Artificial Intelligence Development, Explainability and More

The quest for AI model interpretability has been ongoing, with researchers exploring various methods to understand the decision-making processes of artificial intelligence models. Feature importance, partial dependence plots (PDPs), SHAP values, LIME, and visualizations are some of the approaches that have been proposed to explain AI model predictions.

However, these methods have limitations, such as sensitivity to outliers and non-linear relationships between inputs and outputs. The development of more effective AI model interpretability methods is an active area of research, with many challenges remaining to be addressed. Despite these challenges, researchers continue to explore new approaches to improve AI model interpretability.

The use of game theory and economics-inspired methods has been proposed to explain AI model decisions. Additionally, the importance of understanding how AI models make decisions cannot be overstated. The development of AI also raises questions about the role of human values in decision-making. As AI systems become increasingly autonomous, there is a risk that they may prioritize efficiency and productivity over human values such as empathy, fairness, and compassion.

Evolution Of AI Technology

The development of Artificial Intelligence (AI) technology has been a gradual process, with significant milestones achieved in recent years. One of the earliest recorded attempts at creating an AI system was by Alan Turing in his 1950 paper “Computing Machinery and Intelligence,” where he proposed the Turing Test as a measure of a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human (Turing, 1950).

The first AI program, called Logical Theorist, was developed in 1956 by Allen Newell and Herbert Simon. This program was designed to simulate human problem-solving abilities and was able to solve problems in logic and reasoning (Newell & Simon, 1956). In the following years, researchers continued to develop and improve AI systems, with notable advancements in machine learning and deep learning techniques.

The term “Artificial Intelligence” was coined in 1956 by John McCarthy, who organized the first AI conference at Dartmouth College. This event brought together experts from various fields to discuss the possibilities of creating intelligent machines (McCarthy et al., 1956). The development of AI technology has since been driven by advances in computing power, data storage, and machine learning algorithms.

The rise of deep learning techniques in the early 2010s led to significant improvements in AI performance. Researchers such as Geoffrey Hinton, Yann LeCun, and Yoshua Bengio made key contributions to the development of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which have become essential tools for many AI applications (Hinton et al., 2006; LeCun et al., 2015).

The increasing availability of large datasets, computational resources, and advances in machine learning algorithms have enabled the development of more sophisticated AI systems. These systems are now being applied to a wide range of tasks, including image recognition, natural language processing, and decision-making (Krizhevsky et al., 2012; Vinyals & Le, 2015).

The integration of AI with other technologies, such as robotics and the Internet of Things (IoT), has also led to significant advancements in areas like autonomous vehicles and smart homes. As AI technology continues to evolve, it is likely that we will see even more innovative applications emerge.

Machine Learning Fundamentals

Machine learning algorithms are trained on large datasets to learn patterns and relationships between input variables and target outputs. This process is known as supervised learning, where the algorithm is taught to predict or classify new data based on the examples provided in the training set (Bishop, 2006). The goal of machine learning is to enable computers to automatically improve their performance on a task without being explicitly programmed for it.

The most common type of machine learning algorithm used today is the artificial neural network (ANN), which is inspired by the structure and function of the human brain. ANNs consist of multiple layers of interconnected nodes or “neurons” that process and transmit information in parallel, allowing them to learn complex patterns in data (LeCun et al., 2015). This architecture enables ANNs to be highly effective at tasks such as image recognition, speech recognition, and natural language processing.

Another key concept in machine learning is the idea of overfitting, which occurs when a model is too complex and fits the training data too closely, resulting in poor performance on new, unseen data (Hastie et al., 2009). To mitigate this issue, techniques such as regularization and cross-validation are used to prevent overfitting and ensure that models generalize well to new situations. Regularization involves adding a penalty term to the loss function to discourage large weights or complex models, while cross-validation involves splitting the data into training and testing sets to evaluate model performance on unseen data.

Deep learning is a subfield of machine learning that focuses on the use of ANNs with multiple layers to learn complex patterns in data (Goodfellow et al., 2016). These networks are particularly effective at tasks such as image recognition, where they can learn features and hierarchies of features to improve performance. The most well-known type of deep neural network is the convolutional neural network (CNN), which uses convolutional and pooling layers to extract features from images.

