Explaining the AI black box problem

ZDNET
27 Apr 202007:01

TLDRIn this discussion, Tanya Hall interviews Sheldon Fernandez, CEO of Darwin AI, about the company's efforts to solve the AI black box problem. Fernandez explains that while neural networks are powerful, their decision-making processes are often opaque, leading to trust issues in AI applications like autonomous vehicles. Darwin AI's technology aims to make these processes transparent, using AI to understand and explain the reasoning behind AI decisions, ensuring they are based on real-world logic rather than coincidental correlations.

Takeaways

  • 🧠 The AI black box problem refers to the lack of transparency in how neural networks reach their conclusions, which can lead to unexpected and potentially incorrect outcomes.
  • 🚀 Darwin AI is known for addressing the black box issue in AI by developing technology that provides insights into the decision-making processes of neural networks.
  • 📚 Deep learning, a subset of machine learning, uses large datasets to train neural networks, but the internal mechanisms are often not understood, leading to the black box problem.
  • 🦁 An example given is a neural network trained to recognize horses, which instead learned to recognize the copyright symbol common in horse images, illustrating the issue of incorrect learning.
  • 🤖 The black box problem can manifest in real-world applications, such as autonomous vehicles making decisions based on incorrect correlations learned during training.
  • 🔍 Darwin AI uses other forms of AI to analyze and understand the complex neural networks, helping to demystify the black box.
  • 📈 They have developed a framework using a counterfactual approach to validate the explanations generated by AI about its decision-making process.
  • 🛠️ The industry is working on building foundational explainability to ensure that engineers and data scientists have confidence in the robustness of their AI models.
  • 👨‍🏫 Explainability to the consumer is the next level, where users of AI, like a radiologist, can understand the reasoning behind AI's decisions, such as cancer classification.
  • 🔗 Darwin AI recently published research findings on how enterprises can trust AI-generated explanations and is releasing further details to educate the public.
  • 💼 For those interested in connecting with Sheldon Fernandez, CEO of Darwin AI, they can reach out through the company's website, LinkedIn, or via email.

Q & A

  • What is the 'black box' problem in artificial intelligence?

    -The 'black box' problem in AI refers to the lack of transparency in how neural networks make decisions. Despite their ability to perform tasks effectively, we often don't understand the internal mechanisms that lead to their conclusions, which can lead to unexpected and potentially incorrect outcomes.

  • How does Darwin AI address the black box problem?

    -Darwin AI has developed technology that aims to make neural networks more transparent by providing explanations for their decision-making processes. This is achieved through the use of other forms of artificial intelligence to analyze and interpret the complex workings of neural networks.

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

    -Solving the black box problem is crucial for building trust in AI systems. It allows developers and users to understand why AI makes certain decisions, which is essential for ensuring the reliability and safety of AI applications, especially in critical domains like healthcare and autonomous vehicles.

  • Can you provide an example of how the black box problem manifested in a real-world scenario?

    -One example mentioned in the script is an autonomous vehicle that turned left more frequently when the sky was a certain shade of purple. It turned out that 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's technology uses other forms of AI to analyze neural networks. It identifies potential influencing factors and then employs a counterfactual approach to test these hypotheses by removing them from the input and observing if the decision changes significantly.

  • What is the counterfactual approach in the context of explaining AI decisions?

    -The counterfactual approach involves hypothesizing reasons for an AI's decision and then altering the input data to remove these factors. If the decision changes significantly, it suggests that the removed factors were indeed influencing the AI's decision, thus providing a level of validation for the explanation.

  • How does Darwin AI ensure the validity of the explanations generated by its technology?

    -Darwin AI uses a framework that includes the counterfactual approach to test the validity of explanations. By systematically altering inputs and observing changes in decisions, they can confirm whether the hypothesized factors are indeed the cause of the AI's decision-making.

  • What are the different levels of explainability in AI systems?

    -There are different levels of explainability: one for the technical audience, such as engineers and data scientists, which provides a deep understanding of the AI's decision-making process, and another for the end-user or consumer, which explains the AI's decisions in a more accessible and understandable manner.

  • Why is it important for engineers to have a technical understanding of AI explainability?

    -For engineers, having a technical understanding of AI explainability helps them build more robust AI systems that can handle edge cases and unexpected scenarios. It also aids in identifying and mitigating potential biases in the AI's decision-making process.

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

    -Those interested in Darwin AI's work can connect with Sheldon Fernandez through the company's website at darwina.ai, by finding him on LinkedIn, or by emailing him at [email protected].

Outlines

00:00

🔍 Cracking the AI Black Box Problem

In this 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 how AI, particularly deep learning models, reach their decisions. Despite their effectiveness, these models can sometimes provide correct answers for the wrong reasons, which can be problematic. Darwin AI has developed technology to demystify these processes, and they recently published research on how enterprises can trust AI-generated explanations. The conversation begins with an explanation of the black box problem and its manifestations in real-world scenarios, such as the peculiar behavior of an autonomous vehicle influenced by the color of the sky, highlighting the importance of understanding AI decision-making for safety and reliability.

