This file aims to give a thorough overview of the current state and future prospects of interpretability and explainability in deep learning, making it a valuable resource for students, researchers, and professionals in the field. The post will comprehensively cover the following aspects: Introduction to Interpretability and Explainability: Explaining what these concepts mean in the context of deep learning and why they are critical. The Need for Transparency: Discussing the importance of interpretability and explainability in AI, focusing on ethical considerations, trust in AI systems, and regulatory compliance. Key Concepts and Definitions: Clarifying terms like “black-box” models, interpretability, explainability, and their relevance in deep learning. Methods and Techniques: Visualization Techniques: Detailing methods like feature visualization, attention mechanisms, and tools like Grad-CAM. Feature Importance Analysis: Exploring techniques like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) for understanding feature contributions. Decision Boundary Analysis: Discussing methods to analyze and visualize the decision boundaries of models. Practical Implementations and Code Examples: Providing examples of how these techniques can be implemented using popular deep learning frameworks like TensorFlow or PyTorch. Case Studies and Real-World Applications: Presenting real-world scenarios where interpretability and explainability have played a vital role, especially in fields like healthcare, finance, and autonomous systems. Challenges and Limitations: Addressing the challenges in achieving interpretability and the trade-offs with model complexity and performance. Future Directions and Research Trends: Discussing ongoing research, emerging trends, and potential future advancements in making deep learning models more interpretable and explainable. Conclusion: Summarizing the key takeaways and the importance of continued efforts in this area. References and Further Reading: Providing a list of academic papers, articles, and resources for readers who wish to delve deeper into the topic. Section 1: Introduction to Interpretability and Explainability The field of deep learning has witnessed exponential growth in recent years, leading to significant advancements in various applications such as image recognition, natural language processing, and autonomous systems. However, as these neural network models become increasingly complex, they often resemble “black boxes”, where the decision-making process is not transparent or understandable to users. This obscurity raises concerns, especially in critical applications, and underscores the need for interpretability and explainability in deep learning models. What are Interpretability and Explainability? Interpretability: This refers to the degree to which a human can understand the cause of a decision made by a machine learning model. It’s about answering the questio

1. Improved Interpretability and Explainability of Deep
Learning Models
This file aims to give a thorough overview of the current state and future prospects of
interpretability and explainability in deep learning, making it a valuable resource for students,
researchers, and professionals in the field. The post will comprehensively cover the following
aspects:
● Introduction to Interpretability and Explainability: Explaining what these concepts
mean in the context of deep learning and why they are critical.
● The Need for Transparency: Discussing the importance of interpretability and
explainability in AI, focusing on ethical considerations, trust in AI systems, and regulatory
compliance.
● Key Concepts and Definitions: Clarifying terms like “black-box” models, interpretability,
explainability, and their relevance in deep learning.
● Methods and Techniques:
○ Visualization Techniques: Detailing methods like feature visualization, attention
mechanisms, and tools like Grad-CAM.
○ Feature Importance Analysis: Exploring techniques like SHAP (SHapley
Additive exPlanations) and LIME (Local Interpretable Model-agnostic
Explanations) for understanding feature contributions.
○ Decision Boundary Analysis: Discussing methods to analyze and visualize the
decision boundaries of models.
● Practical Implementations and Code Examples: Providing examples of how these
techniques can be implemented using popular deep learning frameworks like
TensorFlow or PyTorch.
● Case Studies and Real-World Applications: Presenting real-world scenarios where
interpretability and explainability have played a vital role, especially in fields like
healthcare, finance, and autonomous systems.
● Challenges and Limitations: Addressing the challenges in achieving interpretability and
the trade-offs with model complexity and performance.
● Future Directions and Research Trends: Discussing ongoing research, emerging
trends, and potential future advancements in making deep learning models more
interpretable and explainable.
● Conclusion: Summarizing the key takeaways and the importance of continued efforts in
this area.
● References and Further Reading: Providing a list of academic papers, articles, and
resources for readers who wish to delve deeper into the topic.