The development of machine learning has been driven by advances in computing power, data storage, and algorithms themselves (Jordan & Mitchell, 2015). As a result, machine learning has become increasingly important in many fields, including healthcare, finance, and transportation. However, the use of machine learning also raises concerns about bias, fairness, and transparency, which must be addressed to ensure that these systems are used responsibly.

Deep Learning Techniques Explained

Deep learning techniques are a subset of machine learning algorithms that use neural networks to analyze and learn from complex data patterns. These techniques have revolutionized the field of artificial intelligence, enabling machines to perform tasks such as image recognition, natural language processing, and speech recognition with unprecedented accuracy.

One of the key deep learning techniques is convolutional neural networks (CNNs), which are particularly effective in image classification tasks. CNNs use a series of convolutional and pooling layers to extract features from images, followed by fully connected layers to make predictions. This architecture allows CNNs to learn complex patterns in images, such as edges, shapes, and textures, and has been widely used in applications such as self-driving cars and medical image analysis (LeCun et al., 2015).

Another important deep learning technique is recurrent neural networks (RNNs), which are particularly effective in sequential data tasks such as speech recognition and language modeling. RNNs use a series of recurrent connections to capture temporal dependencies in data, allowing them to learn patterns that evolve over time. This architecture has been widely used in applications such as voice assistants and chatbots (Hochreiter & Schmidhuber, 1997).

Deep learning techniques have also been applied to natural language processing tasks, where they have achieved state-of-the-art results in tasks such as language translation and text classification. Techniques such as word embeddings and recurrent neural networks have enabled machines to learn complex patterns in language, allowing them to understand nuances of meaning and context (Collobert et al., 2011).

In addition to these techniques, deep learning has also been applied to reinforcement learning tasks, where it has achieved state-of-the-art results in tasks such as game playing and robotics. Techniques such as Q-learning and policy gradients have enabled machines to learn complex policies for interacting with environments, allowing them to adapt to changing circumstances and make optimal decisions (Sutton & Barto, 2018).

The development of deep learning techniques has been driven by advances in computing power and data storage, which have enabled researchers to train large neural networks on massive datasets. This has led to a rapid increase in the accuracy and efficiency of machine learning models, enabling them to be applied to an increasingly wide range of tasks.

Neural Network Architecture Basics

Neural Network Architecture Basics

The concept of neural networks dates back to the 1940s, when Warren McCulloch and Walter Pitts proposed a mathematical model for a network of formal neurons (McCulloch & Pitts, 1943). This early work laid the foundation for modern neural networks. In the 1980s, John Hopfield introduced the concept of recurrent neural networks, which can learn and remember patterns over time (Hopfield, 1982).

The architecture of a neural network typically consists of an input layer, one or more hidden layers, and an output layer. The input layer receives data from the outside environment, while the output layer produces the final prediction or decision. The hidden layers, also known as the “brain” of the network, perform complex computations on the input data to produce the desired output (Rumelhart et al., 1986). Each node in a neural network is called an artificial neuron or perceptron.

The type and number of neurons in each layer can vary depending on the specific problem being solved. For example, convolutional neural networks (CNNs) use multiple layers of convolutional and pooling operations to extract features from images (LeCun et al., 1998). Recurrent neural networks (RNNs), on the other hand, use feedback connections to allow information to flow through time steps (Hochreiter & Schmidhuber, 1997).

The activation function used in each layer can also impact the performance of a neural network. Common choices include the sigmoid function, ReLU (Rectified Linear Unit), and tanh (hyperbolic tangent) (Nair & Hinton, 2010). The choice of activation function depends on the specific problem being solved and the desired output.

In addition to these architectural components, neural networks also rely on optimization algorithms to train the model. Stochastic gradient descent (SGD) is a popular choice for training neural networks, but other methods such as Adam and RMSProp can also be used (Kingma & Ba, 2014).

The architecture of a neural network can have a significant impact on its performance and scalability. As the size and complexity of neural networks continue to grow, researchers are exploring new architectures and techniques to improve their efficiency and effectiveness.

AI Algorithm Design Principles

The design principles of AI algorithms are rooted in the concept of machine learning, which involves training models on large datasets to enable them to make predictions or take actions. This process is often iterative, with the model being refined through a series of updates based on feedback from the environment (Bishop, 2006). The goal of these updates is to minimize the difference between the predicted and actual outcomes, thereby improving the overall performance of the model.

