Salesforce Salesforce-AI-Associate Exam With Confidence Using Practice Dumps

“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.”

“The Ethical AI Process Maturity Model is the established Salesforce Ethical Maturity Model that the Cloud technical team should use to guide the development of trusted AI solutions. The Ethical AI Process Maturity Model is a framework that helps assess and improve the ethical and responsible practices and processes involved in developing and deploying AI systems. The Ethical AI Process Maturity Model consists of five levels of maturity: Ad Hoc, Aware, Defined, Managed, and Optimized. The Ethical AI Process Maturity Model can help guide the development of trusted AI solutions by providing a roadmap and best practices for achieving higher levels of ethical maturity.”

By checking for null values, a business analyst (BA) is verifying the data quality component of completeness. Completeness refers to the absence of missing values or gaps in the data, which is essential for the accuracy and reliability of reports and analytics used in AI models. Null values can indicate incomplete data, which may adversely affect the performance of AI applications by leading to incorrect predictions or insights. Salesforce emphasizes the importance of data completeness for effective data analysis and provides tools for data quality assessment and improvement. Details on handling data completeness in Salesforce can be explored at Salesforce Help Data Management.