1. Foundations and Evolution of Neural Networks

Introduction and Context

Neural networks are computational models designed to learn relationships between inputs and outputs by adjusting interconnected numerical parameters. They occupy a central position in modern artificial intelligence (AI), particularly in machine learning systems that recognize patterns in images, speech, language, time series, and complex decision environments. Their importance, however, is not limited to recent advances in deep learning. Neural networks represent a long intellectual tradition involving neuroscience, mathematical modeling, statistics, engineering, psychology, and computer science.

The term neural network can create the misleading impression that an artificial neural network is a detailed simulation of the human brain. It is not. Biological nervous systems are extraordinarily complex, involving biochemical processes, dynamic networks, learning mechanisms, and embodied interaction with the environment. Artificial neural networks abstract only a few broad ideas: information is processed by units, units are connected, signals are transformed, and connection strengths can change through learning. This abstraction has proved useful because it provides a flexible framework for approximating functions and discovering regularities in data.

Understanding the foundations of neural networks is essential before studying architectures such as convolutional neural networks, recurrent neural networks, transformers, and generative models. It clarifies what a neuron computes, why a single perceptron is limited, how multilayer networks overcome some of those limitations, and why training, data representation, and generalization remain central concerns. It also places current developments in historical perspective: progress in neural networks has been shaped not only by new algorithms but also by improvements in data, computing hardware, software, and research practice.

Learning Objectives

By the end of this chapter, the reader should be able to:

1. Explain the biological ideas that inspired artificial neural networks and distinguish biological neurons from computational neurons.

2. Describe the structure and mathematical operation of an artificial neuron.

3. Explain the perceptron learning rule and identify the conditions under which a perceptron can or cannot solve a classification problem.

4. Discuss connectionism and its relationship to symbolic approaches to AI.

5. Explain why activation functions and multilayer architectures are necessary for nonlinear modeling.

6. Interpret the universal approximation idea without confusing approximation capacity with successful learning or generalization.

7. Summarize major historical developments in neural networks and relate them to contemporary AI.

8. Identify applications, limitations, professional risks, and responsible-use considerations associated with neural networks.

From Biological Nervous Systems to Computational Abstractions

A biological neuron receives signals through branching structures called dendrites, integrates these signals in the cell body, and may transmit an electrical impulse through its axon when the combined stimulation exceeds a dynamic threshold. At synapses, connections between neurons vary in strength. Learning can involve changes in these synaptic strengths, although the mechanisms are considerably more complex than a simple numerical update.

Artificial neural networks borrow three broad principles from this picture. First, computation is distributed across many units rather than performed by one central symbolic processor. Second, the effect of one unit on another is represented by a connection weight. Third, learning modifies weights in response to experience or data. These principles are analogies, not complete biological explanations. An artificial neuron generally operates on numerical vectors and produces a numerical output in a fixed sequence of calculations. A biological neuron operates in a living system with temporal, chemical, spatial, and adaptive properties that are only partly represented in standard machine-learning models.

The abstraction is valuable because it separates the functional question—how inputs can be transformed into useful outputs—from the physical details of the brain. This distinction also explains why neural networks can be implemented using ordinary digital computers, graphics processing units, specialized accelerators, or neuromorphic hardware.

The Artificial Neuron

The simplest artificial neuron receives inputs x1, x2, ..., xn. Each input is multiplied by a corresponding weight w1, w2, ..., wn. A bias b is then added, producing a net input:

z = w1x1 + w2x2 + ... + wnxn + b = w^T x + b.

The neuron applies an activation function g to this value:

y = g(z).

The weights determine the contribution of the inputs, while the bias shifts the position of the decision boundary or response curve. Without a bias, the model is constrained to pass through the origin in input space, which is often unnecessarily restrictive.

If g is the identity function, the neuron is simply a linear model. If g is nonlinear, the unit can represent a nonlinear transformation. Common activation functions include the sigmoid function, hyperbolic tangent, and rectified linear unit (ReLU):

σ(z) = 1/(1 + e^-z),

tanh(z) = (e^z - e^-z)/(e^z + e^-z),

ReLU(z) = max(0,z).

