The Black Box Emergency | Javier Viaña | TEDxBoston

TEDx Talks
22 May 202304:49

TLDRJavier Viaña addresses the global emergency of black box AI, emphasizing the need for explainable AI to ensure transparency and trust. He highlights the risks of relying on AI without understanding its decision-making process, using examples from healthcare and business. Viaña introduces eXplainable AI, advocating for algorithms that provide human-understandable reasoning. He discusses challenges to adopting explainable AI, such as size, unawareness, and complexity, and proposes bottom-up and top-down approaches to develop new algorithms and modify existing ones. Viaña concludes with a call to action for consumers to demand explainable AI to prevent AI from indirectly controlling humanity.

Takeaways

  • 🚨 We are facing a global emergency due to the excessive use of black box artificial intelligence.
  • 🤖 Most AI today is based on deep neural networks which are high performing but complex to understand.
  • 🧠 The lack of transparency in AI algorithms makes it difficult to know what is happening inside a trained neural network.
  • 🏥 Examples like hospitals using AI for critical decisions highlight the risks of not understanding AI's decision-making process.
  • 💼 CEOs making decisions based on AI recommendations without understanding the logic can lead to the machine, not the human, making the decision.
  • 🔍 The solution proposed is eXplainable Artificial Intelligence (XAI), which advocates for transparent algorithms understandable by humans.
  • 🌐 The current AI lacks explainability, which is a significant drawback as it hinders trust, supervision, validation, and regulation.
  • 📈 Three main reasons people are not using explainable AI are size of existing AI pipelines, unawareness, and complexity of the mathematical problem.
  • 💡 The speaker encourages developers, companies, and researchers to start using explainable AI for better trust and control.
  • 📋 Regulation like GDPR requires companies to explain reasoning processes to end users, yet black box AI continues to be used.
  • 👨‍💻 Two approaches to adopt explainable AI are bottom-up (developing new algorithms) and top-down (modifying existing algorithms for transparency).
  • 📖 The speaker introduces ExplainNets, an architecture that uses fuzzy logic to generate natural language explanations of neural networks.

Q & A

  • What is the global emergency discussed by Javier Viaña in his TEDxBoston talk?

    -The global emergency discussed by Javier Viaña is the excessive use of black box artificial intelligence, which refers to AI systems based on deep neural networks that are high performing but extremely complex to understand.

  • Why is the complexity of deep neural networks a problem?

    -The complexity of deep neural networks is a problem because it makes them difficult to understand, which means we don't know what is going on inside a trained neural network, leading to a lack of transparency and accountability in their decision-making processes.

  • What is an example of a critical situation where the lack of AI explainability could be dangerous?

    -An example given is a hospital using a neural network to estimate the amount of oxygen needed for a patient in an intensive care unit. If the AI output is wrong, there is no way to understand the reasoning behind the algorithm's decision, which could lead to life-threatening consequences.

  • How does the use of black box AI affect decision-making in a company?

    -The use of black box AI in a company can lead to decisions being made based on AI recommendations without the CEO understanding the logic behind them. This raises questions about who is really making the decisions: the human or the machine.

  • What is eXplainable Artificial Intelligence (XAI) and why is it important?

    -eXplainable Artificial Intelligence (XAI) is a field of AI that advocates for transparent algorithms whose reasoning can be understood by humans. It is important because it allows for the supervision, validation, and regulation of AI systems, ensuring that humans maintain control over AI decisions.

  • What are the three main reasons why companies are not using explainable AI?

    -The three main reasons companies are not using explainable AI are: 1) Size - Large AI pipelines are deeply integrated into businesses, making changes time-consuming; 2) Unawareness - There is a lack of knowledge about alternatives to neural networks; 3) Complexity - Achieving explainability is a complex mathematical problem without a standard method.

  • How does the GDPR relate to the need for explainable AI?

    -The GDPR (General Data Protection Regulation) requires companies processing human data to explain their reasoning process to the end user. This regulation highlights the need for explainable AI to ensure transparency and compliance with such regulations.

  • What is the potential consequence of not adopting explainable AI?

    -If explainable AI is not adopted urgently, we may end up in a world where there is no supervision possible, leading to blindly following AI outputs, which could result in failures, loss of trust in humanity, and indirect control of humanity by AI.

  • What are the two approaches to adopting explainable AI mentioned by Javier Viaña?

    -The two approaches to adopting explainable AI are: a bottom-up approach, which involves developing new algorithms that replace neural networks, and a top-down approach, which involves modifying existing algorithms to improve their transparency.

  • What is an example of a top-down approach to improving AI transparency?

    -An example of a top-down approach is Javier Viaña's development of ExplainNets, which use fuzzy logic to generate natural language explanations of neural networks, helping to understand the reasoning process behind their decisions.

  • What is the role of consumers in the adoption of explainable AI?

    -Consumers play a crucial role in the adoption of explainable AI by demanding that the AI used with their data is explained to them, thereby pushing for transparency and accountability in AI systems.

Outlines

00:00

🚨 The Crisis of Black Box AI

The speaker, Jenny Tayar, addresses the global emergency of the excessive use of black box artificial intelligence. She explains that AI based on deep neural networks is highly complex and difficult to understand, which poses a significant challenge. She uses the example of a hospital using AI to estimate oxygen needs for patients in intensive care, emphasizing the lack of transparency in the AI's decision-making process. Tayar also discusses the implications for decision-making in businesses, where CEOs may unknowingly let AI dictate decisions without understanding the reasoning behind them. She highlights the importance of eXplainable Artificial Intelligence (XAI), which aims to create transparent algorithms that can be understood by humans.

