Key Terms for AI Governance

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Questions and Answers

Match the following AI concepts with their descriptions:

Explainability = Transparency in AI decision-making Exploratory data analysis = Gaining preliminary insights into a dataset Fairness = Equal treatment in AI decisions Federated learning = Training models on local data of multiple devices

Match the AI terms with their respective process or characteristic:

Explainability = Providing information about how AI generates output Exploratory data analysis = Identifying anomalies and relationships in data Fairness = Avoiding adverse impacts on sensitive attributes Federated learning = Aggregating local model updates to improve a global model

Match the given concepts with their most appropriate description:

Explainability = Provides sufficient information about AI system's output Exploratory data analysis = Techniques used before training a machine learning model Fairness = Prioritizes equal treatment of individuals or groups Federated learning = Allows training on local data of edge devices

Match these AI related terms with their main focus or goal:

Explainability = Maintaining transparency and trust in AI Exploratory data analysis = Finding patterns and outliers in datasets Fairness = Ensuring consistent, accurate, and measurable treatment Federated learning = Enabling better privacy and security controls Signup and view all the answers

Match the following AI terms with their definition:

Explainability = How an AI system generates an output or makes a decision Exploratory data analysis = Data discovery process prior to model training Fairness = Attribute of AI system that prioritizes equal treatment Federated learning = Machine learning method that trains models on local edge device data Signup and view all the answers

Match the following concepts with the task that they perform:

Explainability = Describes the decision process of AI Exploratory data analysis = Provides preliminary insights to the data Fairness = Aims to avoid disparate impact on sensitive attributes Federated learning = Trains an algorithm on individual datasets on edge devices Signup and view all the answers

Match the given AI concepts to a related function:

Explainability = Allows us to comprehend AI behavior Exploratory data analysis = A method of gaining preliminary knowledge Fairness = Promotes unbiased AI decisions Federated learning = Allows distributed model training Signup and view all the answers

Match the following AI terms with their process or property:

Explainability = Key to trust and transparency Exploratory data analysis = Involves pattern and anomaly identification Fairness = Ensures consistent and equal treatment Federated learning = Improves global models while protecting privacy Signup and view all the answers

Match the following AI governance terms with their definitions:

Accuracy = Measure of the system's performance in producing correct outputs. Active learning = Algorithm selects some of the data it learns from. Adaptive learning = Tailors educational content to the needs of individual students. Adversarial attack = Manipulating the model by introducing malicious input data. Signup and view all the answers

Match the following AI concepts with their corresponding process:

Accuracy = Evaluates outputs based on input data. Active learning = Selects data points it will learn from. Adaptive learning = Adjusts content to the specific learning pace. Adversarial attack = Introduces malicious data to mislead the model. Signup and view all the answers

Match the AI terms with their respective purposes:

Accuracy = Assess the correctness of generated outputs. Active learning = Enhances model training with strategic data selection. Adaptive learning = Provides individualized support for students. Adversarial attack = Meant to exploit the vulnerabilities of the AI. Signup and view all the answers

Match the AI terms with their application contexts:

Accuracy = Essential in applications requiring high precision like medical diagnosis. Active learning = Relevant in data-scarce learning environments. Adaptive learning = Applied in edTech platforms for personalized instruction. Adversarial attack = Can be significant risk in self-driving cars. Signup and view all the answers

Match each AI term with the relevant process it involves:

Accuracy = Producing correct outputs based on input data. Active learning = Requesting additional data points to aid learning. Adaptive learning = Adjusting and tailoring educational content. Adversarial attack = Introducing deceptive data to cause malfunction. Signup and view all the answers

Match the terms with the type of impacts they may have:

Accuracy = Affects the reliability and correctness of outputs. Active learning = Improves the model learning strategies. Adaptive learning = Optimizes educational outcomes. Adversarial attack = Can lead to unsafe or incorrect decisions causing harm. Signup and view all the answers

Match the AI governance terms to a related concept:

Accuracy = Related to the correctness of an AI model. Active learning = Related to efficient data usage in AI learning. Adaptive learning = Related to personalized instruction techniques. Adversarial attack = Related to security vulnerabilities in AI models. Signup and view all the answers

