The significance of the explainability of trusted AI systems is that it describes how AI models make decisions. Explainability is crucial for building trust and accountability in AI systems, ensuring that users and stakeholders understand the decision-making processes and outcomes generated by AI. This is particularly important in scenarios where AI decisions impact personal or financial status, such as in credit scoring or healthcare diagnostics. Salesforce emphasizes the importance of explainable AI through its ethical AI practices, aiming to make AI systems more transparent and understandable. More details about Salesforce’s approach to ethical and explainable AI can be found in Salesforce AI ethics resources at Salesforce AI Ethics.
Questions 5
A data quality expert at Cloud Kicks want to ensure that each new contact contains at least an email address …
“A validation rule should be used to ensure that each new contact contains at least an email address or phone number. A validation rule is a feature that checks the data entered by users for errors before saving it to Salesforce. A validation rule can help ensure data quality by enforcing certain criteria or conditions for the data values.”
Questions 6
What is the key difference between generative and predictive AI?
Options:
A.
Generative AI creates new content based on existing data and predictive AI analyzes existing data.
B.
Generative AI finds content similar to existing data and predictive AI analyzes existing data.
C.
Generative AI analyzes existing data and predictive AI creates new content based on existing data.
“The key difference between generative and predictive AI is that generative AI creates new content based on existing data and predictive AI analyzes existing data. Generative AI is a type of AI that can generate novel content such as images, text, music, or video based on existing data or inputs. Predictive AI is a type of AI that can analyze existing data or inputs and make predictions or recommendations based on patterns or trends.”
Questions 7
What are some of the ethical challenges associated with AI development?
Options:
A.
Potential for human bias in machine learning algorithms and the lack of transparency in AI decision-making processes
B.
Implicit transparency of AI systems, which makes It easy for users to understand and trust their decisions
C.
Inherent neutrality of AI systems, which eliminates any potential for human bias in decision-making
“Some of the ethical challenges associated with AI development are the potential for human bias in machine learning algorithms and the lack of transparency in AI decision-making processes. Human bias can arise from the data used to train the models, the design choices made by the developers, or the interpretation of the results by the users. Lack of transparency can make it difficult to understand how and why AI systems make certain decisions, which can affect trust, accountability, and fairness.”