2. Section 1: Introduction to Interpretability and
Explainability
The field of deep learning has witnessed exponential growth in recent years, leading to
significant advancements in various applications such as image recognition, natural language
processing, and autonomous systems. However, as these neural network models become
increasingly complex, they often resemble “black boxes”, where the decision-making process is
not transparent or understandable to users. This obscurity raises concerns, especially in critical
applications, and underscores the need for interpretability and explainability in deep learning
models.
What are Interpretability and Explainability?
● Interpretability: This refers to the degree to which a human can understand the cause
of a decision made by a machine learning model. It’s about answering the question,
“Why did the model make this prediction?” Interpretability is crucial in validating the
model’s behavior and ensuring it aligns with real-world expectations.
● Explainability: Closely related to interpretability, explainability involves the ability to
explain both the processes and results of the model in human terms. It’s about
conveying an understanding of the model’s mechanisms in a comprehensible way.
Why are They Important?
● Trust and Reliability: For users and stakeholders to trust AI-driven decisions, especially
in high-stakes domains like healthcare or finance, it’s essential they understand how
these decisions are made.
● Ethical AI Practices: Understanding model decisions is critical for identifying and
mitigating biases, ensuring fair and ethical AI practices.
● Regulatory Compliance: With regulations like the EU’s General Data Protection
Regulation (GDPR), there’s increasing legal emphasis on the transparency of AI
systems, particularly in terms of how personal data is used in decision-making.
The “Black Box” Challenge
Deep learning models, especially those with complex architectures like deep neural networks,
often operate as “black boxes.” While they can achieve high accuracy, the intricacies of their
internal decision paths are not easily decipherable. This lack of transparency can be
problematic in scenarios where understanding the rationale behind a decision is as important as
the decision itself.
Bridging the Gap
The goal of improved interpretability and explainability is to bridge the gap between AI
performance and human understanding. This involves developing methodologies and tools that

3. can shed light on the internal workings of complex models, thereby making AI more transparent
and accountable.
Section 2: The Importance of Transparency in AI
The Imperative of Understanding AI Decisions
In this section, we delve into the significance of transparency in AI systems, especially those
powered by deep learning. The increasing deployment of AI in various sectors necessitates a
clear understanding of how these systems make decisions, and more importantly, why these
decisions are made.
Trust and Credibility in AI Systems
● Building Trust: For users to rely on and accept AI-driven decisions, particularly in
high-stakes areas like healthcare, law enforcement, or financial services, there must be
a foundational level of trust. This trust is primarily built through transparency and the
ability to understand and verify AI decisions.
● Credibility and Reliability: The credibility of an AI system is closely tied to its
transparency. A system that can explain its decisions is more likely to be perceived as
reliable and credible.
Ethical and Fair AI Practices
● Detecting and Correcting Biases: AI systems can inadvertently learn and perpetuate
biases present in their training data. Transparency in AI helps in identifying such biases
and ensuring decisions are fair and ethical.
● Ensuring Accountability: When AI systems make decisions that affect people’s lives,
it’s crucial to have accountability mechanisms in place. Transparency facilitates
accountability by making it possible to trace and understand the decision-making
process.
Regulatory and Legal Compliance
● Adhering to Regulations: With the growing focus on data privacy and ethical AI,
regulations like the GDPR in Europe emphasize the need for explainable AI. Compliance
with such regulations is not only a legal requirement but also an ethical responsibility.
● Legal Justification of Decisions: In some scenarios, especially in legal or financial
contexts, AI decisions may need to be justified in court or to regulatory bodies.
Transparency and explainability enable this justification.
Section 3: Key Concepts and Definitions in AI
Interpretability and Explainability

4. Delineating Core Concepts
This section provides a deeper understanding of the fundamental concepts underpinning
interpretability and explainability in AI. It clarifies essential terms and their significance in the
context of deep learning.
1. Interpretability: This concept pertains to the extent to which a human can comprehend
and consistently predict a model’s outcome. Interpretability is often categorized into two
types:
○ Intrinsic Interpretability: This is inherent in simpler models where the
decision-making process is readily understandable (e.g., decision trees).
○ Post-hoc Interpretability: This applies to complex models (like deep neural
networks) and involves techniques used after model training to explain its
decisions.
2. Explainability: While closely related to interpretability, explainability goes a step further.
It’s not just about a model’s decisions being understandable, but also about being able to
explain them in human terms. This involves conveying the model’s functionality and
decision-making process in a way that humans can grasp.
3. Transparency: Often used interchangeably with interpretability and explainability,
transparency in AI refers to the clarity and openness with which a model’s mechanisms
and decisions can be understood by humans.
4. The Black Box Problem: This term describes the situation where the internal workings
of a model (especially in complex neural networks) are not visible or understandable.
The challenge is to open this ‘black box’ to make AI decisions more transparent and
accountable.
Importance of These Concepts
● These concepts are crucial for establishing trust, ethical compliance, and practical
applicability of AI in sensitive and impactful domains.
● Understanding these terms is the first step in addressing the challenges posed by
complex AI models in terms of their interpretability and accountability.
Section 4: Methods and Techniques for AI Interpretability
and Explainability
Overview
In this section, we delve into various methods and techniques employed to enhance the
interpretability and explainability of deep learning models. These methodologies provide insights
into how AI models make decisions, thereby making these processes more transparent.
Visualization Techniques

