Explaining the AI black box problem

ZDNET
27 Apr 202007:01

TLDRIn this discussion, Tanya Hall speaks with Sheldon Fernandez, CEO of Darwin AI, about the company's efforts to address the AI black box problem. Fernandez explains that while AI is powerful, its decision-making processes are often opaque. He uses the example of an autonomous vehicle that turned based on the color of the sky due to a correlation in its training data. Darwin AI's technology helps to uncover these non-sensible correlations, providing insights into AI's reasoning. The company also published research on a counterfactual approach to validate AI explanations, which is crucial for building trust in AI systems.

Takeaways

  • 🧠 The AI black box problem refers to the lack of transparency in how neural networks reach their conclusions, making it difficult to understand their decision-making processes.
  • 🤖 Darwin AI is known for addressing the black box issue in AI, providing explanations for the actions of artificial intelligence systems.
  • 📈 Deep learning, a subset of machine learning, relies on neural networks that learn from vast amounts of data, but the internal workings of these networks are often opaque.
  • 🦁 An example of the black box problem is a neural network trained to recognize lions that instead learned to recognize the copyright symbol, highlighting the potential for incorrect conclusions based on data biases.
  • 🚗 Autonomous vehicles can exhibit unintended behaviors due to AI making decisions based on spurious correlations found in training data, such as turning left when the sky is a certain shade of purple.
  • 🔍 Darwin AI uses other forms of AI to understand and explain how neural networks arrive at decisions, offering a way to 'crack open' the black box.
  • 📊 The company's research introduces a counterfactual approach to validate explanations, by testing if removing hypothesized influencing factors changes the AI's decision.
  • 🔑 Darwin AI's technology helps enterprises trust AI-generated explanations by providing a framework to ensure the validity of these explanations.
  • 🛠️ There are different levels of explainability needed: one for technical professionals to ensure robust AI systems, and another for end-users to understand AI decisions affecting them.
  • 🌐 For those interested in Darwin AI's solutions or seeking to understand AI better, the company's website, LinkedIn, and email provide avenues for connection and further information.
  • 🎓 The script emphasizes the importance of foundational technical understanding in building trust in AI systems before explaining decisions to non-technical stakeholders.

Q & A

  • What is the AI black box problem?

    -The AI black box problem refers to the lack of transparency in how artificial neural networks, particularly those used in deep learning, reach their conclusions. These networks can be highly effective but we often don't understand the internal mechanisms that lead to their decisions, which can lead to incorrect or unexpected outcomes.

  • What is Darwin AI known for?

    -Darwin AI is known for addressing the black box problem in artificial intelligence. They have developed technology that aims to provide insight into how AI systems make decisions, thus making them more transparent and understandable.

  • How does a neural network learn to recognize objects like a lion?

    -A neural network learns to recognize objects by being shown thousands or even millions of examples of that object. For instance, to recognize a lion, it would be trained on a large dataset of images containing lions, gradually improving its ability to identify lions in new images.

  • Why is it a problem if an AI system gets the right answer for the wrong reasons?

    -If an AI system arrives at the correct answer due to incorrect or superficial correlations in the training data, it can lead to unreliable and potentially harmful outcomes. The system may not generalize well to new situations and could make decisions based on flawed logic.

  • Can you provide an example of the black box problem in real-world scenarios?

    -An example given in the script involves an autonomous vehicle that began turning left more frequently when the sky was a certain shade of purple. It turned out the AI had associated this color with a specific training scenario in the Nevada desert, leading to an incorrect and potentially dangerous correlation.

  • How does Darwin AI's technology work to understand neural networks?

    -Darwin AI uses other forms of artificial intelligence to analyze and interpret the complex workings of neural networks. Their technology surfaces explanations for how decisions are made within these networks.

  • What is the counterfactual approach mentioned in the script?

    -The counterfactual approach is a method of testing the validity of an explanation for an AI decision. By removing the hypothesized influencing factors from the input and observing if the decision changes, one can gain confidence in the accuracy of the explanation.

  • What does Darwin AI's research framework propose for validating AI explanations?

    -Darwin AI's research framework suggests using a counterfactual approach to validate explanations. If the AI's decision changes significantly when the hypothesized reasons are removed, it suggests that the explanation is likely valid.

  • How can understanding the AI black box problem benefit AI developers and engineers?

    -Understanding the AI black box problem allows developers and engineers to create more robust AI systems. It gives them the confidence that their models are making decisions based on valid and reliable logic, which is crucial for handling edge cases and ensuring overall system performance.

  • What recommendations does Sheldon Fernandez have for those contemplating an AI solution?

    -Sheldon Fernandez recommends starting with a technical understanding of explainability. This involves ensuring that the AI system's decisions can be explained and understood by developers and engineers, which in turn can be communicated to end-users or consumers in a way that builds trust.

  • How can someone interested in Darwin AI's work get in touch with Sheldon Fernandez?

    -Those interested can connect with Sheldon Fernandez through Darwin AI's website, darwinaI.com, by finding him on LinkedIn, or by emailing him at [email protected].

