Podcast
Questions and Answers
Which customer information should be accessible on the Salesforce Platform for transparency regarding predictions made by Prediction Builder?
Which customer information should be accessible on the Salesforce Platform for transparency regarding predictions made by Prediction Builder?
Which Salesforce field type is most appropriate for capturing a customer’s preferred name?
Which Salesforce field type is most appropriate for capturing a customer’s preferred name?
What type of bias occurs when data is labeled based on stereotypes?
What type of bias occurs when data is labeled based on stereotypes?
Which attribute is classified as an example of an immutable trait?
Which attribute is classified as an example of an immutable trait?
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What distinguishes generative AI from predictive AI?
What distinguishes generative AI from predictive AI?
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What is a potential outcome of maintaining poor data quality in AI models?
What is a potential outcome of maintaining poor data quality in AI models?
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Which field type should a Salesforce administrator select to capture an order's destination country while ensuring data quality?
Which field type should a Salesforce administrator select to capture an order's destination country while ensuring data quality?
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What does user consent imply regarding AI data privacy?
What does user consent imply regarding AI data privacy?
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What is an essential data quality dimension for predicting shoe demand based on historical sales data?
What is an essential data quality dimension for predicting shoe demand based on historical sales data?
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How can a financial institution implement Salesforce's Trusted AI Principle of Transparency?
How can a financial institution implement Salesforce's Trusted AI Principle of Transparency?
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What is a key challenge of human-AI collaboration in decision-making?
What is a key challenge of human-AI collaboration in decision-making?
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Which statement best describes the difference between predictive AI and generative AI?
Which statement best describes the difference between predictive AI and generative AI?
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What best defines machine learning?
What best defines machine learning?
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Which Salesforce Ethical Maturity Model should the Cloud Kicks team use to develop trusted AI solutions?
Which Salesforce Ethical Maturity Model should the Cloud Kicks team use to develop trusted AI solutions?
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What is a potential source of bias in training data for AI models?
What is a potential source of bias in training data for AI models?
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What is one of the goals of empowering users to build AI applications?
What is one of the goals of empowering users to build AI applications?
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Which data quality dimension should be assessed to reduce communication inefficiencies?
Which data quality dimension should be assessed to reduce communication inefficiencies?
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What is a potential outcome of using poor-quality data in AI applications?
What is a potential outcome of using poor-quality data in AI applications?
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What role does data quality play in the ethical use of AI applications?
What role does data quality play in the ethical use of AI applications?
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Which field of AI is most suitable for implementing a chatbot to deflect incoming customer inquiries?
Which field of AI is most suitable for implementing a chatbot to deflect incoming customer inquiries?
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What data does Salesforce automatically exclude from Marketing Cloud Einstein engagement model training to mitigate bias?
What data does Salesforce automatically exclude from Marketing Cloud Einstein engagement model training to mitigate bias?
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Which statement exemplifies Salesforce's honesty guideline when training AI models?
Which statement exemplifies Salesforce's honesty guideline when training AI models?
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What are some of the ethical challenges associated with AI development?
What are some of the ethical challenges associated with AI development?
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What is one way to achieve transparency in AI?
What is one way to achieve transparency in AI?
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What should a company prioritize first when preparing its data for AI?
What should a company prioritize first when preparing its data for AI?
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In the scenario of Cloud Kicks recommending shoes based on purchase history, what type of bias is most likely encountered?
In the scenario of Cloud Kicks recommending shoes based on purchase history, what type of bias is most likely encountered?
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What is a key characteristic of machine learning within AI capabilities?
What is a key characteristic of machine learning within AI capabilities?
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Which of the following is a commonly used example of AI in customer relationship management (CRM)?
Which of the following is a commonly used example of AI in customer relationship management (CRM)?
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How does testing with diverse and representative datasets align with inclusivity in AI development?
How does testing with diverse and representative datasets align with inclusivity in AI development?
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How will incomplete data quality impact Cloud Kicks' product recommendations?
How will incomplete data quality impact Cloud Kicks' product recommendations?
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What method can quickly assess data quality for Einstein Prediction Builder?
What method can quickly assess data quality for Einstein Prediction Builder?
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Which type of AI can enhance customer service agents' email responses by analyzing the written content of previous emails?
Which type of AI can enhance customer service agents' email responses by analyzing the written content of previous emails?
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What is a benefit of a diverse, balanced, and large dataset?
What is a benefit of a diverse, balanced, and large dataset?
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What are predictive analytics, machine learning, natural language processing (NLP), and computer vision?
What are predictive analytics, machine learning, natural language processing (NLP), and computer vision?
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What is one technique to mitigate bias and ensure fairness in AI applications?
What is one technique to mitigate bias and ensure fairness in AI applications?
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Which Einstein feature should Cloud Kicks use for automated chat as its primary support channel?
