- Email support@dumps4free.com
Which of the following most encourages accountability over Al systems?
A. Determining the business objective and success criteria for the Al project.
B. Performing due diligence on third-party Al training and testing data.
C. Defining the roles and responsibilities of Al stakeholders.
D. Understanding Al legal and regulatory requirements.
Explanation:
Defining the roles and responsibilities of AI stakeholders is crucial for encouraging accountability over AI systems. Clear delineation of who is responsible for different aspects of the AI lifecycle ensures that there is a person or team accountable for monitoring, maintaining, and addressing issues that arise. This accountability framework helps in ensuring that ethical standards and regulatory requirements are met, and it facilitates transparency and traceability in AI operations. By assigning specific roles, organizations can better manage and mitigate risks associated with AI deployment and use.
A U.S. mortgage company developed an Al platform that was trained using anonymized details from mortgage applications, including the applicant’s education, employment and demographic information, as well as from subsequent payment or default information. The Al platform will be used automatically grant or deny new mortgage applications, depending on whether the platform views an applicant as presenting a likely risk of default.
Which of the following laws is NOT relevant to this use case?
A. Fair Housing Act.
B. Fair Credit Reporting Act.
C. Equal Credit Opportunity Act.
D. Title VII of the Civil Rights Act of 1964.
Explanation:
The U.S. mortgage company's AI platform relates to housing and credit, making the Fair Housing Act (A), Fair Credit Reporting Act (B), and Equal Credit Opportunity Act (C) relevant. Title VII of the Civil Rights Act of 1964 deals with employment discrimination and is not directly relevant to the mortgage application context (D).
Random forest algorithms are in what type of machine learning model?
A. Symbolic.
B. Generative.
C. Discriminative.
D. Natural language processing.
Explanation:
Random forest algorithms are classified as discriminative models. Discriminative models are used to classify data by learning the boundaries between classes, which is the core functionality of random forest algorithms. They are used for classification and regression tasks by aggregating the results of multiple decision trees to make accurate predictions.
[Reference: The AIGP Body of Knowledge explains that discriminative models, including random forest algorithms, are designed to distinguish between different classes in the data, making them effective for various predictive modeling tasks., , ]
According to the GDPR, an individual has the right to have a human confirm or replace an automated decision unless that automated decision?
A. Is authorized with the data subject s explicit consent.
B. Is authorized by applicable Ell law and includes suitable safeguards.
C. Is deemed to solely benefit the individual and includes documented legitimate interests.
D. Is necessary for entering into or performing under a contract between the data subject and data controller.
Explanation:
According to the GDPR, individuals have the right to not be subject to a decision based solely on automated processing, including profiling, which produces legal effects or similarly significantly affects them. However, there are exceptions to this right, one of which is when the decision is based on the data subject's explicit consent. This means that if an individual explicitly consents to the automated decision-making process, there is no requirement for human intervention to confirm or replace the decision. This exception ensures that individuals can have control over automated decisions that affect them, provided they have given clear and informed consent.
Each of the following actors are typically engaged in the Al development life cycle EXCEPT?
A. Data architects.
B. Government regulators.
C. Socio-cultural and technical experts.
D. Legal and privacy governance experts.
Explanation:
Typically, actors involved in the AI development life cycle include data architects (who design the data frameworks), socio-cultural and technical experts (who ensure the AI system is socio-culturally aware and technically sound), and legal and privacy governance experts (who handle the legal and privacy aspects). Government regulators, while important, are not directly engaged in the development process but rather oversee and regulate the industry.
Reference:
AIGP BODY OF KNOWLEDGE and AI development frameworks.
| Page 1 out of 6 Pages |