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
Guidelines and best practices to develop and use AI ethically.
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High-Quality Data Characteristics
High-Quality Data Characteristics
Accurate, complete, consistent, relevant, and timely data for the AI task.
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AI Data Quality
AI Data Quality
Refers to the accuracy, completeness, and consistency of data used for training and operating AI models.
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Inconsistent Data
Inconsistent Data
Data values that vary or don't follow a common standard.
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Impact of Data on AI Decisions
Impact of Data on AI Decisions
Accuracy, reliability, and credibility of AI-driven decisions are directly impacted by the quality, consistency, and timeliness of the data.
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AI Data Preparation for Business
AI Data Preparation for Business
First step in using AI to optimize business operations is assessing data availability and quality.
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Data Quality in Healthcare AI
Data Quality in Healthcare AI
Accurate, complete, consistent, and relevant data is essential for reliable medical predictions.
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AI Ethical Challenges
AI Ethical Challenges
Possible biases in AI algorithms and lack of transparency in AI decision making are ethical concerns.
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AI Data Availability
AI Data Availability
Determining what data exists, where it's stored, how it's accessed and maintained is crucial to using AI.
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AI Data Outcomes
AI Data Outcomes
Determining the intended outcomes from using AI is a vital step, but may come after data availability has been ascertained.
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AI Bias
AI Bias
AI algorithms can sometimes reflect human biases present in the training data.
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Data Quality Importance (Healthcare)
Data Quality Importance (Healthcare)
High-quality data is key to accurate and reliable predictions in healthcare AI applications.
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AI Data Relevance
AI Data Relevance
The data used in AI must be appropriate and applicable to the problem being addressed.
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Bias in AI data
Bias in AI data
AI systems can unintentionally learn and amplify biases present in the data they are trained on.
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Data quality impact on AI
Data quality impact on AI
Inaccurate, incomplete, or inconsistent data can negatively affect AI performance and make predictions less reliable.
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User consent and AI
User consent and AI
AI systems must obtain explicit user consent for data collection and usage, or they infringe on privacy.
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Data duplication
Data duplication
Multiple copies of the same data record or value can lead to wasted effort and communication issues.
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Dataset selection for AI
Dataset selection for AI
A crucial factor in choosing a dataset to train an AI model is that it must accurately predict current customer behavior.
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AI data privacy
AI data privacy
AI systems must not violate user privacy, which includes obtaining explicit consent for data collection/use.
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Communication Inefficiencies
Communication Inefficiencies
Excessive sales calls and emails indicate data duplication or similar issues in company communications.
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AI Model Accuracy
AI Model Accuracy
While AI model accuracy is maintained, response times can be slower than expected.
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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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