Explaining the AI black box problem

ZDNET
27 Apr 202007:01

TLDRTanya Hall interviews Sheldon Fernandez, CEO of Darwin AI, about the AI black box problem. Darwin AI specializes in making AI transparent by cracking open the black box. Fernandez explains that AI, particularly deep learning, is powerful but lacks transparency in its decision-making process. An example is an autonomous vehicle that turns left when the sky is a certain shade of purple, due to biased training data. Darwin AI uses AI to understand neural networks and validate explanations through a counterfactual approach. The importance of building foundational explainability for engineers and then translating it for consumers is highlighted.

Takeaways

  • 🧠 The AI black box problem refers to the lack of transparency in how AI models, particularly neural networks, arrive at their decisions.
  • 🐝 Darwin AI is known for developing technology to address the black box issue in AI, making AI decisions more understandable.
  • 📈 AI systems like neural networks learn from vast amounts of data, but the process of how they learn is not easily understood by humans.
  • 🦁 An example given is training a neural network to recognize lions by showing it millions of lion images, but it's unclear how the network internally processes this information.
  • 🚗 A real-world example involves an autonomous vehicle that turned left when the sky was a certain shade of purple, due to biased training data from the Nevada desert.
  • 🔍 To understand AI decisions, Darwin AI uses other AI techniques to interpret neural network behavior and provide explanations.
  • 🔑 The key to validating AI explanations is using a counterfactual approach, which tests the validity of the AI's reasoning by altering input data.
  • 📊 Darwin AI published research on a framework for testing AI explanations, showing their technique is superior to existing methods.
  • 🌐 For those implementing AI, it's crucial to first establish a strong technical understanding of AI explainability.
  • 🤝 Explainability is multi-level: it's important for developers to understand how AI works and for end-users to trust AI decisions, like in medical diagnoses.
  • 📧 To connect with Sheldon Fernandez, one can visit Darwin AI's website, find him on LinkedIn, or email him directly.

Q & A

  • What is the AI black box problem?

    -The AI black box problem refers to the lack of transparency in how artificial intelligence systems, specifically neural networks, make decisions. These systems can perform tasks effectively but do not provide insight into how they reach their conclusions.

  • Why is the black box problem significant in AI?

    -The black box problem is significant because it can lead to AI systems making decisions for the wrong reasons, which can have serious consequences, especially in critical applications like autonomous vehicles or medical diagnostics.

  • What is Darwin AI known for?

    -Darwin AI is known for developing technology that addresses the black box problem in AI by providing explanations for how AI systems make decisions.

  • How does Darwin AI's technology work?

    -Darwin AI uses other forms of artificial intelligence to understand and explain the complex workings of neural networks, and then surfaces those explanations for humans to understand.

  • What is the counterfactual approach mentioned in the script?

    -The counterfactual approach is a method used to validate the explanations generated by AI. It involves removing hypothetical reasons for a decision from the input and checking if the decision changes significantly, thereby confirming the validity of the explanation.

  • Why is it challenging to understand how neural networks make decisions?

    -Neural networks are complex, with many variables and layers, making it mathematically infeasible to go through each one to understand what influences a particular decision.

  • Can you provide an example of the black box problem from the script?

    -Yes, the script mentions an autonomous vehicle that turned left more frequently when the sky was a certain shade of purple. The AI had incorrectly correlated the color of the sky with the turning direction because it was trained in an environment where this correlation existed.

  • How does Darwin AI's research help in understanding AI decisions?

    -Darwin AI's research provides a framework for generating and validating explanations of AI decisions, which helps in opening up the black box and understanding why AI is doing what it's doing.

  • What are the different levels of explainability mentioned in the script?

    -The script mentions two levels of explainability: one for technical folks like engineers and data scientists, which helps in building robust AI systems, and another for consumers or end-users, which helps in understanding the AI's decisions.

  • How can someone get in touch with Sheldon Fernandez from Darwin AI?

    -Sheldon Fernandez can be contacted through Darwin AI's website, DarwinAI.com, or on LinkedIn, and also via email at [email protected].

  • What recommendations does Sheldon Fernandez have for explaining AI decisions?

    -Sheldon Fernandez recommends starting with technical understanding to ensure the AI system is robust and can handle edge cases. Once that foundation is built, explanations can be provided to non-technical end-users.

Outlines

00:00

🧠 Understanding the Black Box Problem in AI

Tanya Hall interviews Sheldon Fernandez, CEO of Darwin AI, to discuss the black box problem in artificial intelligence. Darwin AI is known for addressing this issue, which refers to the lack of transparency in AI decision-making processes. AI, particularly deep learning models, are trained on vast amounts of data but the internal workings that lead to their conclusions are not well understood. This can lead to AI systems making decisions for the wrong reasons, as illustrated by an example where an AI incorrectly associated a copyright symbol with images of horses. The conversation highlights the importance of cracking the black box to ensure that AI systems are making decisions based on real-world understanding rather than coincidental correlations in the training data.

