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Making AI Interpretable with Generative Adversarial Networks

Summary

This article discusses how AI has made many advances in technology, business, and science, and how some of the best-performing models are very hard to explain due to their complexity. To address this issue, the authors introduce a framework for expanding the interpretability of complex machine learning models. The framework uses a Generative Adversarial Network (GAN) to generate synthetic data that can be used to generate reason codes to explain model decisions. The authors discuss the process of collecting data, training the GAN, evaluating the synthetic data, and using the data to generate reason codes. Finally, they discuss how this technique can be applied in other domains to help create a fair and sustainable environment for sellers and customers.

Q&As

What are some of the decisions influenced by AI and machine learning in our daily lives?
Some of the decisions influenced by AI and machine learning in our daily lives include music recommendations, eligibility for financial services, autonomous vehicles, medical diagnosis, and criminal court sentencing.

How do more complex algorithms provide better performance compared to simpler ones?
More complex algorithms provide better performance compared to simpler ones because they are able to capture more complex relationships between variables and make more accurate predictions.

What is the purpose of using Generative Adversarial Networks (GANs) to make AI interpretable?
The purpose of using Generative Adversarial Networks (GANs) to make AI interpretable is to generate “reason codes”, i.e. statements that describe the reason for a model’s decision.

How is the GAN framework structured?
The GAN framework is structured as follows: a generator model creates fake data from random noise, a discriminator is trained to determine whether the example was generated or real, and a feedback cycle allows the generator weights to be updated by the training of the discriminator.

What is the benefit of using GANs to generate reason codes for model decisions?
The benefit of using GANs to generate reason codes for model decisions is that it allows for the generation of realistic perturbations of input data, which can be used to generate model decisions that are clear and provide proactive recommendations.

AI Comments

👍 This article provides a great overview of how to use Generative Adversarial Networks to make AI interpretable and generate reason codes. It is well-written and easy to understand.

👎 This article does not provide enough detail on how to put the proposed framework into practice. Additionally, the examples provided are not comprehensive enough to fully illustrate the concept.

AI Discussion

Me: It's about making AI interpretable with Generative Adversarial Networks. It talks about how complex models can be hard to interpret, and how they can be made interpretable using GANs. It also discusses methods for evaluating synthetic data and generating reason codes for model decisions.

Friend: Interesting. What are the implications of this article?

Me: The implications are that it could help create a fairer and more transparent environment for customers and sellers to interact. It could also enable us to explain model predictions to partners and consumers, diagnose what went wrong in cases where we get false predictions, and keep consumers informed about the rationale behind automated decisions. This could be especially useful in applications of automation that result in adverse decisions for customers.

Action items

Technical terms

AI (Artificial Intelligence)
AI is a branch of computer science that focuses on creating intelligent machines that can think and act like humans.
Machine Learning
Machine learning is a type of artificial intelligence that uses algorithms to learn from data and make predictions.
Generative Adversarial Networks (GANs)
GANs are a type of machine learning algorithm that uses two neural networks, a generator and a discriminator, to generate realistic data from random noise.
Reason Codes
Reason codes are statements that describe the reason for a model’s decision.
Mode Collapse
Mode collapse is a phenomenon in which the generator learns to generate data within a small range of possible values.
Actor-Critic Framework
The Actor-Critic framework is a type of reinforcement learning algorithm that evaluates the Wassertstein distance between the real and synthetic data rather than evaluating binary cross-entropy.
K-Nearest Neighbors
K-nearest neighbors is a machine learning algorithm that finds the K most similar data points to a given data point.
Cosine Similarity
Cosine similarity is a measure of similarity between two vectors, which is calculated by taking the dot product of the two vectors and dividing by the product of their magnitudes.

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