The Black Box Emergency | Javier Viaña | TEDxBoston

TEDx Talks
22 May 202304:49

TLDRJavier Viaña addresses the global emergency of 'black box' AI, highlighting its complexity and lack of transparency. He emphasizes the importance of eXplainable AI (XAI) for trust, supervision, and regulation, especially in critical applications like healthcare. Viaña calls for action, urging developers and companies to adopt XAI to prevent AI from indirectly controlling humanity, and introduces 'ExplainNets,' a top-down approach using fuzzy logic to provide natural language explanations of neural networks.

Takeaways

  • 🚨 The excessive use of black box AI poses a global emergency due to its complexity and lack of transparency.
  • 🧠 Deep neural networks, while high performing, are difficult to understand, making it unclear what happens inside a trained neural network.
  • 🏥 In critical applications like healthcare, the lack of clarity in AI decisions can be dangerous if the AI output is incorrect.
  • 🤔 The reliance on black box AI in decision-making raises questions about who is truly making decisions: humans or machines.
  • 🔑 eXplainable AI (XAI) is a field advocating for transparent algorithms that can be understood by humans, contrasting with black box models.
  • 💡 Using explainable AI could provide reasoning behind AI decisions, which is crucial for applications like oxygen estimation in hospitals.
  • 📈 The adoption of explainable AI is hindered by the size of existing AI pipelines, unawareness of alternatives, and the complexity of making AI explainable.
  • 🌐 The GDPR requires companies to explain their reasoning process to users, but black box AI often fails to meet this requirement, leading to fines.
  • 📢 Consumers should demand transparency from AI used with their data, promoting the use of explainable AI.
  • 👨‍🏫 Javier Viaña calls for developers, companies, and researchers to adopt explainable AI to ensure trust, supervision, validation, and regulation.
  • 🛠️ Two approaches to achieving explainable AI are proposed: developing new algorithms or modifying existing ones to improve transparency.

Q & A

  • What is the main concern expressed by Javier Viaña in his TEDxBoston talk?

    -Javier Viaña expresses concern over the excessive use of black box artificial intelligence, which is complex and not easily understood, leading to potential issues in decision-making processes.

  • What is a 'black box' in the context of AI?

    -A 'black box' in AI refers to systems, particularly deep neural networks, that are highly performant but whose internal workings and decision-making processes are not transparent or understandable to humans.

  • Why is the lack of transparency in AI a problem according to Javier Viaña?

    -The lack of transparency in AI is a problem because it makes it difficult to understand the reasoning behind AI decisions, which can lead to incorrect outputs without the ability to trace the logic or correct the errors.

  • What is eXplainable Artificial Intelligence (XAI)?

    -eXplainable Artificial Intelligence (XAI) is a field of AI that focuses on creating algorithms that provide clear, understandable reasoning for their decisions, allowing humans to better supervise and validate AI outputs.

  • What are the three main reasons people are not using explainable AI according to the talk?

    -The three main reasons are the size of existing AI pipelines which would take years to change, unawareness of the alternatives to neural networks, and the complexity of obtaining explainability, as there is no standard method yet.

  • How does Javier Viaña suggest we can start trusting, supervising, validating, and regulating AI?

    -Javier Viaña suggests that we should start using explainable AI, as it is the only way to fully trust, supervise, validate, and regulate artificial intelligence.

  • What is the General Data Protection Regulation (GDPR) and how does it relate to AI?

    -The GDPR is a regulation that requires companies processing human data to explain their reasoning process to the end user. It relates to AI as it demands transparency in how AI systems make decisions with personal data.

  • What is Javier Viaña's call to action for consumers regarding AI?

    -Javier Viaña calls on consumers to demand that the AI used with their data provides explanations for its decisions, promoting the adoption of explainable AI.

  • What are the two approaches to adopting explainable AI mentioned in the talk?

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

  • Can you explain what ExplainNets are as mentioned in the talk?

    -ExplainNets, as introduced by Javier Viaña, are algorithms designed to generate natural language explanations of neural networks' decisions. They use mathematical tools like fuzzy logic to study, learn from, and explain the reasoning process of neural networks.

  • What does Javier Viaña envision if we do not adopt explainable AI urgently?

    -Javier Viaña envisions a world without supervision where humans blindly follow AI outputs, leading to failures, loss of trust in AI, and indirectly, AI controlling humanity rather than humans controlling AI.