05:02

🔍 Cracking the AI Black Box with Counterfactuals

Sheldon Fernandez explains how Darwin AI uses other forms of AI to understand and explain the complex workings of neural networks. The process involves a counterfactual approach where hypothetical reasons for AI decisions are tested by removing them from the input data to see if the decision changes significantly. This method was proven to be superior to existing techniques, as reported in Darwin AI's research published in December of the previous year. The discussion then shifts to the practical application of explainability in AI, emphasizing the need for different levels of explanation depending on the audience. For developers, understanding the AI's decision-making process is crucial for building robust systems, while for end-users, a simpler explanation that justifies the AI's output is necessary.

Mindmap

Keywords

💡AI black box problem

The AI black box problem refers to the lack of transparency in how artificial intelligence systems, particularly neural networks, make decisions. These systems can be highly effective at tasks such as recognizing objects or driving cars, but the internal workings that lead to these decisions are often opaque, even to their creators. In the video, this problem is exemplified by an AI system that incorrectly associates a specific color of the sky with a turning direction in an autonomous vehicle, due to biased training data.

💡Darwin AI

Darwin AI is the company mentioned in the video that is focused on solving the black box problem in AI. They aim to make AI systems more understandable and trustworthy by developing technologies that can interpret how these systems arrive at their decisions. The company's efforts are crucial for industries relying on AI for critical tasks where understanding the decision-making process is essential.

💡Neural networks

Neural networks are a subset of machine learning and are inspired by the human brain. They consist of interconnected nodes or 'neurons' that process information. The video explains how these networks are trained by analyzing vast amounts of data, such as millions of images, to recognize patterns. However, the internal mechanisms that allow them to make decisions based on this data are not well understood, contributing to the black box issue.

💡Deep learning

Deep learning is a type of machine learning that involves training neural networks with many layers, or 'deep' layers, to perform tasks like image and speech recognition. The video script discusses how deep learning models can become very good at specific tasks after being trained on large datasets, but the process by which they learn and make decisions remains a mystery, which is part of the black box problem.

💡Counterfactual approach

The counterfactual approach is a method mentioned in the video for testing the validity of explanations generated by AI systems. It involves altering the input data to see if the AI's decision changes significantly. If the decision changes when certain factors are removed, it suggests that those factors were indeed influencing the AI's decision. This approach helps in understanding and validating the 'why' behind AI decisions.

💡Explainability

Explainability in AI refers to the ability to understand the reasoning behind an AI system's decisions. The video emphasizes the importance of explainability for both developers, who need confidence in their AI models, and end-users, who need to trust the AI's outputs. Explainability is key to addressing the black box problem and ensuring that AI decisions are transparent and reliable.

💡Autonomous vehicles

Autonomous vehicles are self-driving cars that use AI to navigate and make driving decisions. The video provides an example of an autonomous vehicle that turned left based on an incorrect correlation with the color of the sky. This example illustrates how the black box problem can lead to nonsensical decisions in AI systems, with potentially serious consequences in real-world applications like driving.

💡Bias in data

Bias in data refers to systematic errors or prejudices in a dataset that can lead AI systems to make incorrect or unfair decisions. In the video, it is mentioned that the AI system was trained in the Nevada desert, where a certain shade of purple in the sky was coincidentally associated with a specific turning scenario. This unintended bias in the training data led to the AI making incorrect correlations.

💡Non-sensible correlation

A non-sensible correlation is a spurious relationship that an AI system might identify between unrelated factors due to biases or imperfections in the training data. The video uses the example of an AI incorrectly associating the color of the sky with turning directions in an autonomous vehicle. Such correlations do not reflect real-world logic and can lead to flawed AI decision-making.

💡Research findings

The video mentions research findings published by Darwin AI that focus on how enterprises can trust AI-generated explanations. This research is significant as it aims to provide frameworks and methodologies for validating AI decisions, which is crucial for building trust in AI systems, especially in critical applications where reliability is paramount.

💡Technical understanding

Technical understanding, as discussed in the video, is the foundational knowledge that engineers and data scientists need to have confidence in the AI systems they develop. It involves a deep comprehension of how AI models work and the factors that influence their decisions. This understanding is essential for creating robust AI systems and for explaining their decisions to non-technical stakeholders.

Highlights

Darwin AI is known for cracking the black box problem in AI.

AI is used extensively but operates as a 'black box', where we don't know how decisions are made.

Neural networks learn from thousands of data examples but lack transparency in their internal workings.

Darwin AI's technology aims to provide insight into how AI reaches its conclusions.

An example of the black box problem is a neural network trained to recognize horses but actually recognizes copyright symbols.

The black box problem can lead to AI providing correct answers for the wrong reasons.

A real-world example is an autonomous vehicle that turns left when the sky is a certain color due to training bias.

Darwin AI's technology helps understand why AI is making certain decisions.

Understanding neural networks requires using other forms of AI due to their complexity.

Darwin AI's IP uses AI to understand neural networks and surface explanations.

A framework for validating AI explanations involves using a counterfactual approach to test hypotheses.

Darwin AI's research shows their technique is superior to state-of-the-art methods in explaining AI decisions.

There are different levels of explainability needed for developers and end-users.

Building foundational explainability gives engineers confidence in AI system robustness.

Darwin AI is focused on creating technical understanding before explaining to non-experts.

Sheldon Fernandez, CEO of Darwin AI, invites connections through their website, LinkedIn, and email.

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