Match the terms with their descriptions:

Trustworthy AI = Emphasizes principles such as security, safety, and transparency in AI development. Turing test = Evaluates AI's ability to mimic human conversation, originally through a written text. Underfitting = Occurs when a model is too simple and misses patterns in training data. Unsupervised learning = Involves training models on unlabeled data to find patterns. Signup and view all the answers

Match the AI concepts with their key aspects:

Turing test = Focuses on the machine's ability to converse like a human. Underfitting = May result in poor predictive ability and inaccurate outputs. Unsupervised learning = Useful for techniques such as clustering data and outlier detection. Validation data = Used for fine-tuning the parameters of a model and preventing overfitting. Signup and view all the answers

Match the terms to their primary purpose or characteristic:

Trustworthy AI = Concerns ethical AI development and governance. Validation data = A subset of the data to assess model performance during training. Underfitting = Indicates a model that is not complex enough to capture data patterns. Unsupervised learning = AI is provided with pre-existing unlabeled datasets to analyze. Signup and view all the answers

Match the following terms with their core attribute:

Turing test = Assesses machine intelligence against human-like conversational abilities. Underfitting = Signals that the model is too simplistic to model data patterns. Unsupervised learning = Relies on unlabelled datasets to perform classifications. Validation data = Used prior to final model evaluation. Signup and view all the answers

Match the terms with their descriptions relating to model training:

Underfitting = Occurs when a model is too simple and does not fit the data well. Unsupervised learning = Uses only unlabeled data. Validation data = Helps prevent overfitting during training. Trustworthy AI = Aims to implement principles of fairness and transparency. Signup and view all the answers

Match each AI concept with its primary function:

Turing test = Assesses ability to mimic human text communication. Underfitting = Results in a model with poor generalization. Unsupervised learning = Discovers patterns in unlabeled data. Validation data = Optimizes model parameters. Signup and view all the answers

Match these AI terms with their practical significance:

Trustworthy AI = Aims to prevent bias and protect privacy. Turing test = Benchmark for machine intelligence. Underfitting = Leads to inaccurate predictions. Unsupervised learning = Used for data mining and analysis. Signup and view all the answers

Match the following AI applications with their corresponding terms:

Image captioning = Multimodal models Language translation = Natural language processing Image recognition = Neural networks Medical diagnosis = Neural networks Signup and view all the answers

Match the following characteristics with the appropriate AI terms:

Uses hidden layers = Neural networks Processes text and images = Multimodal models Measures sentiment = Natural language processing Learns from data = Machine learning model Signup and view all the answers

Match the following AI concepts with examples:

Neural networks = Deep learning algorithms Natural language processing = Chatbots Multimodal models = Image and text analysis Machine learning model = Recommendation systems Signup and view all the answers

Match the following AI terms with their primary focus:

Natural language processing = Text understanding Multimodal models = Integration of multiple data types Neural networks = Pattern recognition Machine learning model = Data-driven improvement Signup and view all the answers

Match each AI term with its relevant task or function:

Natural language processing = Interpreting human languages Neural networks = Modeling nonlinear relationships Multimodal models = Combining different data modalities Machine learning model = Optimizing performance through training Signup and view all the answers

Match the definitions with correct AI models:

Neural networks = Simulates human brain neuron interactions Multimodal models = Handles various data types at once Natural language processing = Translates and understands text and speech Machine learning model = Learns from observed data patterns Signup and view all the answers

Match the terms with their relevant capabilities:

Natural language processing = Content generation from text Neural networks = Complex data pattern modeling Multimodal models = Image and text merging Machine learning model = Enhanced prediction accuracy Signup and view all the answers

Match the AI governance terms with their definitions:

Human-in-the-loop = A design paradigm that incorporates human oversight over AI decision-making processes Impact assessment = An evaluation process to document and mitigate implications of an AI system Inference = A type of machine learning process used to make predictions based on input data Interpretability = The ability to present a model's reasoning in human-understandable terms Signup and view all the answers

