microsoft/AI-For-Beginners
Publicmirrored fromhttps://github.com/microsoft/AI-For-BeginnersAvailable
etc/quiz-src/questions-en.txt
639lines · modecode
| 1 | Lesson 1B Introduction to AI: Pre Quiz |
| 2 | * A famous 19th century proto-computer engineer was |
| 3 | - Charles Barkley |
| 4 | + Charles Babbage |
| 5 | - Charles Darwin |
| 6 | * Weak AI is a system designed to solve many tasks |
| 7 | - True |
| 8 | + False |
| 9 | * Chat bots are an example of truly intelligent systems |
| 10 | - false, they are usually designed by a series of rules. |
| 11 | - true, they are usually considered to be 'intelligent |
| 12 | + false, but they are increasingly able to pass Turing tests as they become more sophisticated. |
| 13 | |
| 14 | Lesson 1E Introduction to AI: Post-Quiz |
| 15 | * A top-down approach to AI is a model of reasoning called |
| 16 | - strategic reasoning |
| 17 | + symbolic reasoning |
| 18 | - synergistic reasoning |
| 19 | * A bottom-up approach to AI is based on neural networks |
| 20 | + True |
| 21 | - False |
| 22 | * The AI Winter occurred in this era |
| 23 | - 1950s |
| 24 | - 1960s |
| 25 | + 1970s |
| 26 | |
| 27 | Lesson 2B Knowledge Representation and Expert Systems: Pre-Quiz |
| 28 | * The top-down approach to creating intelligent systems was based on: |
| 29 | - knowledge seeking and reading |
| 30 | + knowledge representation and reasoning |
| 31 | - knowledge reasoning and seeking |
| 32 | * Knowledge is the same as information |
| 33 | - True |
| 34 | + False |
| 35 | * Knowledge is obtained by an: |
| 36 | + active learning process |
| 37 | - passive learning process |
| 38 | - both of these |
| 39 | |
| 40 | Lesson 2E Knowledge Representation and Expert Systems: Post-Quiz |
| 41 | * The simplest method of knowledge representation is: |
| 42 | + algorithmic |
| 43 | - symbolic |
| 44 | - synergistic |
| 45 | * Scenarios can represent complex situations that can unfold in time |
| 46 | + true |
| 47 | - false |
| 48 | * Forward inference starts with initial data and then: |
| 49 | + executes a reasoning loop |
| 50 | - looks for a goal |
| 51 | - starts over |
| 52 | |
| 53 | Lesson 3B Introduction to Neural Networks - Perceptron: Pre-Quiz |
| 54 | * Early neural networks required |
| 55 | + manual weight adjusting |
| 56 | - terabytes of data |
| 57 | - special reasoning |
| 58 | * A simple neuron is also called a 'threshold logic unit' |
| 59 | + true |
| 60 | - false |
| 61 | * A perceptron is a ___ type of model |
| 62 | - multi-class classification |
| 63 | - clustering |
| 64 | + binary classification |
| 65 | |
| 66 | Lesson 3E Introduction to Neural Networks - Perceptron: Post-Quiz |
| 67 | * To train a perceptron, find a weights vector that results in the smallest ___. |
| 68 | - size |
| 69 | + error |
| 70 | - nodes |
| 71 | * To minimize the function of weights, you can use gradient descent |
| 72 | + true |
| 73 | - false |
| 74 | * During gradient descent, each step updates the ___ |
| 75 | - learning rate |
| 76 | + weights |
| 77 | - gradient |
| 78 | |
| 79 | Lesson 4B Neural Networks: Pre Quiz |
| 80 | * The quality of prediction is measured by Loss function |
| 81 | + True |
| 82 | - False |
| 83 | * One layer network is capable of classifying ____ |
| 84 | - linearly joined classes |
| 85 | + linearly separable classes |
| 86 | - single layers of classes |
| 87 | * The method of training multi-layered perceptron is called ____ |
| 88 | + back propagation |
| 89 | - multiple propagation |
| 90 | - front propagation |
| 91 | |
| 92 | Lesson 4E Neural Networks: Post Quiz |
| 93 | * We use ____ for regression loss functions |
| 94 | - absolute error |
| 95 | - mean squared error |
| 96 | + all of the above |
| 97 | * All but one is a type of classification loss function |
| 98 | - 0-1 loss |
| 99 | + binary loss |
| 100 | - logistic loss |