The sigmoid maps values to the interval (0,1), making it useful in some probability-like output settings, although it can produce very small gradients for highly positive or negative inputs. Tanh maps values to (-1,1) and is centered around zero, but it also saturates. ReLU is computationally simple and has been widely used in deep networks because it avoids saturation on its positive side, although units can become permanently inactive when their inputs remain negative. Modern architectures also use variants such as leaky ReLU, GELU, and other smooth or self-gated functions.

A crucial point is that stacking linear neurons without nonlinear activation functions does not create a genuinely more expressive model. The composition of linear transformations is itself a linear transformation. Nonlinearity is therefore what allows multilayer networks to model curved decision boundaries and complex input-output relationships.

The Perceptron: A Foundational Learning Model

The perceptron, introduced by Frank Rosenblatt in the late 1950s, is a binary classifier based on a thresholded linear combination of inputs. In its basic form, it predicts one class when w^T x + b is greater than or equal to zero and the other class otherwise. The decision boundary is the hyperplane:

w^T x + b = 0.

In two dimensions this boundary is a line; in three dimensions it is a plane; in higher dimensions it is a hyperplane. The perceptron therefore divides the input space into two half-spaces.

For a labeled training example (x, t), where t is the target class, the perceptron updates its parameters when the example is misclassified. In one common representation, with targets encoded as -1 and +1, the update is:

w ← w + ηt x,

b ← b + ηt,

where η is a positive learning rate. Equivalent formulations use targets 0 and 1 and update only on certain errors. The underlying principle is the same: errors cause a directional adjustment intended to make the incorrect example more likely to be classified correctly.

The perceptron convergence theorem states that if the training data are linearly separable, the perceptron learning algorithm will find a separating hyperplane after a finite number of updates, assuming suitable conditions. This result is important but limited. It does not guarantee a particular speed of convergence, optimal generalization, or success on nonseparable data.

The classic counterexample is the exclusive OR (XOR) function. With binary inputs, XOR produces one when exactly one input is one. Its positive examples cannot be separated from its negative examples by a single straight line. Consequently, a single-layer perceptron cannot represent XOR. This is not a minor technical defect; it demonstrates the representational limits of a single linear threshold unit.

Connectionism and the Structure of Multilayer Networks

Connectionism is a perspective in cognitive science and AI that explains intelligent behavior through networks of simple, interacting units whose collective activity produces useful representations. In contrast, classical symbolic AI emphasizes explicit symbols, rules, logic, and manipulation of structured representations. The distinction should not be treated as absolute. Contemporary AI systems often combine learned neural representations with symbolic constraints, search, retrieval, programs, or domain knowledge.

A multilayer feedforward network contains an input layer, one or more hidden layers, and an output layer. Information moves from the input toward the output without recurrent feedback within the basic architecture. A two-layer mathematical form can be written as:

y = g2(W2 g1(W1x + b1) + b2).

The hidden layer transforms the original representation into an intermediate representation. For example, in an image task, early units may respond to local contrasts, later units may combine these into shapes, and still later units may support object-level predictions. This interpretation is useful, but the exact meaning of individual hidden units is not always simple or stable.

The expressive power of a network depends on its depth, width, activation functions, connectivity pattern, and parameter values. A network with sufficient hidden units can implement functions that a single perceptron cannot. Training such networks typically involves defining a loss function, computing how the loss changes with respect to parameters, and updating parameters through an optimization method such as gradient descent. These training mechanisms are treated in greater detail in later chapters; here, the foundational point is that architecture determines the class of functions that can be represented, while learning determines whether useful parameters can be found from data.

Universal Approximation: Capacity Is Not Competence

Universal approximation results provide a theoretical explanation for the power of feedforward neural networks. In simplified terms, under appropriate conditions, a network with at least one hidden layer and a suitable nonlinear activation function can approximate any continuous function on a compact domain to arbitrary accuracy, given enough hidden units.

This statement is frequently misunderstood. It does not mean that every neural network can learn every task, that a shallow network is always practically adequate, or that approximation guarantees imply good generalization. Several qualifications are essential.

First, the result concerns approximation on a specified domain, usually under mathematical regularity assumptions. Real-world data may be noisy, incomplete, high-dimensional, nonstationary, or outside the training distribution. Second, the theorem says that suitable parameters exist; it does not say that a selected optimization algorithm will find them efficiently. Third, the required number of units may be impractically large. Fourth, low approximation error on observed data does not guarantee reliable performance on new cases. Finally, a function may be approximated numerically while the resulting model remains difficult to interpret, computationally expensive, or unsafe in deployment.

