🤖 AI Interview Questions with Answers (Part 5)
2️⃣1️⃣ What is Explainable AI (XAI)?
👉 Explainable AI (XAI) refers to techniques that help humans understand how and why an AI model produces a particular output.
Examples:
🔹 Feature Importance
🔹 SHAP
🔹 LIME
🔹 Decision Rules
💡 XAI is especially useful when model decisions need to be interpreted or audited.
2️⃣2️⃣ What is AI Bias?
👉 AI Bias occurs when an AI system produces systematically unfair or skewed results due to problems in data, model design, or the way the system is used.
Possible sources include:
🔹 Biased Training Data
🔹 Unbalanced Data
🔹 Sampling Problems
🔹 Historical Bias
🔹 Evaluation Choices
📌 Data → Model → Output
Bias can enter at different stages of this process.
2️⃣3️⃣ What is Responsible AI?
👉 Responsible AI refers to designing and using AI systems with attention to fairness, transparency, privacy, safety, reliability, and accountability.
Important principles:
🔹 Fairness
🔹 Transparency
🔹 Privacy
🔹 Safety
🔹 Accountability
🔹 Human Oversight
💡 Responsible AI aims to consider both technical performance and real-world impact.
2️⃣4️⃣ What is AI Model Evaluation?
👉 AI Model Evaluation is the process of measuring how well an AI model performs on appropriate data and tasks.
Different tasks use different metrics:
📊 Classification: Accuracy, Precision, Recall, F1-Score
📈 Regression: MAE, MSE, RMSE
📝 Generative AI: Task-specific quality, factuality, safety, and human or automated evaluations
💡 The evaluation metric should match the purpose of the AI system.
2️⃣5️⃣ What is AI Ethics?
👉 AI Ethics deals with the principles and practices involved in developing and using AI responsibly.
Important areas include:
🔹 Privacy
🔹 Fairness
🔹 Transparency
🔹 Accountability
🔹 Safety
🔹 Human Control
Example:
Before deploying an AI system that makes important decisions, developers should consider data quality, potential bias, privacy, transparency, and appropriate human oversight.
💬 Save this for your next AI interview preparation!
🔥 Next Part will cover 5 important AI questions on Expert Systems, Knowledge Representation, Fuzzy Logic, Genetic Algorithms & Search Algorithms.
#AI #ArtificialIntelligence #AIInterview #MachineLearning #ExplainableAI #ResponsibleAI #AI ethics #DataScience #InterviewQuestions #Programming
2️⃣1️⃣ What is Explainable AI (XAI)?
👉 Explainable AI (XAI) refers to techniques that help humans understand how and why an AI model produces a particular output.
Examples:
🔹 Feature Importance
🔹 SHAP
🔹 LIME
🔹 Decision Rules
💡 XAI is especially useful when model decisions need to be interpreted or audited.
2️⃣2️⃣ What is AI Bias?
👉 AI Bias occurs when an AI system produces systematically unfair or skewed results due to problems in data, model design, or the way the system is used.
Possible sources include:
🔹 Biased Training Data
🔹 Unbalanced Data
🔹 Sampling Problems
🔹 Historical Bias
🔹 Evaluation Choices
📌 Data → Model → Output
Bias can enter at different stages of this process.
2️⃣3️⃣ What is Responsible AI?
👉 Responsible AI refers to designing and using AI systems with attention to fairness, transparency, privacy, safety, reliability, and accountability.
Important principles:
🔹 Fairness
🔹 Transparency
🔹 Privacy
🔹 Safety
🔹 Accountability
🔹 Human Oversight
💡 Responsible AI aims to consider both technical performance and real-world impact.
2️⃣4️⃣ What is AI Model Evaluation?
👉 AI Model Evaluation is the process of measuring how well an AI model performs on appropriate data and tasks.
Different tasks use different metrics:
📊 Classification: Accuracy, Precision, Recall, F1-Score
📈 Regression: MAE, MSE, RMSE
📝 Generative AI: Task-specific quality, factuality, safety, and human or automated evaluations
💡 The evaluation metric should match the purpose of the AI system.
2️⃣5️⃣ What is AI Ethics?
👉 AI Ethics deals with the principles and practices involved in developing and using AI responsibly.
Important areas include:
🔹 Privacy
🔹 Fairness
🔹 Transparency
🔹 Accountability
🔹 Safety
🔹 Human Control
Example:
Before deploying an AI system that makes important decisions, developers should consider data quality, potential bias, privacy, transparency, and appropriate human oversight.
💬 Save this for your next AI interview preparation!
🔥 Next Part will cover 5 important AI questions on Expert Systems, Knowledge Representation, Fuzzy Logic, Genetic Algorithms & Search Algorithms.
#AI #ArtificialIntelligence #AIInterview #MachineLearning #ExplainableAI #ResponsibleAI #AI ethics #DataScience #InterviewQuestions #Programming