5. 1. Feature Visualization:
○ Purpose: Helps in understanding what features a model is focusing on.
○ Techniques: Includes creating activation maps and saliency maps.
○ Applications: Useful in models where visual input plays a key role, like image
classification.
○ Reference: “Visualizing and Understanding Convolutional Networks” by Zeiler
and Fergus provides foundational insights into feature visualization in CNNs.
2. Grad-CAM:
○ Purpose: Provides insights into which regions of the input image are important
for predictions.
○ Technique: Uses gradients flowing into the final convolutional layer for
localization.
○ Applications: Widely used in image recognition tasks for understanding model
focus areas.
○ Reference: The original Grad-CAM paper by Ramprasaath R. Selvaraju et al.
offers a comprehensive understanding of this method.
Feature Importance Analysis
1. SHAP (SHapley Additive exPlanations):
○ Purpose: To interpret the impact of having certain values for predictor variables.
○ Technique: SHAP values are calculated to show the contribution of each feature
to the prediction.
○ Applications: Useful in complex models for both global and local explanations.
○ Reference: “A Unified Approach to Interpreting Model Predictions” by Scott
Lundberg and Su-In Lee provides a detailed discussion on SHAP.
2. LIME (Local Interpretable Model-agnostic Explanations):
○ Purpose: To explain individual predictions regardless of the classifier used.
○ Technique: Approximates complex models locally with an interpretable model.
○ Applications: Can be used across various types of models for local
explanations.
○ Reference: The foundational paper on LIME by Marco Tulio Ribeiro et al.
outlines the methodology in detail.
Decision Boundary Analysis
1. Decision Trees as Surrogate Models:
○ Purpose: To approximate complex model decision boundaries with simpler
models.
○ Technique: A decision tree is trained to mimic the predictions of a complex
model.
○ Applications: Useful for explaining complex models in a more understandable
format.
○ Reference: “Interpretable Machine Learning” by Christoph Molnar discusses
surrogate models as a means of interpretability.

6. 2. Sensitivity Analysis:
○ Purpose: To understand how slight changes in input affect the model’s output.
○ Technique: Involves perturbing inputs and observing the variation in outputs.
○ Applications: Important in models where input features are closely interrelated.
○ Reference: “Sensitivity Analysis in Neural Networks” by Saltelli and Annoni
provides insights into this approach.
Section 5: Practical Implementations and Code Examples
Demonstrating Concepts Through Real Code
In this section, the focus is on practical implementations, providing code examples for various
interpretability and explainability techniques in AI. These examples will help bridge the gap
between theory and hands-on application, allowing for a deeper understanding of how
interpretability is achieved in practice. They serve as a starting point for exploring these
methods in greater depth. For more complex models or specific use cases, further
customization and deeper understanding will be required.
Example 1: SHAP in a Machine Learning Model
SHAP (SHapley Additive exPlanations) offers insights into the contribution of each feature in a
prediction. Here’s a basic Python example using SHAP with a tree-based model:
import shap
import xgboost
from sklearn.model_selection import train_test_split
import pandas as pd
# Load a sample dataset
data = pd.read_csv('sample_data.csv')
X = data.drop('target', axis=1)
y = data['target']
# Split the dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Train an XGBoost model
model = xgboost.XGBClassifier().fit(X_train, y_train)
# Initialize SHAP explainer and calculate SHAP values
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)
# Plot SHAP values (for the first prediction in the test set)