The practical significance of universal approximation is therefore conceptual: neural networks are highly flexible function approximators. Their value depends on the interaction among inductive biases, architecture, data quality, optimization, regularization, evaluation, and deployment conditions.

A Historical Path from Mathematical Neurons to Deep Learning

The evolution of neural networks is best understood as a sequence of ideas combined with changing technical conditions.

In 1943, Warren McCulloch and Walter Pitts proposed a formal model of a neuron using logical threshold behavior. Their work showed how networks of simple units could, in principle, implement logical computations. The model was highly simplified but established a mathematical language for discussing neural computation.

Donald Hebb proposed a learning principle in 1949 often summarized as “cells that fire together wire together.” Hebbian learning linked changes in connection strength to patterns of co-activation. Although modern neural-network training is usually based on gradient methods rather than a direct implementation of Hebb’s rule, the idea influenced theories of distributed learning and representation.

Rosenblatt’s perceptron in the late 1950s demonstrated a trainable machine that could classify patterns using adjustable weights. Its practical successes generated substantial optimism. In 1969, Marvin Minsky and Seymour Papert analyzed limitations of single-layer perceptrons, including their inability to represent XOR. Their critique was mathematically valuable, but it contributed to reduced enthusiasm for neural approaches, particularly when computing resources and training methods for multilayer models were still inadequate.

During the 1980s, interest revived. The backpropagation algorithm, popularized in influential work by Rumelhart, Hinton, and Williams, provided an efficient method for computing gradients through multilayer networks. Backpropagation made it practical to train networks with hidden layers, although the method itself had earlier antecedents. Research also advanced in recurrent networks, distributed representations, and cognitive modeling.

In the 1990s, convolutional neural networks achieved important results in visual recognition, especially through work associated with Yann LeCun and colleagues. Support vector machines and other methods were also highly competitive, and neural networks did not dominate every application. The 2000s brought improved initialization, optimization methods, representation-learning strategies, larger datasets, and greater computing capacity.

The deep-learning expansion of the 2010s was accelerated by graphics processing units, large labeled datasets, algorithmic refinements, and benchmark-driven research. Deep convolutional networks substantially improved image classification; recurrent and sequence-to-sequence models advanced language and speech applications; and attention-based architectures later became central to large language and multimodal systems. Generative adversarial networks, variational methods, diffusion models, and foundation models extended neural networks beyond prediction into synthesis and general-purpose representation learning.

This history shows that progress rarely comes from an algorithm in isolation. A model may become useful only when data, hardware, software libraries, optimization methods, evaluation practices, and deployment infrastructure mature together.

Neural Networks Within AI and Machine Learning

Artificial intelligence is the broad field concerned with designing systems capable of tasks associated with intelligence, such as perception, reasoning, learning, planning, language use, and decision-making. Machine learning is a major approach within AI in which systems infer patterns or decision rules from data rather than relying solely on manually written instructions. Neural networks are one family of machine-learning models.

Their relationship can be represented conceptually as follows: AI is the broad objective and field; machine learning is a principal methodological approach; deep learning is machine learning based on multilayer neural networks. The categories overlap in practice. Not every AI system learns, not every machine-learning system is neural, and not every neural network is deep.

Neural networks are especially appropriate when the relationship between inputs and outputs is complex, difficult to specify with rules, and supported by sufficient data. They are less automatically suitable when data are scarce, strict interpretability is required, the environment changes rapidly, or a simple model performs equally well at lower cost. Model selection should therefore be driven by task requirements rather than by the popularity of a technique.

Applications Across Sectors

Neural networks are used for classification, regression, ranking, representation learning, anomaly detection, generation, and control. In healthcare, they may assist with medical-image interpretation, risk prediction, or analysis of physiological signals. Such systems should support qualified professionals rather than silently replace clinical judgment, and their performance must be evaluated across relevant demographic and clinical groups.

In finance, networks can model transaction patterns, estimate risk, detect unusual activity, and process text. Because financial data are subject to distribution shifts, regulatory requirements, and costly false positives, performance evaluation must include stability, calibration, explainability, and operational controls.

