ITGSS Certified Technical Associate: Project Management Practice Exam

Disable ads (and more) with a membership for a one time $2.99 payment

Prepare for the ITGSS Certified Technical Associate Exam with interactive flashcards and multiple choice questions, all accompanied by detailed explanations. Enhance your project management knowledge and ace the exam with confidence!

Each practice test/flash card set has 50 randomly selected questions from a bank of over 500. You'll get a new set of questions each time!

Practice this question and more.

Which component is essential for creating AI transparency?

Complexity of algorithms
Purpose, limitations, and results of solutions
User engagement
Data gathering methods

The correct answer is: Purpose, limitations, and results of solutions

Creating AI transparency is fundamentally about ensuring that users and stakeholders can understand not just how AI systems operate but also what to expect from them. The most critical aspect in this context is to clearly communicate the purpose of the AI application, the limitations it has, and the results it produces. This transparency enables users to make informed decisions about how to interact with the AI, understand its capabilities, and recognize any potential biases or constraints it may have. By outlining the intended purpose of the AI, users understand the specific problem the AI is meant to address. Detailing limitations informs users of any boundaries or scenarios where the AI may not perform effectively, which is crucial for responsible usage. Finally, sharing results fosters trust and accountability, allowing users to gauge the effectiveness of the AI's outputs based on real-world performance and reliability. This foundational clarity is vital for instilling confidence in AI systems, making it the essential component for transparency. While other factors like complexity of algorithms, user engagement, and data gathering methods are relevant to the overall development and use of AI systems, they do not directly address the need for transparency in communication regarding the AI's functionality and outcomes. Hence, focusing on the purpose, limitations, and results directly addresses the needs for AI transparency.