7. shap.force_plot(explainer.expected_value, shap_values[0,:], X_test.iloc[0,:])
Example 2: Grad-CAM with a CNN in PyTorch
Grad-CAM is a technique used to visualize the areas in an input image that are important for a
CNN’s decision. Here’s a simple example using PyTorch:
import torch
from torchvision import models, transforms
from PIL import Image
import matplotlib.pyplot as plt
# Function to apply Grad-CAM
def apply_gradcam(model, image_path):
# Preprocess the image
preprocess = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
img = Image.open(image_path)
input_tensor = preprocess(img).unsqueeze(0)
# Forward pass
output = model(input_tensor)
output_idx = output.argmax()
output_max = output[0, output_idx]
# Backward pass
model.zero_grad()
output_max.backward()
gradients = model.get_activations_gradient()
pooled_gradients = torch.mean(gradients, dim=[0, 2, 3])
# Get the activations and weight them
activations = model.get_activations(input_tensor).detach()
for i in range(activations.shape[1]):
activations[:, i, :, :] *= pooled_gradients[i]
# Generate heatmap
heatmap = torch.mean(activations, dim=1).squeeze()
heatmap = np.maximum(heatmap, 0)
heatmap /= torch.max(heatmap)
plt.matshow(heatmap.squeeze())

8. # Load a pre-trained model
model = models.vgg16(pretrained=True)
# Register hooks to access the gradients and activations
model.register_backward_hooks()
model.register_forward_hooks()
# Apply Grad-CAM
apply_gradcam(model, 'path_to_image.jpg')
Example 3: LIME (Local Interpretable Model-agnostic Explanations)
LIME explains predictions of machine learning models by locally approximating them with
interpretable models.
import lime
import lime.lime_tabular
import sklearn.ensemble
import numpy as np
# Prepare the dataset and model
iris = sklearn.datasets.load_iris()
train, test, labels_train, labels_test = sklearn.model_selection.train_test_split(iris.data, iris.target,
train_size=0.80)
rf = sklearn.ensemble.RandomForestClassifier(n_estimators=500)
rf.fit(train, labels_train)
# Initialize LIME explainer
explainer = lime.lime_tabular.LimeTabularExplainer(train, feature_names=iris.feature_names,
class_names=iris.target_names, discretize_continuous=True)
# Choose a sample to explain
idx = 1
exp = explainer.explain_instance(test[idx], rf.predict_proba, num_features=2)
# Display the explanation
exp.show_in_notebook(show_table=True, show_all=False)
Example 4: Decision Trees as Surrogate Models
Using decision trees to approximate complex models provides an interpretable view of their
decision process.
from sklearn.tree import DecisionTreeClassifier, export_text
from sklearn.model_selection import train_test_split

9. from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
# Load data and create a complex model
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=0)
complex_model = RandomForestClassifier(n_estimators=100, random_state=0).fit(X_train,
y_train)
# Train a decision tree as a surrogate model
surrogate = DecisionTreeClassifier(max_depth=3)
surrogate.fit(X_train, complex_model.predict(X_train))
# Display the rules
tree_rules = export_text(surrogate, feature_names=iris['feature_names'])
print(tree_rules)
Example 5: Sensitivity Analysis
Sensitivity analysis involves varying input features to see how they affect the output, giving
insights into the model’s dependence on certain features.
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestRegressor
from sklearn.datasets import load_boston
# Load data
boston = load_boston()
X = boston.data
y = boston.target
feature_names = boston.feature_names
# Train a model
model = RandomForestRegressor()
model.fit(X, y)
# Choose a feature for sensitivity analysis
feature_idx = 5 # 'RM' - average number of rooms
x_vals = np.linspace(min(X[:, feature_idx]), max(X[:, feature_idx]), 100)
predictions = []
# Vary the feature and observe the change in predictions
for val in x_vals:
X_temp = np.copy(X)