Manufacturing applications include predictive maintenance, visual quality inspection, process optimization, and robotics. In retail and digital services, neural networks support recommendation, demand forecasting, search, advertising, and conversational interfaces. In language technology, they enable translation, speech recognition, summarization, question answering, and text generation. In scientific computing, they can approximate expensive simulations, discover patterns in experimental data, or assist inverse problems.

These applications share a common requirement: the model is only one component of a larger sociotechnical system. Data pipelines, human workflows, monitoring, security, governance, and accountability determine whether a technically accurate model produces beneficial outcomes.

Illustrative Case Study: Neural Inspection in Manufacturing

Background

Consider an illustrative case involving a manufacturer of electronic components. The company uses a camera-based inspection station to identify surface defects after assembly. Previously, inspectors examined every component manually. Inspection quality varied with fatigue, lighting, and production speed.

Situation and Problem

The organization wants to reduce missed defects without rejecting a large number of acceptable components. A neural network appears attractive because defects vary in shape, texture, and location, making a fixed collection of hand-written rules difficult to maintain.

Analysis

The data-science team first defines the task as image classification and localization rather than simply purchasing a model. Historical images are reviewed for labeling consistency. The team discovers that defective units are rare, that some defect types are absent from older records, and that camera settings changed across production lines. A random split of images would therefore risk placing nearly identical images from the same production batch in both training and test sets, producing an overly optimistic estimate.

The team creates a batch-based split, establishes a baseline using image-processing rules and a simpler classifier, and selects evaluation measures that reflect business costs. Recall is important because missed defects may reach customers, while precision matters because unnecessary rejection creates waste. A confusion matrix, defect-specific performance, calibration, and performance by production line are monitored.

Approach and Solution

A convolutional network is trained using labeled images, augmentation that reflects realistic changes in orientation and lighting, and a validation process separated from the final test set. The system does not make an irreversible decision in isolation. High-confidence normal items proceed automatically, high-confidence defects are diverted, and uncertain cases are sent to human inspectors. New examples are logged for periodic retraining, while data drift and camera performance are monitored.

Outcome

In the illustrative scenario, the combined human-machine workflow reduces inspection time and improves detection of common defects. However, rare new defects remain difficult to identify. The company therefore treats the network as a decision-support component, not as a complete replacement for inspection expertise.

Learning Points

The case demonstrates that neural-network success depends on representative data, appropriate splitting, task-specific metrics, human oversight, monitoring, and clear escalation procedures. A high benchmark score would not by itself establish production readiness. The case also illustrates the importance of considering the cost of errors rather than reporting accuracy alone.

Challenges, Risks, and Common Mistakes

A neural network can fit training data extremely well while performing poorly on unseen data; this is overfitting. It may also learn shortcuts, such as background, camera artifacts, or demographic proxies, rather than the intended concept. Data leakage occurs when information from evaluation data influences training, directly or indirectly. Leakage can make a model appear far more capable than it is.

Other challenges include class imbalance, noisy labels, missing data, distribution shift, adversarial or malicious inputs, high computational cost, and difficult debugging. Deep models may be difficult to interpret, but interpretability tools should not be treated as infallible explanations of internal reasoning. A visual heat map, for example, may indicate regions associated with a prediction without proving that the model used a valid causal rationale.

Common professional mistakes include selecting a model before defining the decision problem, using accuracy for imbalanced data, ignoring a simple baseline, splitting correlated observations randomly, tuning repeatedly on the test set, and deploying without monitoring. Another mistake is assuming that increased depth automatically produces better performance. Architectural complexity should be justified by evidence and operational need.

Ethical, Governance, and Professional Considerations

Neural networks can reproduce or amplify biases present in training data and institutional processes. In high-impact domains, teams should document data sources, intended use, known limitations, evaluation populations, and escalation procedures. Privacy must be protected through lawful collection, access control, minimization, retention policies, and appropriate technical safeguards.

Governance should address who is responsible for model decisions, how users can challenge outcomes, how incidents are reported, and when a model must be withdrawn or retrained. Security considerations include model theft, data poisoning, evasion attacks, and leakage of sensitive information. Professional responsibility requires communicating uncertainty and avoiding claims that exceed the evidence.

Regulatory requirements vary by jurisdiction and application. Regardless of the specific law, a defensible governance process includes risk classification, documentation, validation, auditability, human oversight where appropriate, and post-deployment monitoring. Ethical review should occur throughout the model lifecycle, not only after a system has been built.