| 101 | * Cross-entropy loss is a function that can calculate similarity between two arbitrary probability distributions |
| 102 | + True |
| 103 | - False |
| 104 | |
| 105 | Lesson 5B Frameworks: Pre Quiz |
| 106 | * Deep Neural Network training requires a lot of computations |
| 107 | + True |
| 108 | - False |
| 109 | * Overfitting occurs because of ____ |
| 110 | - Not enough testing data |
| 111 | + Too powerful model |
| 112 | - Too much noise in output data |
| 113 | * Bias errors are caused by our ____ not being able to capture the relationship between training data correctly. |
| 114 | - model |
| 115 | + algorithm |
| 116 | - computer |
| 117 | |
| 118 | Lesson 5E Frameworks: Post Quiz |
| 119 | * After compiling our model object, we train by calling ____ function |
| 120 | + fit |
| 121 | - train |
| 122 | - teach |
| 123 | * Binary cross-entropy is also called log loss |
| 124 | + True |
| 125 | - False |
| 126 | * TensorFlow is to ____ while PyTorch is to ____ |
| 127 | - Facebook, Google |
| 128 | + Google, Facebook |
| 129 | - Microsoft, Google |
| 130 | |
| 131 | Lesson 6B Introduction to Computer Vision: Pre Quiz |
| 132 | * Computer vision aims to allow computers gain understanding of _____ |
| 133 | + images |
| 134 | - text |
| 135 | - computers |
| 136 | * Python libraries available for image processing includes |
| 137 | - OpenCV |
| 138 | - Pillow |
| 139 | + a and b |
| 140 | * Images cannot be represented as NumPy arrays in Python |
| 141 | - true |
| 142 | + False |
| 143 | |
| 144 | Lesson 6E Introduction to Computer Vision: Post Quiz |
| 145 | * Optical Flow helps us to understand how each pixel on video frames move. |
| 146 | + true |
| 147 | - false |
| 148 | * _____ computes the vector field that shows where each pixel is moving |
| 149 | - Sparse Optical Flow |
| 150 | + Dense Optical Flow |
| 151 | - none |
| 152 | * Resizing and Blurring are steps that can be taken during? |
| 153 | + pre-processing |
| 154 | - training |
| 155 | - image transformation |
| 156 | |
| 157 | Lesson 7B Convolutional Neural Networks: Pre Quiz |
| 158 | * To extract patterns from images we use? |
| 159 | + convolutional filters |
| 160 | - extractor |
| 161 | - filters |
| 162 | * One of these is not a CNN Architecture |
| 163 | - ResNet |
| 164 | - MobileNet |
| 165 | + TensorFlow |
| 166 | * CNN are mostly used for computer vision tasks. |
| 167 | + true |
| 168 | - false |
| 169 | |
| 170 | Lesson 7E Convolutional Neural Networks: Post Quiz |
| 171 | * Which pooling layer is used "scale down" the size of the image |
| 172 | - average pooling |
| 173 | - max pooling |
| 174 | + a and b |
| 175 | * Convolutional networks generalizes much better |
| 176 | + True |
| 177 | - False |
| 178 | * To train our neural network, we need to convert images to tensors |
| 179 | + true |
| 180 | - false |
| 181 | |
| 182 | Lesson 8B Pre-trained Networks and Transfer Learning: Pre Quiz |
| 183 | * Transfer learning approach uses untrained models for classification |
| 184 | - true |
| 185 | + false |
| 186 | * One of these is not a normalization technique? |
| 187 | + height normalization |
| 188 | - weight normalization |
| 189 | - layer normalization |
| 190 | * We choose Stochastic Gradient Descent(SGD) in deep learning because classical gradient descent can be ____ |
| 191 | - fast |
| 192 | + slow |
| 193 | |
| 194 | Lesson 8E Pre-trained Networks and Transfer Learning: Post Quiz |
| 195 | * Dropout layers act as a ____ technique |
| 196 | - gradient boosting |
| 197 | - training |
| 198 | + regularization |
| 199 | * freezing weights of convolutional feature extractor can be done by ____ |
| 200 | - setting `requires_grad` property to `False` |
| 201 | - setting `trainable` property to `False` |
| 202 | + a and b |
| 203 | * Batch normalization is to bring values that flow through the ____ to right interval |
| 204 | - algorithms |
| 205 | - batches |
| 206 | + neural network |