Which Einstein feature should Cloud Kicks use for automated chat as its primary support channel?
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In the context of Salesforce's Trusted AI Principles, what does the principle of Responsibility primarily focus on?
In the context of Salesforce's Trusted AI Principles, what does the principle of Responsibility primarily focus on?
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What are the potential consequences of an organization suffering from poor data quality?
What are the potential consequences of an organization suffering from poor data quality?
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Which type of records negatively impact data quality?
Which type of records negatively impact data quality?
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Study Notes
AI in Salesforce
- Empower users at all skill levels to develop AI applications without writing code.
- Use AI models to predict shoe demand based on sales and regional data.
- Ensure data is reliable for accurate predictions.
- Communicate how factors like credit scores impact customers.
- Flag sensitive variables to avoid biased lending practices.
Trusted AI Principles
- Transparency: Explain how a model works and communicate its rationale.
- Fairness: Ensure unbiased decisions and avoid discrimination.
- Accountability: Ensure responsible use of AI.
AI Challenges and Considerations
- AI reliance may decrease human critical thinking and oversight.
- Predictive AI: Analyzes data to predict outcomes.
- Generative AI: Generates new output based on data.
- Machine Learning: AI that learns from data and improves over time.
Ethical AI Maturity Model
- Ethical AI Process Maturity Model: Use this framework to guide the development of trusted AI solutions.
Data Quality and Bias in AI
- Bias: Training data may reflect existing societal biases.
- Data Quality: Data quality impacts AI model accuracy, fairness, and performance.
- Using low-quality data can result in biased results.
- High-quality data promotes unbiased decisions and prevents discrimination.
AI Applications in Salesforce
- Use NLP to build chatbots that answer common customer questions.
- Salesforce automatically excludes sensitive demographic data from AI model training.
Building Ethical AI Models
- Use safeguards to prevent biased content.
- Ensure consent and transparency when using generated responses.
- Minimize the model’s environmental impact during training.
AI Transparency
- Transparency: Provide explanations for AI predictions to users.
- Fairness: Train AI models using diverse datasets.
Understanding AI Concepts
- Predictive AI: Analyzes existing data to predict outcomes.
- Generative AI: Creates new content based on existing data.
Data Fields in Salesforce
- Use the Text field type for capturing preferred names on customer records.
Bias in AI Training Data
- Societal Bias: Stems from data labeled according to stereotypes.
Immutable Traits and Data Privacy
- Immutable Traits: Avoid using immutable traits unnecessarily in AI applications, such as financial status.
- Poor data quality can lead to AI systems learning and amplifying bias.
Data Types in Salesforce
- Use the Picklist field type to ensure data quality when capturing order destination countries.
User Consent and AI Data Privacy
- User consent is crucial for responsible AI use.
Preparing Data for AI Implementations
- Step 1: Determine data availability.
- Step 2: Assess data quality.
Bias in Recommendation Engines
- Confirmation Bias: Recommendations may reinforce existing customer preferences.
AI Capabilities
- Machine Learning: Uses algorithms to learn from data and make decisions.
- Natural Language Processing (NLP): Processes and understands human language.
AI Applications in CRM
- Predictive Scoring: Predict customer behavior.
- Forecasting: Predict sales and demand.
- Recommendations: Generate personalized product recommendations.
Building Inclusive AI Solutions
- Test AI models with diverse datasets to ensure inclusivity.
Impacts of Data Quality
- Incomplete data can hinder the accuracy of product recommendations.
Assessing Data Quality
- Use reports to quickly assess data quality.
AI in Customer Service
- Use NLP to personalize email responses for customers.
AI Datasets
- Large, diverse, and balanced datasets improve model accuracy.
Understanding AI Concepts
- Predictive Analytics: Analyze data to predict future events.
- Machine Learning: AI that learns from data and improves over time.
- Natural Language Processing (NLP): Processes and understands human language.
- Computer Vision: Enables AI to interpret and analyze visual data.
Mitigating Bias in AI
- Auditing and Monitoring: Monitor AI systems for bias.
Salesforce AI Features
- Einstein Bots: Implement automated chat features.
AI Responsibility
- Responsibility: Ensure ethical AI use.
Consequences of Poor Data Quality
- Poor data quality can result in revenue loss, poor customer service, and reputational damage.
AI for Customer Engagement
- Personalized Emails: Use AI to personalize email campaigns for better engagement.
Data Quality Issues
- Duplicate Records: Duplicate records negatively impact data quality.
Data Quality for AI Effectiveness
- Consistency: Ensure data is consistent across all systems.
Preparing Data for AI Models
- Standardize Data: Standardize data to improve the effectiveness of AI models.
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Description
Explore the integration of AI in Salesforce to predict outcomes and enhance user capabilities without coding. Understand the principles of transparency, fairness, and accountability in AI applications and the importance of ethical considerations in predictive and generative AI models.