05:02

🤖 Understanding Neural Networks with AI

This paragraph delves into the complexities of neural networks and the challenges of deciphering their decision-making processes. It acknowledges the irony of using AI to understand other AI systems, given the intricate layers and variables akin to the human brain. The segment discusses Darwin AI's intellectual property developed by Canadian academics, which employs counterfactual approaches to validate the explanations generated by AI. This involves testing hypotheses by removing suspected influencing factors and observing changes in AI decisions. The company's research, published in December of the previous year, proposed a framework for this validation process, demonstrating the technique's superiority over existing methods. The conversation concludes with recommendations for those contemplating AI solutions or enhancing existing ones, emphasizing the importance of building a foundational understanding of AI explainability among technical professionals before extending it 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 make decisions. It is a significant issue because while AI can perform complex tasks, we often do not understand the internal mechanisms that lead to its outputs. In the video, this problem is highlighted as a major challenge in the field of AI, where neural networks can perform tasks such as recognizing objects or driving cars but without clear insight into how they reach those conclusions.

💡Darwin AI

Darwin AI is the company mentioned in the script, known for addressing the black box problem in AI. The company's technology aims to make the decision-making processes of AI systems more understandable and transparent. In the context of the video, Darwin AI's work is central to the discussion on how to 'crack' the black box and provide explanations for AI behavior.

💡Neural networks

Neural networks are a subset of machine learning and a key component of AI. They are designed to mimic the human brain's structure and function, learning from large datasets to perform tasks such as image recognition. The script explains that while neural networks are powerful, their internal workings are often opaque, leading to the black box problem where we do not understand how they arrive at specific decisions.

💡Deep learning

Deep learning is a subset of machine learning that involves training neural networks with many layers, allowing them to learn complex patterns in data. The script uses deep learning as an example of a powerful AI technique that is also prone to the black box problem due to its complexity and the difficulty in tracing how it processes information.

💡Insight

Insight, in the context of the video, refers to the understanding of the internal processes and decision-making criteria used by AI systems. The lack of insight is a problem because it means that even when AI systems provide correct answers, we may not know if they are based on valid reasoning or coincidental correlations, as illustrated by the example of the neural network recognizing horses.

💡Counterfactual approach

The counterfactual approach is a method proposed by Darwin AI to validate the explanations generated for AI decisions. It involves altering the input data to see if the AI's decision changes significantly, which can help confirm whether the hypothesized reasons for a decision are accurate. The script discusses this approach as a way to gain confidence in the explanations provided by AI systems.

💡Non-sensible correlation

A non-sensible correlation is a spurious connection that an AI system might make based on the data it has been trained on, rather than on logical or real-world relationships. In the video, an example is given of an autonomous vehicle that turns left when the sky is a certain shade of purple, which is an unintended correlation picked up during its training in the Nevada desert.

💡Explainability

Explainability in AI refers to the ability to provide clear and understandable explanations for how an AI system arrives at its decisions or outputs. The script emphasizes the importance of building technical explainability for developers and engineers to ensure robust AI systems and then extending this to consumers or end-users to help them understand and trust AI decisions.

💡Autonomous vehicle

An autonomous vehicle, also known as a self-driving car, is a type of AI system that the script uses to illustrate the real-world implications of the black box problem. The example given demonstrates how a lack of explainability can lead to unexpected and potentially dangerous behavior if the AI's decision-making process is not well understood.

💡Technical robustness

Technical robustness refers to the strength and reliability of an AI system's underlying technology and algorithms. In the video, it is suggested that having a deep understanding of an AI system's decision-making process contributes to its robustness, helping to prevent errors and improve performance in edge case scenarios.

Highlights

Transforming the AI black box into a glass box with the help of Darwin AI.

Darwin AI is known for solving the black box problem in artificial intelligence.

AI is widely used but operates as a black box, performing tasks without clear understanding of its processes.

Neural networks learn from vast amounts of data but lack transparency in their decision-making.

The black box problem leads to AI making decisions for the wrong reasons, as illustrated by the horse and copyright symbol example.

Real-world implications of the black box problem are demonstrated by the autonomous vehicle's odd behavior influenced by the color of the sky.

Darwin AI's technology helped identify the non-sensible correlation that caused the autonomous vehicle issue.

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

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

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

Darwin AI's research framework was published, demonstrating the effectiveness of their technique.

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

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

Explainability to consumers involves translating technical insights into understandable reasons for AI decisions.

Recommendations for those contemplating AI solutions include focusing on technical understanding before explaining to others.

Sheldon Fernandez, CEO of Darwin AI, offers contact information for further engagement.

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