Salesforce AI Ethics and Data Quality
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Questions and Answers

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?

  • Timeliness
  • Consistency (correct)
  • Relevancy
  • Completeness
  • 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?

    <p>Adhering to legal and regulatory standards in their industry.</p> Signup and view all the answers

    Which element is crucial for fostering trust in AI systems?

    <p>Ensuring high-quality data that is accurate and relevant.</p> Signup and view all the answers

    What does inconsistency in data signify?

    <p>Variations in data can lead to errors and confusion in analysis.</p> Signup and view all the answers

    What role does Salesforce's Trusted AI Principles play in AI development?

    <p>They serve as guidelines for responsible and ethical AI development.</p> Signup and view all the answers

    Why is high-quality data particularly important for AI systems?

    <p>It ensures the model learns from accurate information to make reliable predictions.</p> Signup and view all the answers

    What issue can arise from poor data quality in AI systems?

    <p>Biases in data can be inadvertently learned and amplified by AI systems.</p> Signup and view all the answers

    Which statement accurately describes user consent in AI data privacy?

    <p>AI infringes on privacy when user consent is not obtained.</p> Signup and view all the answers

    What aspect of data quality should be evaluated to address customers receiving excessive marketing communications?

    <p>Duplication of customer records</p> Signup and view all the answers

    What should a developer prioritize when selecting a dataset to train an AI model for predicting customer behavior?

    <p>Relevance and quality of data</p> Signup and view all the answers

    What is a potential risk when AI systems amplify existing biases in data?

    <p>Worsening fairness and ethical outcomes</p> Signup and view all the answers

    Which of the following is NOT a characteristic of poor data quality?

    <p>Completeness</p> Signup and view all the answers

    Why is ensuring user consent vital in AI applications regarding personal data?

    <p>It respects users' rights concerning their personal information.</p> Signup and view all the answers

    What can data duplication lead to in communication with customers?

    <p>Confusion and inefficiencies in communication</p> Signup and view all the answers

    What should be the first step for Cloud Kicks to prepare its data for AI optimization?

    <p>Determine data availability</p> Signup and view all the answers

    How does data quality impact AI applications in healthcare?

    <p>It enhances accuracy and reliability of medical predictions</p> Signup and view all the answers

    Which ethical challenge is linked to the development of AI?

    <p>Potential for human bias in algorithms</p> Signup and view all the answers

    What is a critical action for mitigating bias in machine learning algorithms?

    <p>Training with diverse and representative data</p> Signup and view all the answers

    What is the role of data availability in AI projects?

    <p>It sets the stage for evaluating the scope and feasibility of the projects</p> Signup and view all the answers

    What is a common misconception about AI algorithms in relation to bias?

    <p>They inherently reflect the biases present in training data</p> Signup and view all the answers

    Why is it important to ensure privacy and compliance in AI applications?

    <p>It can enhance public trust in AI technologies</p> Signup and view all the answers

    What outcome can poor data quality lead to in AI decision-making?

    <p>Inaccurate and potentially harmful results</p> Signup and view all the answers

    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.

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