10. X_temp[:, feature_idx] = val
predictions.append(model.predict(X_temp).mean())
# Plot
plt.figure(figsize=(10, 6))
plt.plot(x_vals, predictions, label=feature_names[feature_idx])
plt.xlabel(feature_names[feature_idx])
plt.ylabel('Predicted Median Value')
plt.title('Sensitivity Analysis of Feature')
plt.legend()
plt.show()
Section 6: Case Studies and Real-World Applications
Understanding Through Practical Examples
This section highlights various case studies and real-world applications that demonstrate the
importance and effectiveness of interpretability and explainability in AI. These examples offer
insights into how these concepts are applied in different industries and scenarios.
Case Studies in Healthcare
1. Diagnosis and Treatment Recommendations: AI models used for diagnosing
diseases and recommending treatments have benefitted greatly from interpretability. For
instance, models that predict cancer from imaging data can provide visual explanations
for their predictions, which are crucial for medical professionals.
2. Personalized Medicine: AI systems that suggest personalized treatment plans based
on patient data are more trustworthy when they can explain their recommendations. This
allows healthcare professionals to understand the rationale behind a treatment plan
tailored to individual patients.
Financial Services Applications
1. Credit Scoring Models: AI models used in credit scoring can explain why a loan was
approved or denied, which is essential for both regulatory compliance and customer
service.
2. Fraud Detection Systems: In banking, explainable AI systems help in identifying and
explaining fraudulent transactions, thereby enhancing the trust in these systems and
aiding in the investigation process.
Autonomous Systems and Robotics
1. Self-Driving Cars: In the field of autonomous vehicles, explainability is crucial for
understanding the decisions made by the vehicle in critical situations, which is vital for
safety and regulatory approval.

11. 2. Industrial Robotics: In manufacturing, robots equipped with AI that can explain their
actions allow for better human-robot collaboration and troubleshooting.
Retail and Customer Service
1. Personalized Recommendations: E-commerce platforms use AI for personalized
product recommendations. Explainable AI helps in understanding why certain products
are recommended, enhancing customer trust and improving the recommendation
algorithms.
2. Customer Support Chatbots: AI-driven chatbots are more effective when they can
explain their advice or actions, leading to improved customer satisfaction and efficiency.
Ethical AI and Governance
1. Bias Detection: Case studies in detecting and mitigating biases in AI systems highlight
the role of explainable AI in ensuring fairness and ethical AI practices.
2. AI Governance: Organizations implementing AI governance frameworks use
explainability to ensure compliance, transparency, and accountability in their AI
initiatives.
Section 7: Challenges and Limitations in AI
Interpretability and Explainability
Navigating the Complexities
This section addresses the challenges and limitations associated with achieving interpretability
and explainability in AI, particularly in deep learning. It discusses the obstacles AI practitioners
face and the potential trade-offs involved in making complex models more transparent and
understandable.
Balance Between Performance and Interpretability
1. Complexity vs. Clarity: One of the biggest challenges is the inherent trade-off between
model complexity (which often correlates with performance) and interpretability. Simpler
models are generally more interpretable, but they may not perform as well as complex
models like deep neural networks.
2. Loss of Accuracy: In some cases, efforts to increase interpretability can lead to a
reduction in accuracy or predictive power, which can be a significant setback, especially
in applications where performance is critical.
Technical and Practical Challenges
1. Computational Costs: Implementing interpretability and explainability methods can be
computationally expensive, especially for large-scale models and datasets.

12. 2. Lack of Standardization: There is no one-size-fits-all approach to interpretability and
explainability, making it challenging to standardize these processes across different
models and applications.
Ethical and Societal Implications
1. Bias and Fairness: While interpretability can help in detecting biases, it does not
automatically ensure fairness. Misinterpretations or oversimplifications of complex
models can lead to misguided conclusions.
2. Privacy Concerns: In some instances, explaining AI decisions might require revealing
sensitive or personal information used in the decision-making process, raising privacy
concerns.
Theoretical Limitations
1. Incomplete Understanding of Deep Learning: The theoretical foundations of deep
neural networks are still not fully understood. This lack of complete understanding poses
a significant barrier to developing comprehensive interpretability methods.
2. Ambiguity in Interpretations: Interpretations are often subjective and can vary
depending on the person analyzing the model. This ambiguity can make it challenging to
derive definitive conclusions.
Section 8: Future Directions and Research Trends in AI
Interpretability and Explainability
Exploring the Horizon
This section discusses the prospective advancements and emerging research trends in the field
of AI interpretability and explainability. It highlights the potential future developments and how
they might shape the landscape of AI.
Advancements in Interpretability Methods
1. Integration with Advanced AI Models: Continued efforts are expected in integrating
interpretability techniques with more advanced AI models, including newer variants of
neural networks.
2. Automated Interpretability: Research into automating the interpretability process is
likely to gain traction, making it easier and more efficient to apply these techniques in
different scenarios.
Explainability in Complex Systems
1. Explainability in Reinforcement Learning: As reinforcement learning systems become
more prevalent, especially in complex environments, there will be an increased focus on
making these systems interpretable and explainable.