| 207 | |
| 208 | Lesson 9B Autoencoders: Pre Quiz |
| 209 | * Self-supervised learning uses ____ data for training |
| 210 | - pre-trained |
| 211 | + raw |
| 212 | - labeled |
| 213 | * Encoder Network coverts input images into latent spaces |
| 214 | + true |
| 215 | - false |
| 216 | * VAE is short for? |
| 217 | - Variable AutoEncoding |
| 218 | + Variation auto-encoder |
| 219 | - Variational automated encoders |
| 220 | |
| 221 | Lesson 9E Autoencoders: Post Quiz |
| 222 | * Properties of autoencoders include |
| 223 | - it is data Specific |
| 224 | - works on unlabeled data |
| 225 | + all of the above |
| 226 | * Auto encoders can be used to effectively remove noise from images |
| 227 | + true |
| 228 | - false |
| 229 | * Variational auto-encoders loss function does not consist of which of these? |
| 230 | - reconstruction loss |
| 231 | - KL loss |
| 232 | + TF loss |
| 233 | |
| 234 | Lesson 10B Generative Adversarial Networks: Pre Quiz |
| 235 | * Generators take vectors and produce ____ |
| 236 | - videos |
| 237 | + image |
| 238 | - gif |
| 239 | * GANs is short for? |
| 240 | - General adversarial networks |
| 241 | - Generative advisor networks |
| 242 | + Generative adversarial networks |
| 243 | * GAN uses ____ neural networks |
| 244 | - 1 |
| 245 | + 2 |
| 246 | - 3 |
| 247 | |
| 248 | Lesson 10E Generative Adversarial Networks: Post Quiz |
| 249 | * We can use Batch normalization and BatchNorm1D to stabilize the training |
| 250 | + true |
| 251 | - false |
| 252 | * Deep Convolutional GAN uses convolutional layers for ____ and ____ |
| 253 | + generator, discriminator |
| 254 | - CNN, generator |
| 255 | - training, testing |
| 256 | * Problems of GAN training includes |
| 257 | - Sensitivity to hyperparameters |
| 258 | - Keeping balance between generator and discriminator |
| 259 | + all of the above |
| 260 | |
| 261 | Lesson 11B Object Detection: Pre Quiz |
| 262 | * Neural networks can only be used to classify images |
| 263 | - true |
| 264 | + false |
| 265 | * With object detection, we don't just get the class of an object, but also its ____ |
| 266 | - shape |
| 267 | + location |
| 268 | - type |
| 269 | * How many objects can an object detection model detect? |
| 270 | - one |
| 271 | - two |
| 272 | + any number |
| 273 | |
| 274 | Lesson 11E Object Detection: Post Quiz |
| 275 | * An Object detection model gives us |
| 276 | - object class |
| 277 | - bounding box |
| 278 | + both class and bounding box |
| 279 | * Which object detection models are faster? |
| 280 | + one-pass models |
| 281 | - region proposal networks |
| 282 | - Fast R-CNN |
| 283 | * Which metric can be used to determine how well bounding boxes are aligned? |
| 284 | - accuracy |
| 285 | - precision |
| 286 | + IoU |
| 287 | |
| 288 | Lesson 12B Segmentation: Pre Quiz |
| 289 | * How many segmentation algorithm are there? |
| 290 | - 1 |
| 291 | + 2 |
| 292 | - 3 |
| 293 | * Segmentation is a _____ task |
| 294 | + computer vision |
| 295 | - natural language processing |
| 296 | - neural networks |
| 297 | * Segmentation networks consist of ____ and ____ parts |
| 298 | - classifier, divider |
| 299 | + encoder, decoder |
| 300 | - generator, discriminator |
| 301 | |
| 302 | Lesson 12E Segmentation: Post Quiz |
| 303 | * ____ extracts features from an input image |
| 304 | - decoder |
| 305 | - generator |
| 306 | + encoder |
| 307 | * ____ transforms input features into mask image |
| 308 | + decoder |
| 309 | - generator |
| 310 | - encoder |
| 311 | * SegNet relies on ____ to train multi-layered network |
| 312 | + batch normalization |
| 313 | - height normalization |
| 314 | - weight normalization |
| 315 | |
| 316 | Lesson 13B Text Representation: Pre Quiz |
| 317 | * Each word in a Bag of Words is linked to a vector index |
| 318 | + true |
| 319 | - false |
| 320 | * Text can be represented using _____ approaches |
