, recommendation, or decision in terms the user can understand?
  • Engineers: What notifications, processes, checks, or failsafes can we build into the system to mitigate harm?

Development teams

Notice that these questions involve the perspectives of multiple roles. Involving stakeholders and team members at every stage of the product development lifecycle helps correct the impact of systemic social inequalities in your system. If you find yourself on a team that’s missing any of these roles, or where you play multiple roles, you may need to wear multiple hats to ensure each of these perspectives is included—and that may involve seeking out external expertise or advice. When employees are dissatisfied with the answers they receive, there should be a clear process for resolving the problem areas, like a review board. We go into more detail on that later.