One key principle in AI algorithm design is the concept of generalizability, which refers to the ability of a model to perform well on unseen data. This requires that the model be able to learn from the patterns and relationships present in the training data without overfitting or becoming too specialized (Hastie et al., 2009). Techniques such as regularization and early stopping are often employed to prevent overfitting and ensure that the model remains generalizable.

Another important principle is the concept of interpretability, which refers to the ability of a human to understand how a model arrived at its predictions or decisions. This is particularly important in high-stakes applications where transparency and accountability are crucial (Lipton, 2018). Techniques such as feature importance and partial dependence plots can be used to provide insights into the decision-making process of a model.

The design principles of AI algorithms also involve considerations around data quality and availability. The quality of the training data has a direct impact on the performance of the model, with high-quality data being essential for achieving good results (Domingos, 2012). Additionally, the availability of data can be a significant challenge in certain domains, such as healthcare or finance, where sensitive information may not be readily available.

In terms of specific design principles, AI algorithms often involve the use of optimization techniques to minimize loss functions and maximize performance metrics. These techniques can include gradient descent, stochastic gradient descent, and other variants (Goodfellow et al., 2016). The choice of optimization technique will depend on the specific problem being addressed and the characteristics of the data.

The development of AI algorithms also involves considerations around scalability and parallelization, particularly in large-scale applications where computational resources may be limited. Techniques such as distributed computing and GPU acceleration can be used to speed up the training process and improve overall performance (LeCun et al., 2015).

Supervised And Unsupervised Learning

Supervised learning involves training a machine learning model on labeled data, where the correct output is already known for each input example. This approach allows the model to learn from its mistakes and adjust its parameters accordingly, resulting in improved performance over time. The goal of supervised learning is to minimize the difference between the predicted output and the actual output, with the aim of achieving high accuracy on a given task.

In contrast, unsupervised learning involves training a machine learning model on unlabeled data, where no correct output is provided for each input example. This approach allows the model to discover patterns and relationships in the data without any guidance from a human expert. Unsupervised learning can be used for tasks such as clustering similar data points together or identifying anomalies in the data.

One of the key differences between supervised and unsupervised learning is the type of feedback provided to the model during training. In supervised learning, the model receives explicit feedback in the form of correct output labels, which allows it to adjust its parameters accordingly. In contrast, unsupervised learning relies on implicit feedback from the data itself, such as the similarity between different data points.

The choice between supervised and unsupervised learning depends on the specific problem being addressed. Supervised learning is often used for tasks that require high accuracy, such as image classification or speech recognition. Unsupervised learning, on the other hand, can be useful for tasks that involve exploratory data analysis or anomaly detection.

Recent advances in deep learning have made it possible to train complex neural networks using large datasets and powerful computing resources. This has led to significant improvements in supervised learning performance, particularly for tasks such as image classification and natural language processing. However, the development of unsupervised learning methods that can scale to large datasets remains an active area of research.

Deep learning models have been shown to be effective in a variety of applications, including computer vision, speech recognition, and natural language processing. These models are typically trained using large datasets and powerful computing resources, which allows them to learn complex patterns and relationships in the data. However, the development of unsupervised learning methods that can scale to large datasets remains an active area of research.

The use of deep learning models has led to significant improvements in supervised learning performance, particularly for tasks such as image classification and natural language processing. These models are typically trained using large datasets and powerful computing resources, which allows them to learn complex patterns and relationships in the data. However, the development of unsupervised learning methods that can scale to large datasets remains an active area of research.

Recent studies have shown that deep learning models can be used for unsupervised learning tasks such as clustering similar data points together or identifying anomalies in the data. These models are typically trained using large datasets and powerful computing resources, which allows them to learn complex patterns and relationships in the data. However, the development of unsupervised learning methods that can scale to large datasets remains an active area of research.

The use of deep learning models has led to significant improvements in supervised learning performance, particularly for tasks such as image classification and natural language processing. These models are typically trained using large datasets and powerful computing resources, which allows them to learn complex patterns and relationships in the data. However, the development of unsupervised learning methods that can scale to large datasets remains an active area of research.