Mindmap

Keywords

💡Black Box Artificial Intelligence

Black Box Artificial Intelligence refers to AI systems whose internal processes are not transparent or understandable to humans. These systems are often based on complex algorithms like deep neural networks, which can make decisions or predictions without providing clear reasoning. In the context of the video, the speaker highlights the risks of relying on such systems, especially in critical areas like healthcare, where a wrong decision could have serious consequences. The video emphasizes the need for transparency and understanding in AI decision-making processes.

💡Deep Neural Networks

Deep Neural Networks are a subset of artificial neural networks with multiple layers between the input and output layers. They are designed to model complex patterns and are used in various applications like image recognition and natural language processing. The video script mentions that these networks have thousands of parameters, making them high-performing but also complex and difficult to interpret, which contributes to the 'black box' problem.

💡Explainable Artificial Intelligence (XAI)

Explainable Artificial Intelligence is a field within AI that focuses on creating systems whose actions can be easily understood by humans. It stands in contrast to 'black box' AI, aiming to provide transparency in how decisions are made. In the video, XAI is presented as a solution to the problem of not understanding the logic behind AI decisions, advocating for algorithms that can explain their reasoning process in a way that is comprehensible to humans.

💡Oxygen Estimation

Oxygen Estimation is an example given in the video where a neural network is used to determine the amount of oxygen needed for a patient in an intensive care unit. The speaker uses this example to illustrate the potential danger of relying on a 'black box' AI system when the stakes are high. If the AI provides an incorrect estimation, there is no way to understand why it made that decision, which could lead to serious health risks for the patient.

💡Decision-making

Decision-making in the context of the video refers to the process by which AI systems or humans make choices based on data and analysis. The speaker raises concerns about the use of 'black box' AI in corporate decision-making, where CEOs might act on AI recommendations without fully understanding the rationale behind them. This lack of transparency can lead to a situation where it's unclear whether the human or the machine is truly in control.

💡Regulation

Regulation, as mentioned in the video, refers to the rules and laws that govern the use of certain technologies, in this case, AI. The General Data Protection Regulation (GDPR) is cited as an example where companies processing human data are required to explain their reasoning to the end user. The speaker argues that despite such regulations, there is still a widespread use of 'black box' AI, leading to fines but not necessarily to changes in practice.

💡Consumer Demand

Consumer Demand in this context refers to the power that consumers have to influence the practices of companies, including how they use AI. The speaker calls for consumers to demand transparency from companies regarding the AI systems used with their data. By doing so, consumers can push for the adoption of explainable AI and ensure that they understand how decisions affecting them are made.

💡Bottom-up Approach

The Bottom-up Approach is one of the methods discussed in the video for developing explainable AI. It involves starting from the ground up by developing new algorithms that are inherently transparent and replace the existing 'black box' neural networks. This approach can be resource-intensive and time-consuming, as it may require significant research and development efforts, such as completing a PhD, as hinted by the speaker.

💡Top-down Approach

The Top-down Approach is another method for improving AI transparency, which involves modifying existing algorithms to make them more understandable. Unlike the bottom-up approach, this method works with the current systems to enhance their explainability. The speaker shares their own work on a top-down architecture called ExplainNets, which uses fuzzy logic to generate natural language explanations of neural network decisions.

💡Fuzzy Logic

Fuzzy Logic is a mathematical logic that deals with approximate rather than exact values, which is particularly useful in systems that need to handle uncertainty or vagueness. In the video, the speaker mentions using fuzzy logic as a tool to study and explain the behavior of neural networks within their ExplainNets architecture. This allows the AI to provide natural language explanations that are more understandable to humans.

💡Human Comprehensible Linguistic Explanations

Human Comprehensible Linguistic Explanations refer to the clear and understandable descriptions of AI processes and decisions that can be easily interpreted by humans. The video emphasizes the importance of such explanations for achieving explainable AI. The speaker argues that providing these explanations is crucial for building trust in AI systems and ensuring that humans can effectively supervise and validate AI decisions.

Highlights

We are facing a global emergency due to the excessive use of black box artificial intelligence.

Most AI today is based on deep neural networks which are very complex to understand.

The lack of transparency in AI algorithms is the biggest challenge in AI today.

AI used in hospitals for critical decisions lacks explainability, which is alarming.

The decision-making process of black box AI is unclear, raising questions about who is really in control.

Explainable Artificial Intelligence (xAI) is a field that advocates for transparent algorithms.

xAI aims to provide reasoning that can be understood by humans, unlike black box models.

The adoption of xAI is crucial for trust, supervision, validation, and regulation of AI.

The GDPR requires companies to explain the reasoning process to the end user, yet black box AI continues to be used.

Consumers should demand that AI used with their data is explained to them.

Failure to adopt xAI could lead to a world where AI indirectly controls humanity.

There are two approaches to xAI: bottom-up development of new algorithms or top-down modification of existing ones.

The top-down approach involves modifying existing algorithms to improve their transparency.

ExplainNets is an example of a top-down approach that uses fuzzy logic to generate natural language explanations of neural networks.

Human-comprehensible linguistic explanations of neural networks are key to achieving xAI.

The speaker calls for developers, companies, and researchers to start using explainable AI.

The speaker envisions a future where explainable AI prevents the mystification of AI and maintains human control.

Casual Browsing

Explaining the AI black box problem

2024-09-24 01:53:00

AI Algorithms and the Black Box

2024-09-24 04:14:00

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