Outlines

00:00

🔍 Cracking the AI Black Box Problem

In this video segment, Tanya Hall interviews Sheldon Fernandez, CEO of Darwin AI, about the company's mission to solve the 'black box' issue in artificial intelligence. The black box problem refers to the lack of transparency in AI decision-making processes, where neural networks can perform tasks effectively but without clear understanding of how they arrived at their conclusions. Darwin AI's technology aims to provide explanations for AI actions, enhancing trust and reliability in AI systems used in business and industry. The conversation delves into the challenges of understanding neural networks, which learn from vast amounts of data but can sometimes make incorrect inferences due to unrecognized biases or correlations in the training data. An example of an autonomous vehicle incorrectly associating the color of the sky with turning directions highlights the real-world implications of this issue.

05:02

🛠️ Methodology for Understanding Neural Networks

This paragraph discusses the approach taken by Darwin AI to demystify the inner workings of neural networks. Sheldon Fernandez explains that due to the complexity of these networks, it is impractical to manually examine each variable and layer to understand decision-making processes. Instead, Darwin AI employs other forms of AI to analyze and interpret how neural networks function. The company's intellectual property, developed several years prior, surfaces explanations that can be validated through a counterfactual approach. This method involves removing hypothesized influencing factors from inputs to see if the AI's decision changes significantly, thereby confirming the validity of the explanation. Darwin AI's research, published in December of the previous year, introduced a framework for this validation process, which was shown to be superior to existing methods. The segment concludes with advice for those considering AI solutions, emphasizing the importance of building a foundation of technical understanding and robustness in AI models before explaining their decisions to end-users.

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. It's a significant issue because while AI can perform tasks with high accuracy, the reasoning behind its decisions is often unclear. In the video, this concept is central as it discusses the challenges of understanding AI's internal processes, which is essential for building trust in AI systems.

💡Darwin AI

Darwin AI is the company mentioned in the script, known for addressing the black box problem in AI. It has developed technology to make AI's decision-making process more understandable. The company's work is highlighted as an example of efforts to 'crack' the black box and provide explanations for AI behavior, which is a key theme in the video.

💡Neural networks

Neural networks are a subset of machine learning and are the focus of the black box problem discussed in the video. They are powerful in recognizing patterns and making decisions based on large amounts of data. However, the internal workings of these networks are complex and not easily interpretable, leading to the black box issue where the rationale behind their decisions is opaque.

💡Deep learning

Deep learning is a subset of machine learning that involves training neural networks with multiple layers to learn and make decisions. It is mentioned in the script as the technology behind the black box problem. Deep learning models are adept at tasks like image recognition but can struggle with explainability due to their depth and complexity.

💡Insight

Insight, in the context of the video, refers to the understanding of how an AI system reaches a particular conclusion. The lack of insight is a problem because it means that while AI can perform tasks effectively, we cannot see the steps it took to get there, which is a core aspect of the black box dilemma.

💡Counterfactual approach

The counterfactual approach is a method mentioned in the script for validating the explanations generated by AI. It involves altering the input data to see if the AI's decision changes significantly, thus providing confidence in the explanation. This approach is crucial for Darwin AI's research and is a practical solution to the black box problem.

💡Autonomous vehicles

Autonomous vehicles are used in the script as a practical example of where the black box problem can manifest. The script describes an incident where an autonomous car turned left based on an irrelevant factor—the color of the sky—due to a non-sensible correlation learned during training. This example illustrates the potential dangers of not understanding AI decision-making processes.

💡Non-sensible correlation

A non-sensible correlation is a term used in the script to describe a relationship that the AI incorrectly infers from the data, leading to decisions that do not make sense in the real world. The example of the autonomous vehicle turning based on sky color is a clear instance of a non-sensible correlation, which underscores the need for explainable AI.

💡Explainability

Explainability in AI refers to the ability to understand and interpret the decision-making process of an AI system. The script emphasizes the importance of building explainability into AI systems to ensure they are robust and to provide confidence to both developers and end-users. Explainability is key to overcoming the black box problem.

💡Technical understanding

Technical understanding is highlighted in the script as a foundational aspect of building explainable AI. It is the first level of explainability that needs to be established, allowing engineers and data scientists to have confidence in the robustness of their AI models. Only after this can explanations be effectively communicated to non-technical stakeholders.

💡Consumer

In the context of the video, a consumer refers to the end-user of an AI system, such as a radiologist who needs to understand why an AI classified an image as indicative of cancer. The script discusses the importance of providing explanations to consumers as a way to build trust and ensure the responsible use of AI.

Highlights

Tanya Hall and Sheldon Fernandez discuss the AI black box problem and how Darwin AI aims to solve it.

Darwin AI is known for addressing the lack of transparency in AI decision-making processes.

Artificial intelligence operates as a 'black box' due to the complexity of neural networks.

Neural networks can perform tasks effectively but the reasoning behind their decisions remains unclear.

An example of a neural network incorrectly identifying horses based on copyright symbols.

The black box problem leads to AI making decisions for the wrong reasons.

A real-world scenario where an autonomous vehicle made turns based on the color of the sky.

Darwin AI's technology helped uncover the non-sensible correlation causing the vehicle's behavior.

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

Darwin AI's IP uses AI to interpret and explain the decisions made by neural networks.

A framework for validating AI explanations through counterfactual approaches was introduced.

Darwin AI's research showed their technique outperformed state-of-the-art methods.

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

Building foundational explainability for technical professionals is crucial for robust AI systems.

Sheldon Fernandez emphasizes the importance of technical understanding before explaining to consumers.

Darwin AI provides a pathway for developers to connect and learn more about their solutions.

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