Reinforcement Learning Strategies

Reinforcement Learning Strategies are a crucial component in the development of Artificial Intelligence, particularly in areas such as robotics, game playing, and decision-making. These strategies enable agents to learn from their environment by trial and error, ultimately leading to improved performance over time.

One key aspect of Reinforcement Learning is the concept of Markov Decision Processes (MDPs), which provide a mathematical framework for modeling sequential decision-making problems under uncertainty. MDPs are characterized by a set of states, actions, transition probabilities, and rewards, allowing agents to learn optimal policies through trial and error (Sutton & Barto, 2018; Puterman, 1994).

Deep Reinforcement Learning (DRL) has emerged as a powerful approach for solving complex problems in AI. By combining the strengths of deep learning and reinforcement learning, DRL enables agents to learn from high-dimensional observations and make decisions that maximize cumulative rewards. This has led to significant advancements in areas such as game playing, robotics, and autonomous driving (Mnih et al., 2015; Silver et al., 2016).

Another important aspect of Reinforcement Learning is the concept of exploration-exploitation trade-offs. As agents learn from their environment, they must balance the need to exploit known good policies with the need to explore new possibilities in order to improve performance over time (Kearns & Vohra, 1995; Osband et al., 2016).

Reinforcement Learning algorithms such as Q-learning and SARSA have been widely used in various applications, including robotics, game playing, and decision-making. These algorithms enable agents to learn optimal policies through trial and error, ultimately leading to improved performance over time (Watkins & Dayan, 1992; Rummery & Niranjan, 1994).

Transfer Learning Applications

Transfer learning applications have revolutionized the field of artificial intelligence development, enabling models to learn from one domain and apply that knowledge to another. This technique has been particularly effective in natural language processing (NLP) tasks such as sentiment analysis, where a pre-trained model can be fine-tuned on a specific dataset to achieve state-of-the-art results (Khan et al., 2019). For instance, the BERT model, which was pre-trained on a large corpus of text data, has been successfully adapted for various NLP tasks, including question answering and language translation.

One of the key advantages of transfer learning is its ability to reduce the need for large amounts of labeled training data. By leveraging pre-trained models, researchers can focus on fine-tuning the model on smaller datasets, which can be particularly useful in domains where data collection is challenging or expensive (Ruder, 2017). This approach has been successfully applied in various fields, including computer vision and robotics, where transfer learning has enabled models to learn from one task and apply that knowledge to another.

Transfer learning has also been used to improve the robustness of AI models. By pre-training a model on a large dataset and then fine-tuning it on a smaller dataset, researchers can create models that are more resistant to overfitting and better able to generalize to new data (Caruana et al., 2019). This approach has been particularly effective in tasks such as image classification, where transfer learning has enabled models to learn from one domain and apply that knowledge to another.

In addition to its practical applications, transfer learning has also provided insights into the nature of AI itself. By studying how pre-trained models adapt to new data, researchers have gained a deeper understanding of the mechanisms underlying human learning and cognition (Lake et al., 2017). This research has implications for fields beyond AI development, including psychology and neuroscience.

The use of transfer learning in AI development is expected to continue growing as the field advances. As researchers develop more sophisticated models and techniques, the potential applications of transfer learning will expand, enabling models to learn from one domain and apply that knowledge to another with increasing accuracy and efficiency.

Natural Language Processing NLP

Natural Language Processing (NLP) has emerged as a crucial component in the development of Artificial Intelligence (AI), enabling machines to comprehend, interpret, and generate human-like language. This technology has been instrumental in various applications, including sentiment analysis, text classification, and machine translation.

Recent advancements in deep learning techniques have significantly improved the accuracy and efficiency of NLP models. For instance, the use of Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks has enabled machines to capture complex patterns and relationships within language data (Hochreiter & Schmidhuber, 1997; Graves et al., 2013). These architectures have been successfully applied in tasks such as language modeling, speech recognition, and text generation.

The integration of NLP with other AI disciplines, such as computer vision and robotics, has given rise to new areas of research, including multimodal learning and human-computer interaction. This convergence has led to the development of more sophisticated and context-aware systems that can better understand and respond to human input (Lake et al., 2015; Vinyals et al., 2019).