Match the concepts with their related descriptions:

Inference = Using a trained model to make predictions Input data = Data provided to a learning algorithm Human-in-the-loop = Incorporating human intervention in AI operations Impact assessment = Identifying ethical implications of an AI system Signup and view all the answers

Match the terms with their functions in AI systems:

Interpretability = Facilitating understanding of AI models Input data = Basis for machine learning models Impact assessment = Evaluating societal implications Human-in-the-loop = Ensuring human control in AI decisions Signup and view all the answers

Match the AI concepts with their focuses:

Inference = Making decisions based on trained models Interpretability = Explaining models' decisions Input data = Data for model training Impact assessment = Understanding implications of AI usage Signup and view all the answers

Match the terms related to AI with their descriptions:

Human-in-the-loop = Involves human oversight in AI processes Input data = Essential for machine learning models Inference = Predictive analytics using trained models Impact assessment = Documentation of ethical and legal implications Signup and view all the answers

Match the AI governance concepts with their characteristics:

Interpretability = Designing models for easier understanding Human-in-the-loop = Emphasizing human control Input data = Crucial element in machine training Impact assessment = Focus on societal effects of AI Signup and view all the answers

Match the terms with their implications in AI:

Impact assessment = Addressing ethical concerns Inference = Utilizing models for data-driven decisions Human-in-the-loop = Incorporates human feedback Input data = Foundation for learning algorithms Signup and view all the answers

Match the following AI terms with their respective definitions:

Inference = Process of making predictions with trained models Impact assessment = Evaluates ethical implications of AI deployment Interpretability = Explains reasoning behind AI decisions Human-in-the-loop = Maintaining human control in AI operations Signup and view all the answers

Match the following concepts in machine learning with their descriptions:

Open-source software = A decentralized development model providing free access to source code Overfitting = A model that becomes too specific to the training data Oversight = Monitoring and supervising an AI system for compliance Parameters = Internal variables an algorithmic model learns from data Signup and view all the answers

Match the following machine learning processes with their definitions:

Post processing = Steps after model run to adjust outputs Preprocessing = Steps to prepare data for training Model training = The phase where the model learns from data Validation = Testing the model's performance on unseen data Signup and view all the answers

Match the following terms related to AI governance with their meanings:

Certification processes = Formal evaluations to ensure compliance Conformity assessments = Evaluations that measure adherence to standards Regulatory authorities = Organizations responsible for enforcement Transparency = Openness about processes and decision-making in AI Signup and view all the answers

Match the following machine learning techniques with their outcomes:

Parameter tuning = Optimizing model performance by adjusting internal variables Data augmentation = Increasing the diversity of training data Feature scaling = Normalizing the range of independent variables Model ensemble = Combining multiple models for improved accuracy Signup and view all the answers

Match the following aspects of model evaluation with their characteristics:

Accuracy = The ratio of correct predictions to total predictions Precision = The ratio of true positives to the sum of true and false positives Recall = The ratio of true positives to the sum of true positives and false negatives F1 Score = The harmonic mean of precision and recall Signup and view all the answers

Match the following data-related terms with their purposes:

Data cleaning = Removing errors and inconsistencies from data Feature extraction = Selecting important variables for model training Data normalization = Adjusting the scales of data features Data encoding = Transforming categorical variables into numerical values Signup and view all the answers

Match the following types of machine learning with their examples:

Supervised learning = Using labeled data to train models Unsupervised learning = Clustering data without labeled outcomes Reinforcement learning = Training agents through rewards and penalties Semi-supervised learning = Combining labeled and unlabeled data for training Signup and view all the answers

Match the following error types in machine learning with their descriptions:

Bias = Error due to oversimplification of the model Variance = Error due to excessive complexity in the model Irreducible error = Error inherent in the data that cannot be reduced Overfitting error = Error from a model that fits the training data too closely Signup and view all the answers

Flashcards

Accuracy

Measure of a system's performance in producing correct outputs based on input data.

Active learning

A subfield of AI where an algorithm selects specific data points to learn from.