13. 2. Contextual and Situational Explainability: Developing methods that provide
explanations tailored to the specific context or situation, making them more relevant and
easier to understand for end-users.
Ethical and Regulatory Developments
1. Standardization of Interpretability: Efforts towards standardizing what constitutes
‘good’ interpretability in AI systems, potentially leading to industry-wide benchmarks or
guidelines.
2. Regulation-Driven Research: With stricter AI regulations anticipated, research is likely
to align more closely with regulatory requirements, focusing on transparency, fairness,
and accountability.
Human-Centric AI
1. Human-in-the-loop Interpretability: Emphasizing the role of humans in interpreting AI,
including research on how to effectively communicate AI decisions to different
stakeholders.
2. User-Centric Design of Explainability: Tailoring explainability tools and interfaces to
suit the needs and understanding of specific user groups, such as domain experts,
laypersons, or regulatory bodies.
Interdisciplinary Approaches
1. Collaborations Across Fields: Anticipated collaborations between AI researchers,
ethicists, psychologists, and domain experts to develop more holistic interpretability
solutions.
2. Leveraging Psychological Insights: Incorporating findings from cognitive psychology
to design interpretability tools that align with human cognitive processes and biases.
Technological Innovation
1. AI for Interpreting AI: Utilizing AI techniques themselves to aid in interpreting and
explaining complex AI models.
2. Visualization Technologies: Advancements in visualization tools and technologies to
provide more intuitive and insightful representations of AI decision processes.
Final Takeaways
● Interdisciplinary Effort: Achieving meaningful interpretability in AI requires an
interdisciplinary approach, combining technical prowess with ethical, legal, and
psychological insights.
● Dynamic Field: The field of AI interpretability and explainability is dynamic, with
continuous advancements and evolving methodologies. Keeping abreast of these
changes is crucial for practitioners and researchers.

14. ● Ethical Imperative: As AI systems become more integrated into critical aspects of
society, the ethical imperative for these systems to be transparent and understandable
becomes increasingly paramount.
● Collaboration and Standardization: Future progress in this field will likely hinge on
collaborative efforts across industries and the development of standardized approaches
and benchmarks for interpretability.
● Empowerment Through Understanding: Ultimately, the goal of AI interpretability and
explainability is to empower users, stakeholders, and society at large with a clear
understanding of how AI systems make decisions, ensuring these systems are used
responsibly and ethically.
References and Further Reading for AI Interpretability
and Explainability
1. “An empirical comparison of deep learning explainability approaches for EEG
using simulated ground truth” by Akshay Sujatha Ravindran and Jose
Contreras-Vidal. Published in Scientific Reports, this paper compares multiple model
explanation methods for EEG, identifying the most suitable methods and understanding
their limitations. DeepLift was found to be consistently accurate and robust. Link to the
article (Published: 18 October 2023)
2. “Breaking the Paradox of Explainable Deep Learning.” This paper proposes a
method that trains deep hypernetworks to generate explainable linear models. The
proposed method retains the accuracy of black-box deep networks while offering
inherent explainability. Link to the article
3. “Using model explanations to guide deep learning models towards consistent
explanations for EHR data”. This study focuses on enhancing explanation consistency
in deep learning models, particularly in the context of Electronic Health Records. A novel
deep learning ensemble architecture is proposed, significantly improving explanation
consistency. Link to the article (Published: 18 November 2022)
4. “Obtaining genetics insights from deep learning via explainable artificial
intelligence” by Novakovsky, G., Dexter, N., Libbrecht, M.W., et al. This paper explores
the use of explainable AI in the context of genetics and deep learning, highlighting the
significance of interpretability in this domain. Link to the article (Published: 03 October
2022)
5. “Explaining machine learning models with interactive natural language
conversations using TalkToModel”. This paper introduces TalkToModel, a dialogue
system that explains ML models through natural language conversations. It
demonstrates the effectiveness of this approach in making model explainability more
accessible and intuitive. Link to the article (Published: 27 July 2023)
Tags: Deep learning, Explainability, Neural network, Visualization