Emerging Directions

Current neural-network research increasingly focuses on foundation models, multimodal learning, efficient adaptation, self-supervised learning, retrieval-augmented systems, and models that combine neural computation with tools or symbolic reasoning. At the same time, practical research emphasizes smaller models, energy efficiency, specialized hardware, privacy-preserving learning, robustness, and reliable uncertainty estimation.

Another important direction is the movement from isolated models toward compound AI systems. In such systems, a neural model may interact with a database, search engine, simulator, workflow tool, or human reviewer. Evaluation must then consider the behavior of the complete system, including tool errors, feedback loops, latency, and failure recovery.

The future development of neural networks will depend not only on scaling model size but also on improving data quality, reasoning reliability, interpretability, controllability, and efficiency. For professionals, the central question is not whether neural networks are universally superior, but when their representational power creates value that can be validated and governed.

Best Practices for Foundational Neural-Network Work

A sound project begins with a precise problem definition, an explicit target variable, and a clear account of the cost of errors. Teams should establish a simple baseline, inspect data quality, and design evaluation splits that reflect deployment conditions. They should choose metrics appropriate to the task, preserve a final test set, and record experiments reproducibly.

Architecture and hyperparameters should be selected through disciplined validation rather than intuition alone. Training data should be documented, sensitive attributes considered where legally and ethically appropriate, and subgroup performance examined. Before deployment, the system should undergo stress testing, security review, human-factors assessment, and operational monitoring. After deployment, drift, failures, feedback, and changes in the environment should be tracked.

The most reliable professional attitude is cautious empiricism: use the expressive power of neural networks, but verify claims against realistic evidence and maintain a procedure for detecting and correcting failure.

Chapter Summary

Neural networks are computational models composed of connected units whose weights transform inputs into outputs. Their broad inspiration comes from biological neurons, but artificial networks are mathematical abstractions rather than complete brain simulations. The artificial neuron computes a weighted sum, adds a bias, and applies an activation function. A perceptron implements a linear threshold classifier and converges on linearly separable data, but it cannot solve nonlinearly separable problems such as XOR.

Connectionism explains intelligent behavior through interacting units and learned connection strengths, while multilayer networks use nonlinear hidden representations to model complex functions. Universal approximation results establish theoretical expressive capacity, not guaranteed learnability, efficiency, interpretability, or generalization. The history of neural networks includes threshold models, Hebbian ideas, perceptrons, the revival of backpropagation, convolutional networks, and the modern deep-learning era enabled by data and computation.

Neural networks are a major family within machine learning and are used in perception, language, forecasting, control, recommendation, science, and decision support. Their successful application requires careful data design, appropriate evaluation, governance, monitoring, and human judgment. The foundational lesson is that a neural network is not merely an algorithm: it is part of a larger technical and organizational system whose value depends on evidence, context, and responsible deployment.

Key Terms

Artificial neuron: A mathematical unit that transforms weighted inputs through a bias and activation function.

Activation function: A function applied to a neuron's net input, usually introducing nonlinearity.

Bias: A trainable parameter that shifts a neuron's activation or decision boundary.

Weight: A trainable numerical parameter representing the strength and direction of an input connection.

Perceptron: A single-layer threshold-based neural classifier for linearly separable data.

Hyperplane: A flat decision boundary that generalizes a line or plane to higher-dimensional spaces.

Linear separability: The property that two classes can be divided by a single hyperplane.

Hidden layer: A layer between input and output layers whose representations are not directly observed as model outputs.

Feedforward network: A network in which information moves from input toward output without recurrent cycles.

Connectionism: An approach that explains cognition or computation through interacting units and adjustable connections.

Backpropagation: An algorithm for efficiently computing gradients of a loss with respect to network parameters.

Gradient descent: An optimization method that updates parameters in a direction intended to reduce loss.

Universal approximation: The theoretical result that suitable neural networks can approximate broad classes of functions under specified conditions.

Overfitting: Learning patterns specific to training data that do not generalize well to new data.

Generalization: The ability of a trained model to perform effectively on relevant unseen data.

Data leakage: Unintended use of information from evaluation or future data during model development.

Distribution shift: A change between the data used for development and the data encountered in deployment.

Deep learning: Machine learning based on neural networks with multiple layers of learned transformations.

