Key Terms for AI Governance

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

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

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

<p>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</p> Signup and view all the answers

<p>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</p> Signup and view all the answers

<p>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</p> Signup and view all the answers

<p>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</p> Signup and view all the answers

<p>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</p> Signup and view all the answers

<p>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.</p> Signup and view all the answers

<p>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.</p> Signup and view all the answers

<p>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.</p> Signup and view all the answers

<p>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.</p> Signup and view all the answers

<p>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.</p> Signup and view all the answers

<p>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.</p> Signup and view all the answers

<p>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.</p> Signup and view all the answers

<p>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.</p> Signup and view all the answers

<p>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.</p> Signup and view all the answers

<p>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.</p> Signup and view all the answers

<p>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.</p> Signup and view all the answers

<p>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.</p> Signup and view all the answers

<p>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.</p> Signup and view all the answers

<p>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.</p> Signup and view all the answers

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

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

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

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

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

<p>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</p> Signup and view all the answers

<p>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</p> Signup and view all the answers

<p>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</p> Signup and view all the answers

<p>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</p> Signup and view all the answers

<p>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</p> Signup and view all the answers

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

<p>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</p> Signup and view all the answers

<p>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</p> Signup and view all the answers

<p>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</p> Signup and view all the answers

<p>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</p> Signup and view all the answers

<p>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</p> Signup and view all the answers

<p>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</p> Signup and view all the answers

<p>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</p> Signup and view all the answers

<p>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</p> Signup and view all the answers

<p>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</p> Signup and view all the answers

<p>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</p> Signup and view all the answers

<p>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</p> Signup and view all the answers

<p>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</p> Signup and view all the answers