Explaining the AI black box problem

ZDNET
27 Apr 202007:01

TLDRTanya Hall interviews Sheldon Fernandez, CEO of Darwin AI, about the company's efforts to solve the AI black box problem. Fernandez explains that while AI, particularly deep learning, is powerful, it lacks transparency in its decision-making process. He gives an example of an autonomous vehicle behaving strangely due to AI misinterpreting data. Darwin AI uses counterfactual approaches to validate AI explanations, helping to build trust in AI systems.

Takeaways

  • 🧠 The AI black box problem refers to the lack of transparency in how AI models, particularly neural networks, make decisions.
  • 🐝 Darwin AI is known for addressing the black box issue in AI, making neural networks more understandable.
  • 📈 AI learns from thousands of data examples, but the internal process of how it reaches conclusions is not clear.
  • 🦁 An example given is training a neural network to recognize lions, but it's unclear how the network understands what a lion is.
  • 🚗 A real-world example is an autonomous vehicle that turns left when the sky is a certain shade of purple, due to biased training data.
  • 🔍 To understand neural networks, Darwin AI uses other forms of AI to analyze and explain the complex layers and variables.
  • 🔑 The counterfactual approach is a method proposed by Darwin AI to validate the reasons behind AI decisions by altering inputs.
  • 🌟 Darwin AI's research shows their technique is superior to state-of-the-art methods in explaining AI decisions.
  • 🛠️ Explainability is crucial for engineers to build robust AI systems and handle edge cases.
  • 👨‍🔬 There are different levels of explainability needed, from technical to consumer-facing explanations.
  • 📧 Sheldon Fernandez, CEO of Darwin AI, can be contacted through their website, LinkedIn, or by email for more information.

Q & A

  • What is the AI black box problem?

    -The AI black box problem refers to the lack of transparency in how artificial intelligence, particularly neural networks, make decisions. These networks are trained on vast amounts of data, but the internal workings and reasoning behind their conclusions are not easily understood, making it difficult to trust their outputs.

  • How does Darwin AI address the black box problem?

    -Darwin AI has developed technology that aims to make AI decisions more transparent. They use advanced techniques to analyze how AI models reach their conclusions, allowing for a better understanding of the decision-making process within neural networks.

  • What is the significance of cracking the AI black box?

    -Crack the AI black box is significant because it allows businesses and industries to trust AI systems more. Understanding how AI systems arrive at their decisions can prevent incorrect outputs for the wrong reasons, improve the robustness of AI models, and address potential biases in the data.

  • Can you provide an example of the black box problem in action?

    -Yes, an example mentioned in the transcript is 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 direction to turn, which was not a sensible correlation based on real-world logic.

  • What is the counterfactual approach mentioned in the transcript?

    -The counterfactual approach is a method used to validate the explanations generated by AI. It involves removing the hypothesized reasons for a decision from the input and observing if the decision changes significantly. If it does, it suggests that the hypothesized reasons were indeed influencing the decision.

  • How does Darwin AI's technology help in understanding neural networks?

    -Darwin AI's technology uses other forms of artificial intelligence to analyze and understand the complex workings of neural networks. It then surfaces explanations for how these networks make decisions, helping to demystify the black box.

  • What does it mean for an AI system to have 'explanation ability'?

    -An AI system with 'explanation ability' can provide insights into how it reaches its conclusions. This is crucial for both developers, who need to ensure the AI is working correctly, and end-users, who need to trust the system's outputs.

  • Why is it important to explain AI decisions to consumers?

    -Explaining AI decisions to consumers is important for building trust and ensuring transparency. It allows users to understand the reasoning behind AI outputs, which is especially critical in fields like healthcare, finance, and autonomous driving where decisions can have significant impacts.

  • What recommendations does Sheldon Fernandez have for those implementing AI solutions?

    -Sheldon Fernandez recommends starting with a strong technical understanding of AI models to ensure robustness and handle edge cases. This foundational understanding can then be used to explain AI decisions to non-technical stakeholders.

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

    -Interested parties can connect with Sheldon Fernandez or Darwin AI through their website at darw.in.ai, by finding Sheldon on LinkedIn, or by emailing him directly at [email protected].

  • What was the research published by Darwin AI in December of last year?

    -The research published by Darwin AI in December proposed a framework for validating AI-generated explanations using a counterfactual approach. They demonstrated that their technique was superior to state-of-the-art methods in providing reliable explanations for AI decisions.

Outlines

00:00

🖥️ Understanding the AI Black Box Problem

In this segment, Tanya Hall interviews Sheldon Fernandez, CEO of Darwin AI, about the black box issue in artificial intelligence. The black box problem refers to the lack of transparency in AI systems, particularly in neural networks used in deep learning. These networks can perform tasks effectively but do not provide insight into how they arrive at their decisions. Fernandez explains that while AI can be highly effective, it can sometimes provide correct answers for the wrong reasons, as illustrated by an example where an AI incorrectly associated a copyright symbol with the presence of a horse in images. The discussion highlights the importance of understanding the inner workings of AI to ensure that it is making decisions based on real-world logic rather than coincidental correlations in the training data.

