Podcast
Questions and Answers
How does high-quality data impact AI-driven decisions?
How does high-quality data impact AI-driven decisions?
- High-quality data improves the reliability and credibility of AI-driven decisions, fostering trust among users. (correct)
- High-quality data increases the complexity of AI models without any benefits.
- High-quality data is irrelevant to AI system performance.
- High-quality data leads to unreliable predictions, creating distrust among users.
Which data quality dimension is primarily affected by variations in state and country values?
Which data quality dimension is primarily affected by variations in state and country values?
- Timeliness
- Consistency (correct)
- Relevancy
- Completeness
What is an effective strategy for companies to minimize potential AI bias?
What is an effective strategy for companies to minimize potential AI bias?
- Use demographic data to identify minority groups.
- Focus solely on profit-driven AI solutions without ethical considerations.
- Develop models that automatically obscure sensitive information.
- Integrate AI models that auto-correct biased data. (correct)
What should developers prioritize to ensure ethical AI practices?
What should developers prioritize to ensure ethical AI practices?
Which element is crucial for fostering trust in AI systems?
Which element is crucial for fostering trust in AI systems?
What does inconsistency in data signify?
What does inconsistency in data signify?
What role does Salesforce's Trusted AI Principles play in AI development?
What role does Salesforce's Trusted AI Principles play in AI development?
Why is high-quality data particularly important for AI systems?
Why is high-quality data particularly important for AI systems?
What issue can arise from poor data quality in AI systems?
What issue can arise from poor data quality in AI systems?
Which statement accurately describes user consent in AI data privacy?
Which statement accurately describes user consent in AI data privacy?
What aspect of data quality should be evaluated to address customers receiving excessive marketing communications?
What aspect of data quality should be evaluated to address customers receiving excessive marketing communications?
What should a developer prioritize when selecting a dataset to train an AI model for predicting customer behavior?
What should a developer prioritize when selecting a dataset to train an AI model for predicting customer behavior?
What is a potential risk when AI systems amplify existing biases in data?
What is a potential risk when AI systems amplify existing biases in data?
Which of the following is NOT a characteristic of poor data quality?
Which of the following is NOT a characteristic of poor data quality?
Why is ensuring user consent vital in AI applications regarding personal data?
Why is ensuring user consent vital in AI applications regarding personal data?
What can data duplication lead to in communication with customers?
What can data duplication lead to in communication with customers?
What should be the first step for Cloud Kicks to prepare its data for AI optimization?
What should be the first step for Cloud Kicks to prepare its data for AI optimization?
How does data quality impact AI applications in healthcare?
How does data quality impact AI applications in healthcare?
Which ethical challenge is linked to the development of AI?
Which ethical challenge is linked to the development of AI?
What is a critical action for mitigating bias in machine learning algorithms?
What is a critical action for mitigating bias in machine learning algorithms?
What is the role of data availability in AI projects?
What is the role of data availability in AI projects?
What is a common misconception about AI algorithms in relation to bias?
What is a common misconception about AI algorithms in relation to bias?
Why is it important to ensure privacy and compliance in AI applications?
Why is it important to ensure privacy and compliance in AI applications?
What outcome can poor data quality lead to in AI decision-making?
What outcome can poor data quality lead to in AI decision-making?
Flashcards
Data Quality Impact on AI Trustworthiness
Data Quality Impact on AI Trustworthiness
High-quality data improves the reliability and credibility of AI-driven decisions, building user trust.
Data Quality Dimension: Consistency
Data Quality Dimension: Consistency
Data values should be uniform and follow standards across records. Variations like multiple state values are a consistency issue.
AI Bias Mitigation
AI Bias Mitigation
Using guidelines and best practices for fair and ethical AI development to avoid bias.
Trusted AI Principles
Trusted AI Principles
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High-Quality Data Characteristics
High-Quality Data Characteristics
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AI Data Quality
AI Data Quality
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Inconsistent Data
Inconsistent Data
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Impact of Data on AI Decisions
Impact of Data on AI Decisions
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AI Data Preparation for Business
AI Data Preparation for Business
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Data Quality in Healthcare AI
Data Quality in Healthcare AI
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AI Ethical Challenges
AI Ethical Challenges
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AI Data Availability
AI Data Availability
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AI Data Outcomes
AI Data Outcomes
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AI Bias
AI Bias
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Data Quality Importance (Healthcare)
Data Quality Importance (Healthcare)
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AI Data Relevance
AI Data Relevance
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Bias in AI data
Bias in AI data
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Data quality impact on AI
Data quality impact on AI
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User consent and AI
User consent and AI
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Data duplication
Data duplication
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Dataset selection for AI
Dataset selection for AI
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AI data privacy
AI data privacy
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Communication Inefficiencies
Communication Inefficiencies
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AI Model Accuracy
AI Model Accuracy
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Study Notes
Salesforce Al Honesty Guidelines
- Minimize Al model's carbon footprint and environmental impact during training.
- Ensure appropriate consent and transparency when using Al-generated responses.
- Control bias, toxicity, and harmful content with embedded guardrails and guidance.
Cloud Kicks Al Capacity
- Salesforce Einstein Lead Scoring and Case Classification
Data Quality in Al Implementation
- Data's role in training and fine-tuning Salesforce Al models is a key consideration.
Preventing Bias in Al
- Use diverse training data.
Assessing Data Quality for Einstein Prediction Builder
- Leverage data quality apps from AppExchange.
Developing Trusted Al
- Create guardrails that mitigate toxicity and protect PII (Personally Identifiable Information).
- Be transparent when Al has created and automatically delivered content.
Privacy Concerns with Al and CRM Data
- Critical to consider privacy concerns when dealing with Al and CRM data.
Role of Data Quality in Ethical Al
- High-quality data is essential for ensuring unbiased and fair Al decisions, preventing discrimination, and promoting ethical use.
Key Components of Data Quality Standard
- Accuracy
- Completeness
- Consistency
Predictive vs Generative Al
- Predictive Al uses machine learning to classify or predict outputs from its input data.
- Generative Al uses machine learning to generate new and original output for a given input.
Al Bias
- Societal bias imposes a system's values on others.
Al Business Objectives
- Data quality is required to create accurate Al data insights.
Outcomes of Poor Al Data Quality
- Al model training becomes slower and less efficient.
- Al models may produce biased or erroneous results.
Benefits of Data Quality and Transparency
- Chances of bias are mitigated.
Salesforce Al Applications
- Lead scoring and opportunity forecasting
- Sales dashboards and reporting
- Data modeling and management
Al CRM Customer Interactions
- Provides call summaries to help understand customer interactions
Salesforce Al Ethics (Responsible)
- Safeguarding human rights and data protection
Salesforce Al Empowerment
- Augmenting human capabilities
Al Algorithmic Bias
- Equitable treatment by Al systems
Al Transparency
- Ensuring users understand the reasoning behind Al recommendations
Factors affecting Al Data Quality
- Age and consistency of data
- Volume and granularity of data
- Accuracy, completeness, and uniqueness of data
- Source and timeliness of data
Salesforce Al Business Objectives
- Data is required to create accurate Al data insights.
Data Quality Factors
- Accuracy, completeness, consistency, and relevance of data
- Age of the data and its timeliness
- Volume of data
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Description
Explore the essential guidelines for implementing AI in Salesforce, focusing on minimizing environmental impact, ensuring data quality, and preventing bias. This quiz will cover approaches to developing trusted AI systems while addressing privacy concerns and strategies for effective lead scoring and case classification.