| 321 | - 1 |
| 322 | + 2 |
| 323 | - 3 |
| 324 | * Character level representation represents each _____ as a number |
| 325 | + letter |
| 326 | - word |
| 327 | - symbol |
| 328 | |
| 329 | Lesson 13E Text Representation: Post Quiz |
| 330 | * Word level representation represents _____ as a number |
| 331 | - letter |
| 332 | + word |
| 333 | - symbol |
| 334 | * N-Grams refers to _____ |
| 335 | - combination of n number of words and symbols |
| 336 | - combination of n number of letters |
| 337 | + combination of n number of Words |
| 338 | * The main drawback of N-gram is that the vocabulary size grows fast |
| 339 | + true |
| 340 | - false |
| 341 | |
| 342 | Lesson 14B Embeddings: Pre Quiz |
| 343 | * Embedding is used to represent words with _____ dimensional dense vectors |
| 344 | + lower |
| 345 | - higher |
| 346 | - average |
| 347 | * Word2Vec pre-trained embeddings can also be used in place of embedding layer in neural networks |
| 348 | + True |
| 349 | - False |
| 350 | * Using embedding layer we cannot switch from bag-of-words to embedding bag |
| 351 | - True |
| 352 | + false |
| 353 | |
| 354 | Lesson 14E Embeddings: Post Quiz |
| 355 | * Word2Vec has _____ main architectures |
| 356 | - 1 |
| 357 | + 2 |
| 358 | - 3 |
| 359 | * Word sense disambiguation is a limitation of traditional pretrained embedding representations |
| 360 | + True |
| 361 | - False |
| 362 | * An embedding layer takes _____ as input |
| 363 | + word |
| 364 | - symbol |
| 365 | - number |
| 366 | |
| 367 | Lesson 15B Language Modeling: Pre Quiz |
| 368 | * Which of the following can be considered a language model? |
| 369 | + Word2Vec embeddings |
| 370 | - Embedding layer in RNN |
| 371 | - RNN used for text classification |
| 372 | * A language model should be able to ____ the next word in the sentence |
| 373 | - use |
| 374 | + predict |
| 375 | - guess |
| 376 | * Language models are trained on |
| 377 | - language vocabulary |
| 378 | - specially labeled data |
| 379 | + any natural text |
| 380 | |
| 381 | Lesson 15E Language Modeling: Post Quiz |
| 382 | * Which of the architectures predicts a word from neighboring words? |
| 383 | + CBoW |
| 384 | - Skip-gram |
| 385 | - N-Gram |
| 386 | * When we train a CBoW model, we obtain |
| 387 | - A model that can generate text |
| 388 | + Word2Vec embedding vectors |
| 389 | - Text classification model |
| 390 | * The CBoW model is based on |
| 391 | + Dense neural network |
| 392 | - Convolutional neural network |
| 393 | - Recurrent neural network |
| 394 | |
| 395 | Lesson 16B RNN: Pre Quiz |
| 396 | * RNN is short for? |
| 397 | - regression neural network |
| 398 | + recurrent neural network |
| 399 | - re-iterative neural network |
| 400 | * A simple RNN cell has two weight _____ |
| 401 | + matrices |
| 402 | - cell |
| 403 | - neuron |
| 404 | * vanishing gradients is a problem of _____ |
| 405 | + RNN |
| 406 | - CNN |
| 407 | - KNN |
| 408 | |
| 409 | Lesson 16E RNN: Post Quiz |
| 410 | * _____ takes some information from the input and hidden vector, and inserts it into state |
| 411 | - forget gate |
| 412 | - output gate |
| 413 | + input gate |
| 414 | * Bidirectional RNNs runs recurrent computation in _____ |
| 415 | + both directions |
| 416 | - north-west direction |
| 417 | - left-right direction |
| 418 | * All RNN Cells have the same shareable weights |
| 419 | + True |
| 420 | - False |
| 421 | |
| 422 | Lesson 17B Generative networks: Pre Quiz |
| 423 | * RNNs can be for generative tasks |
| 424 | + yes |
| 425 | - no |
| 426 | * _____ is a traditional neural network with one input and one output |
| 427 | + one-to-one |
| 428 | - sequence-to-sequence |
| 429 | - one-to-many |
| 430 | * RNN generates texts by generating next output token for each input token |
| 431 | + true |
| 432 | - false |
| 433 | |
| 434 | Lesson 17E Generative networks: Post Quiz |