Connection weight: Another term for a trainable parameter controlling the influence of one unit or feature on another.

Model governance: Processes for documenting, validating, monitoring, controlling, and assigning responsibility for a model.

Review Questions

Short Answer Questions

1. What are the roles of weights, bias, and activation function in an artificial neuron?

2. Why can a single-layer perceptron not represent the XOR function?

3. Define connectionism and distinguish it briefly from symbolic AI.

4. What does the universal approximation result establish, and what does it not establish?

5. Identify two ways in which an artificial neuron differs from a biological neuron.

Long Answer Questions

1. Explain the perceptron model, its learning rule, geometric interpretation, and convergence condition.

2. Discuss why nonlinear activation functions are essential in multilayer neural networks.

3. Trace the major historical developments from McCulloch–Pitts neurons to modern deep learning.

4. Explain the relationship among artificial intelligence, machine learning, deep learning, and neural networks.

5. Discuss the technical and organizational factors that determine whether a neural-network application succeeds in practice.

Analytical / Application Questions

1. A dataset contains two classes arranged in concentric circles. Explain why a single perceptron is unsuitable and propose a neural-network strategy that could represent the problem.

2. In the manufacturing case, why would a random image-level train-test split produce misleading results? Design a more realistic evaluation approach.

3. A neural network achieves 98% accuracy on a dataset in which only 2% of cases are defective. Analyze why accuracy may be inadequate and recommend additional metrics and deployment controls.

22. Multiple Choice Questions with Answers

Question 1

Which mathematical component allows a multilayer neural network to represent genuinely nonlinear functions?

A. Bias terms alone

B. Nonlinear activation functions

C. Input normalization alone

D. The number of output labels

Correct Answer: B. Nonlinear activation functions

Question 2

A single-layer perceptron is guaranteed to converge when the training classes are:

A. Randomly labeled

B. Highly imbalanced

C. Linearly separable

D. Nonstationary

Correct Answer: C. Linearly separable

Question 3

Why can a single perceptron not solve XOR?

A. It has no trainable weights

B. XOR requires a nonlinear decision boundary

C. XOR has no target values

D. Its learning rate is always zero

Correct Answer: B. XOR requires a nonlinear decision boundary

Question 4

What does the universal approximation result primarily indicate?

A. Every network will generalize perfectly

B. Suitable networks can approximate broad classes of functions under conditions

C. Training never requires optimization

D. A shallow network is always more efficient than a deep network

Correct Answer: B. Suitable networks can approximate broad classes of functions under conditions

Question 5

In an artificial neuron, the bias primarily:

A. Determines the number of training examples

B. Shifts the activation or decision boundary

C. Removes the need for weights

D. Converts labels into images

Correct Answer: B. Shifts the activation or decision boundary

Question 6

Which statement best describes the relationship between deep learning and machine learning?

A. Deep learning is unrelated to machine learning

B. Deep learning is machine learning based on multilayer neural networks

C. Machine learning is a subset of deep learning only

D. Deep learning refers only to symbolic rule systems

Correct Answer: B. Deep learning is machine learning based on multilayer neural networks

Question 7

In the manufacturing case, why were batch-based data splits preferred to random image-level splits?

A. They guarantee perfect accuracy

B. They prevent the model from using activation functions

C. They better test performance on genuinely distinct production batches

D. They eliminate the need for labels

Correct Answer: C. They better test performance on genuinely distinct production batches

Question 8

Which situation is an example of data leakage?

A. Using a validation set to compare models once

B. Including future information in features available during training

C. Separating training and test data by production batch

D. Monitoring performance after deployment

Correct Answer: B. Including future information in features available during training

Question 9

Why is accuracy potentially misleading when defective items represent only 2% of production?

A. Accuracy cannot be calculated for classifiers

B. A model predicting every item as normal could appear highly accurate

C. Accuracy always equals recall

D. Neural networks cannot process imbalanced data

Correct Answer: B. A model predicting every item as normal could appear highly accurate

Question 10

Which statement best reflects responsible neural-network deployment?

A. A high test score removes the need for monitoring

B. Model complexity should be increased regardless of operational need

C. A model should be evaluated in its deployment context and governed throughout its lifecycle

D. Human oversight is never appropriate for automated decisions

Correct Answer: C. A model should be evaluated in its deployment context and governed throughout its lifecycle