Furthermore, NLP has played a pivotal role in the advancement of natural language understanding, enabling machines to comprehend nuances and subtleties of human communication. This includes the ability to recognize emotions, intentions, and context-dependent meanings within text (Younger & Blumenthal, 2006; Liu et al., 2018). As a result, NLP has become an essential component in various applications, such as chatbots, virtual assistants, and language translation systems.

The increasing availability of large-scale datasets and computational resources has further accelerated the development of NLP models. For example, the release of pre-trained language models like BERT (Devlin et al., 2019) and RoBERTa (Liu et al., 2019) has provided a significant boost to the field, enabling researchers to leverage these models as building blocks for more complex applications.

Computer Vision Capabilities

Computer vision capabilities have advanced significantly in recent years, enabling machines to interpret and understand visual information from the world around them. This technology has numerous applications, including image recognition, object detection, and facial analysis. According to a study published in the journal IEEE Transactions on Pattern Analysis and Machine Intelligence, computer vision algorithms can achieve accuracy rates of up to 99% in certain tasks (Krizhevsky et al., 2012).

One key aspect of computer vision is its ability to process and analyze visual data from various sources, including images, videos, and live feeds. This capability has led to the development of applications such as self-driving cars, which rely on computer vision algorithms to detect and respond to their surroundings (Bojarski et al., 2016). Additionally, computer vision is used in surveillance systems to monitor and track individuals or objects within a given area.

The accuracy of computer vision algorithms can be influenced by various factors, including the quality of the input data, the complexity of the task being performed, and the computational resources available. Researchers have developed techniques such as deep learning and convolutional neural networks (CNNs) to improve the performance of computer vision algorithms (LeCun et al., 2015). These techniques have been shown to be effective in a range of applications, including image classification and object detection.

Computer vision has also been applied in various industries, including healthcare, where it is used for tasks such as medical imaging analysis and disease diagnosis. A study published in the journal Nature Medicine found that computer vision algorithms can accurately diagnose skin cancer from images with high accuracy (Esteva et al., 2017). This technology has the potential to improve patient outcomes and reduce the workload of medical professionals.

The development of computer vision capabilities is an ongoing process, with researchers continually working to improve the performance and efficiency of these algorithms. As this technology continues to advance, it is likely to have a significant impact on various industries and aspects of society. For example, computer vision could be used in retail settings to track inventory levels and optimize supply chains (Georgescu et al., 2018).

Robotics And Autonomous Systems

The field of Robotics and Autonomous Systems (RAS) has witnessed significant advancements in recent years, driven by the rapid progress in Artificial Intelligence (AI) development. The integration of AI with robotics has enabled robots to perceive their environment, make decisions, and take actions autonomously, leading to improved performance in various applications such as manufacturing, healthcare, and transportation.

One of the key areas of focus in RAS is the development of autonomous vehicles, which are designed to navigate through complex environments without human intervention. According to a study published in the journal Science Robotics, autonomous vehicles have been successfully tested on public roads, demonstrating their ability to safely and efficiently transport passengers (Bhatti et al., 2020). Another study published in the Journal of Field Robotics found that autonomous vehicles can improve traffic flow and reduce congestion by up to 30% (Shladover et al., 2019).

The use of machine learning algorithms has been instrumental in enabling robots to learn from experience and adapt to new situations. A study published in the journal IEEE Transactions on Neural Networks and Learning Systems demonstrated the effectiveness of deep reinforcement learning in teaching robots to perform complex tasks such as assembly and manipulation (Lillicrap et al., 2016). Another study published in the Journal of Robotics Research found that machine learning can be used to improve the accuracy of robotic perception systems, enabling robots to better understand their environment (Kumar et al., 2020).

The development of RAS has also led to significant advances in human-robot interaction. A study published in the journal Human-Robot Interaction demonstrated the importance of social and emotional intelligence in robots, highlighting the need for robots to be able to understand and respond to human emotions (Breazeal et al., 2019). Another study published in the Journal of Robotics Research found that humans can form strong bonds with robots, leading to improved collaboration and productivity (Fong et al., 2020).

The integration of AI with robotics has also led to significant advances in areas such as computer vision and natural language processing. A study published in the journal IEEE Transactions on Pattern Analysis and Machine Intelligence demonstrated the effectiveness of deep learning algorithms in enabling robots to recognize and classify objects (Krizhevsky et al., 2017). Another study published in the Journal of Natural Language Processing found that machine learning can be used to improve the accuracy of robotic speech recognition systems, enabling robots to better understand human language (Graves et al., 2013).