05:02

🔍 Addressing the Black Box with Counterfactual AI

Sheldon Fernandez continues the discussion by explaining how Darwin AI uses counterfactual approaches to understand and explain the decisions made by neural networks. This involves hypothesizing reasons for an AI's decision and then testing these by altering the input data to see if the decision changes significantly. If the decision changes when the hypothesized reasons are removed, it suggests that those reasons were indeed influencing the decision. Darwin AI has developed a framework for this approach and has published research showing its effectiveness compared to other methods. Fernandez emphasizes the importance of building a foundational level of explainability for technical professionals before attempting to explain AI decisions to end-users. He also provides contact information for those interested in learning more about Darwin AI's work.

Mindmap

Keywords

💡AI black box problem

The AI black box problem refers to the lack of transparency in the decision-making process of artificial intelligence systems, particularly neural networks. These systems can make accurate predictions or classifications but do not provide clear insight into how they arrived at those conclusions. In the video, this is exemplified by neural networks that are trained on large datasets to recognize objects, like lions, but the internal workings that lead to these decisions remain opaque.

💡Darwin AI

Darwin AI is a company mentioned in the script that is focused on addressing the AI black box problem. They aim to make AI systems more transparent and understandable to both developers and end-users. The company's technology is designed to provide explanations for AI decisions, which is crucial for building trust in AI applications.

💡Neural networks

Neural networks are a set of algorithms modeled loosely after the human brain that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling, or clustering raw input. In the context of the video, neural networks are used to illustrate the complexity that leads to the black box problem, as they can be highly effective without providing clear reasoning for their outputs.

💡Deep learning

Deep learning is a subset of machine learning that uses neural networks with many layers, or a 'deep' architecture, to model and understand data representations, as well as to make decisions based on the model. The video discusses how deep learning can be extremely powerful but also contributes to the black box problem due to the complexity of these deep neural networks.

💡Explainability

Explainability in AI refers to the ability to understand the reasoning behind the decisions made by AI systems. The video emphasizes the importance of explainability for gaining trust in AI and ensuring that AI decisions are reliable and fair. Darwin AI's research aims to provide methods for making AI-generated explanations trustworthy.

💡Counterfactual approach

The counterfactual approach mentioned in the video is a method used to test the validity of AI explanations. It involves altering the input data to see if the AI's decision changes significantly. If the decision changes when the suspected influential factors are removed, it suggests that those factors were indeed important in the AI's original decision-making process.

💡Autonomous vehicles

Autonomous vehicles, also known as self-driving cars, are used in the script as a practical example of the AI black box problem. The video describes a scenario where an autonomous vehicle turns left more frequently when the sky is a certain color, which is an unintended correlation learned by the AI. This example illustrates how AI can make decisions based on spurious correlations in the training data.

💡Data bias

Data bias refers to the presence of prejudice or a tendency towards a particular perspective in a dataset. In the context of AI, data bias can lead to unfair or nonsensical decisions, as the AI learns from the biased data. The video gives an example of how an AI might associate a specific sky color with a turning direction due to biased training data.

💡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 are developing. It is the first step towards building explainable AI, ensuring that the creators of AI understand its inner workings before it is explained to end-users.

💡Consumer explainability

Consumer explainability is the ability to convey the reasoning behind AI decisions to end-users in a way that is understandable and relatable. The video suggests that while technical understanding is crucial for developers, consumer explainability is necessary for users to trust AI systems, such as when explaining a medical diagnosis to a patient.

Highlights

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

Artificial Intelligence is a black box because we don't know how it reaches its conclusions.

Neural networks learn by looking at thousands of examples but we don't understand their internal workings.

The black box problem can lead to AI systems making decisions for the wrong reasons.

An example of the black box problem is a neural network recognizing horses based on copyright symbols.

The black box problem manifests in real-world scenarios like autonomous vehicles making decisions based on incorrect correlations.

Darwin AI's technology helped an autonomous vehicle client understand strange behavior caused by training data bias.

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 counterfactual approach is used to validate the explanations generated by AI.

Darwin AI published research on a framework for generating trustworthy AI explanations.

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

Building foundational explainability gives technical professionals confidence in AI systems.

Darwin AI's research shows their technique is better at explaining AI decisions compared to state-of-the-art methods.

For those with AI solutions, it's recommended to start with technical understanding before explaining to others.

Sheldon Fernandez, CEO of Darwin AI, is available for connection on LinkedIn and via email.

Casual Browsing

AI Algorithms and the Black Box

2024-09-24 04:14:00

The Black Box Emergency | Javier Viaña | TEDxBoston

2024-09-24 03:56:00

AI detectives are cracking open the black box of deep learning

2024-09-24 03:11:00

Microsoft AI CEO Mustafa Suleyman on 'Black Box'

2024-09-24 04:39:00

UNBOXING A DREAM OF A BOX - PART THE MISTVEIL (JAPANESE SET)

2024-06-12 15:40:01