| 435 | * Output encoder converts hidden state into _____ output |
| 436 | + one-hot-encoded |
| 437 | - sequence |
| 438 | - number |
| 439 | * Selecting the character with higher probabilities always gives a meaningful text. |
| 440 | - true |
| 441 | + false |
| 442 | - maybe |
| 443 | * Many-to-many can also be referred to as _____ |
| 444 | - one-to-one |
| 445 | + sequence-to-sequence |
| 446 | - one-to-many |
| 447 | |
| 448 | Lesson 18B Transformers: Pre Quiz |
| 449 | * Attention mechanism provides a means of _____ the impact of an inout vector on an output prediction of RNN |
| 450 | + weighting |
| 451 | - training |
| 452 | - testing |
| 453 | * BERT is an acronym for |
| 454 | - Bidirectional Encoded Representations From Transformers |
| 455 | + Bidirectional Encoder Representations From Transformers |
| 456 | - Bidirectional Encoder Representatives of Transformers |
| 457 | * In positional encoding the relative position of the token is represented by number of steps |
| 458 | + true |
| 459 | - false |
| 460 | |
| 461 | Lesson 18E Transformers: Post Quiz |
| 462 | * Positional embedding _____ the original token and its position within the sequence |
| 463 | - separates |
| 464 | - compares |
| 465 | + embeds |
| 466 | * Multi-Head Attention is used in transformers to give network the power to capture _____ of dependencies |
| 467 | + different types |
| 468 | - same type |
| 469 | - none |
| 470 | * In transformers attention is used in _____ instances |
| 471 | - 1 |
| 472 | + 2 |
| 473 | - 3 |
| 474 | |
| 475 | Lesson 19B Named Entity Recognition: Pre Quiz |
| 476 | * What does NER stands for? |
| 477 | - Nearest Estimated Region |
| 478 | - Nearest Entity Region |
| 479 | + Named Entity Recognition |
| 480 | * An entity always consists of one token |
| 481 | - true |
| 482 | + false |
| 483 | * To train NER model, we need |
| 484 | + Labeled dataset |
| 485 | - Any natural text |
| 486 | - Translated texts in two languages |
| 487 | |
| 488 | Lesson 19E Named Entity Recognition: Post Quiz |
| 489 | * NER model is essentially a ____ model |
| 490 | - text classification |
| 491 | + token classification |
| 492 | - text regression |
| 493 | * Which neural network types can be used for NER? |
| 494 | - RNNs |
| 495 | - Transformers |
| 496 | + Both RNNs and Transformers |
| 497 | * NER model is a good example of ____ network architecture |
| 498 | - one-to-one |
| 499 | - one-to-many |
| 500 | + many-to-many |
| 501 | |
| 502 | Lesson 20B Language Models: Pre Quiz |
| 503 | * What does GPT stand for? |
| 504 | - Generic Pre-Trained network |
| 505 | + Generative Pre-trained Transformers |
| 506 | - Generic Positional Text |
| 507 | * What can GPT be used for? |
| 508 | - Text generation |
| 509 | - Text classification |
| 510 | + Both text generation and other tasks |
| 511 | * GPT is based on transformer architecture |
| 512 | + true |
| 513 | - false |
| 514 | |
| 515 | Lesson 20E Language Models: Post Quiz |
| 516 | * What is zero-shot learning? |
| 517 | + Getting an answer from pre-trained network |
| 518 | - Training the network from scratch |
| 519 | - Training the network only for one epoch |
| 520 | * Prompt engineering can be used with |
| 521 | - Zero-shot learning |
| 522 | - Few-shot learning |
| 523 | + Both |
| 524 | * Which metric can be used to estimate the quality of a language model? |
| 525 | - accuracy |
| 526 | - recall |
| 527 | + perplexity |
| 528 | |
| 529 | Lesson 21B Genetic Algorithms: Pre Quiz |
| 530 | * Genetic Algorithms are based on which of the following? |
| 531 | - mutations |
| 532 | - Selection |
| 533 | + both a and b |
| 534 | * Crossover allows us to combine two solutions together to obtain a new valid solution |
| 535 | + true |
| 536 | - false |
| 537 | * Valid solutions to genetic algorithm can be represented as _____ |
| 538 | + genes |
| 539 | - neurons |