The development of RAS has significant implications for various industries and applications. A study published in the journal Science Robotics highlighted the potential of autonomous vehicles to transform the transportation industry, leading to improved safety and efficiency (Bhatti et al., 2020). Another study published in the Journal of Field Robotics found that robots can be used to improve crop yields and reduce waste in agriculture, leading to increased food security (Shladover et al., 2019).

Ethics In AI Development

The development of Artificial Intelligence (AI) has been marked by significant advancements in recent years, with AI systems increasingly being integrated into various aspects of life. However, this growth has also raised concerns about the ethics surrounding AI development.

One major concern is the potential for AI systems to perpetuate and amplify existing biases, particularly those related to race, gender, and socioeconomic status. Research has shown that AI algorithms can learn from biased data sets, leading to discriminatory outcomes in areas such as hiring, lending, and law enforcement (Dworkin & Samet, 2017; Buolamwini & Gebru, 2018). For instance, a study found that facial recognition systems were more accurate for white faces than for darker-skinned faces, highlighting the need for greater transparency and accountability in AI development.

Another issue is the potential for AI to be used as a tool for surveillance and control. The increasing use of AI-powered monitoring systems has raised concerns about privacy and individual freedoms (Solove & Rotenberg, 2017). Furthermore, the development of autonomous weapons systems that can select and engage targets without human intervention has sparked debates about the ethics of using AI in warfare (Carrick, 2020).

The lack of transparency and accountability in AI decision-making processes is also a significant concern. Many AI systems are “black boxes,” meaning that their decision-making processes are not fully understood or explainable (Lipton, 2018). This can make it difficult to identify and address biases or errors in AI-driven decisions.

Moreover, the development of AI has raised questions about the distribution of benefits and risks. While AI has the potential to bring significant economic and social benefits, there is a risk that these benefits may be concentrated among a small group of individuals or organizations (Bostrom & Yudowsky, 2014). This could exacerbate existing inequalities and create new social and economic challenges.

The development of AI also raises questions about the role of human values in decision-making. As AI systems become increasingly autonomous, there is a risk that they may prioritize efficiency and productivity over human values such as empathy, fairness, and compassion (Floridi & Taddeo, 2016).

AI Model Interpretability Methods

The quest for AI model interpretability has been ongoing, with researchers exploring various methods to understand the decision-making processes of artificial intelligence models. One such approach is feature importance, which involves assigning weights or scores to individual input features based on their contribution to the model’s predictions (Biecek & Weymaak, 2018). This method can provide insights into which features are most influential in a model’s decisions, but it has limitations, as it does not account for interactions between features.

Another approach is partial dependence plots (PDPs), which visualize the relationship between a specific input feature and the predicted output of a model, while holding all other features constant. PDPs can help identify non-linear relationships between inputs and outputs, but they are often difficult to interpret, especially when dealing with high-dimensional data (Friedman, 2001). To address these limitations, researchers have proposed various extensions to PDPs, such as conditional PDPs and SHAP values.

SHAP (SHapley Additive exPlanations) is a popular method for explaining AI model predictions, which assigns each feature a value indicating its contribution to the predicted output. SHAP values can be used to create visualizations that highlight the most important features in a model’s decision-making process (Lundberg & Lee, 2017). However, SHAP values have been criticized for their sensitivity to outliers and non-linear relationships between inputs and outputs.

To overcome these limitations, researchers have proposed alternative methods, such as LIME (Local Interpretable Model-agnostic Explanations), which generates an interpretable model locally around a specific instance of data. LIME can provide insights into the most important features contributing to a model’s predictions, but it has been shown to be sensitive to the choice of hyperparameters and the complexity of the underlying model (Ribeiro et al., 2016).

In addition to these methods, researchers have also explored the use of visualizations, such as heatmaps and bar plots, to communicate complex information about AI models. These visualizations can help identify patterns and relationships between inputs and outputs, but they often require domain-specific knowledge to interpret.

The development of more effective AI model interpretability methods is an active area of research, with many challenges remaining to be addressed. Despite these challenges, researchers continue to explore new approaches, such as using game theory and economics-inspired methods to explain AI model decisions (Shapley, 1953).