| 540 | - cells |
| 541 | |
| 542 | Lesson 21E Genetic Algorithms: Post Quiz |
| 543 | * Genetic Algorithms can solve which of these tasks |
| 544 | - Schedule optimization |
| 545 | - Optimal packing |
| 546 | + both of a and b |
| 547 | * In implementing a genetic algorithm the first step is to randomly select two genes |
| 548 | - true |
| 549 | + false |
| 550 | * When using Crossover operation the algorithm randomly selects _____ genes |
| 551 | - 3 |
| 552 | - 1 |
| 553 | + 2 |
| 554 | |
| 555 | Lesson 22B Reinforcement Learning: Pre Quiz |
| 556 | * To train RL model, we need |
| 557 | + Simulation environment |
| 558 | - Labeled dataset |
| 559 | - Unlabeled dataset |
| 560 | * What is a good example of Reinforcement learning: |
| 561 | - Zero-shot image classification |
| 562 | - Zero-shot text classification |
| 563 | + Learning to play chess |
| 564 | * When creating RL-based chess engine, we need to |
| 565 | - use all existing chess matches as a dataset |
| 566 | + let computer play against itself many times |
| 567 | - program exhaustive search algorithm |
| 568 | |
| 569 | Lesson 22E Reinforcement Learning: Post Quiz |
| 570 | * How does RL training algorithm knows how well it did? |
| 571 | - It achieves high accuracy |
| 572 | - Using perplexity metric |
| 573 | + Using reward function |
| 574 | * Which problem(s) is RL is applicable to? |
| 575 | - With discrete environment |
| 576 | - With continuous environment |
| 577 | + Both |
| 578 | * In Actor-Critic model, critic predicts |
| 579 | + Reward function |
| 580 | - Best next action |
| 581 | - Probability of next actions |
| 582 | |
| 583 | Lesson 23B Multi-Agent Modeling: Pre Quiz |
| 584 | * By modeling the behavior of simple agents, we can understand more complex behaviors of a system. |
| 585 | + true |
| 586 | - false |
| 587 | * The principle of metasystem transition is derived from: |
| 588 | - Evolutionary Cybernetics |
| 589 | - Emergentism |
| 590 | + both of these |
| 591 | * Multi-Agent systems emerged in the ____: |
| 592 | - 1970s |
| 593 | - 1980s |
| 594 | + 1990s |
| 595 | |
| 596 | Lesson 23E Multi-Agent Modeling: Post Quiz |
| 597 | * An agent is: |
| 598 | - an entity that lives alone |
| 599 | + an entity that lives in an environment |
| 600 | - an entity that is intelligent |
| 601 | * Reactive agents usually have: |
| 602 | + simple request-response behavior |
| 603 | - complex behavior |
| 604 | - no behavior |
| 605 | * Multi-agent systems are used in: |
| 606 | - video production and systems modeling |
| 607 | - games and automations |
| 608 | + both the above |
| 609 | |
| 610 | Lesson 24B Ethical and Responsible AI: Pre Quiz |
| 611 | * Why we need to worry about Ethical AI? |
| 612 | - AI is a very powerful tool and can cause harm |
| 613 | - We need to make sure AI models do not discriminate people |
| 614 | + Both |
| 615 | * Which is the example of interpretable AI? |
| 616 | + Expert system |
| 617 | - Neural network |
| 618 | - Image classifier |
| 619 | * It is not ethical to use AI in medicine |
| 620 | - true |
| 621 | + false |
| 622 | |
| 623 | Lesson 24E Ethical and Responsible AI: Post Quiz |
| 624 | * Why an AI model can discriminate? |
| 625 | - Because it may become unfriendly |
| 626 | + Because datasets were not properly balanced |
| 627 | - Because developers programmed it in such a way |
| 628 | * Which of the following is not a principle of Responsible AI? |
| 629 | - Transparency |
| 630 | - Fairness |
| 631 | + Cleverness |
| 632 | * Accountability of an AI system means that |
| 633 | + there should be a human being involved in taking decisions, who can take responsibility |
| 634 | - AI system should be held responsible for its actions |
| 635 | - AI system developers should be held responsible |
| 636 | * Model fairness is related to |
| 637 | - Interpretability |
| 638 | + Biases |
| 639 | - Accountability |