Outlines

00:00

🚨 The Challenge of Black Box AI

The video script addresses the critical issue of 'black box' artificial intelligence, which refers to AI systems based on deep neural networks that are highly complex and difficult to understand. The speaker, Jenny Tayar, highlights the potential risks of relying on such systems, especially in sensitive areas like healthcare and corporate decision-making, where the lack of transparency could lead to serious consequences. She emphasizes the need for eXplainable Artificial Intelligence (XAI), which promotes algorithms that can provide clear reasoning for their outputs, thus allowing for human understanding and supervision. The speaker also discusses the barriers to adopting XAI, such as the size and entrenched nature of existing AI systems, unawareness of alternatives, and the inherent complexity of making AI explainable. She calls for action from developers, companies, and researchers to embrace XAI to ensure trust, supervision, validation, and regulation of AI systems.

Mindmap

Keywords

💡Black Box AI

Black Box AI refers to artificial intelligence systems whose internal processes are not transparent or understandable, making it difficult to explain how they reach certain decisions. In the context of the video, this lack of transparency poses a significant challenge, as it is unclear who is truly making decisions when AI is involved, potentially leading to a loss of human control over AI systems.

💡Deep Neural Networks

Deep Neural Networks are a class of machine learning algorithms modeled loosely after the human brain that are composed of multiple layers of interconnected nodes. They are known for their high performance but also for their complexity, which contributes to the 'black box' nature of AI as mentioned in the video. The speaker uses the example of a hospital's AI system estimating oxygen needs to illustrate the potential dangers of not understanding these networks.

💡Explainable AI

Explainable AI is a subfield of AI that focuses on creating algorithms that provide clear, understandable explanations for their decisions. The video emphasizes the importance of this approach to ensure that humans can trust, supervise, and validate the actions of AI systems. It is positioned as a solution to the problems posed by black box AI.

💡Oxygen Estimation

In the script, oxygen estimation refers to a hypothetical scenario where an AI system is used to determine the required amount of oxygen for a patient in an intensive care unit. This serves as an example to highlight the potential risks of relying on black box AI without understanding the underlying logic that drives its recommendations.

💡CEO Decision Making

The video discusses a scenario where a CEO makes decisions based on recommendations from a black box AI system. This illustrates the concern that without understanding the AI's reasoning, the CEO—and by extension the company—may be unknowingly allowing the machine to make critical decisions.

💡Regulation

Regulation in the context of the video pertains to the oversight and control of AI systems, particularly in relation to the GDPR, which mandates that companies explain their decision-making processes when handling personal data. The speaker points out that despite such regulations, black box AI continues to be used, leading to significant fines.

💡Consumer Demand

The script calls for consumers to demand transparency from AI systems that use their data. This is presented as a way to push for the adoption of explainable AI and to ensure that consumers understand how their data is being utilized.

💡ExplainNets

ExplainNets, as introduced by the speaker, is a term for a proposed solution involving algorithms that can generate natural language explanations for the decisions made by neural networks. These algorithms use fuzzy logic to interpret and explain the reasoning processes of AI systems, thereby increasing their transparency.

💡Fuzzy Logic

Fuzzy logic is a mathematical logic system that allows for reasoning with imprecise or uncertain information. In the video, it is mentioned as a tool used by ExplainNets to analyze and explain the behavior of neural networks, making AI decisions more understandable to humans.

💡Human Control

The concept of human control is central to the video's message, emphasizing the need for humans to maintain oversight and control over AI systems. The speaker argues that without explainable AI, there is a risk of AI indirectly controlling humanity, rather than the other way around.

💡Mystification

Mystification in the video refers to the process of making something seem mysterious or difficult to understand, which is a risk when AI systems are not transparent. The speaker warns that without explainable AI, the complexity of black box systems could lead to a loss of trust and a sense of mystification around AI.

Highlights

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

AI today is largely based on deep neural networks that are high performing but complex to understand.

The lack of transparency in AI algorithms poses a significant challenge in understanding their decision-making processes.

Imagine a hospital relying on AI to estimate oxygen needs for ICU patients; errors could have dire consequences without understanding the AI's logic.

A CEO making decisions based on AI recommendations without understanding the AI's reasoning raises questions about who is truly making decisions.

eXplainable Artificial Intelligence (XAI) is a field advocating for transparent algorithms understandable by humans.

Explainable AI could provide reasoning behind AI decisions, such as in the oxygen estimation problem, enhancing trust and supervision.

Despite its value, the adoption of explainable AI is hindered by the size of existing AI pipelines, unawareness, and complexity.

The field of explainability AI is still in its infancy, lacking a standard method for achieving transparency.

Developers, companies, and researchers are urged to start using explainable AI to build trust, supervision, validation, and regulation of AI.

GDPR requires companies processing human data to explain their reasoning process, yet black box AI continues to be used despite fines.

Consumers should demand explanations for AI used with their data to promote the adoption of explainable AI.

Failure to adopt explainable AI could lead to a world where AI indirectly controls humanity without proper supervision or understanding.

There are two approaches to achieving explainable AI: developing new algorithms or modifying existing ones to improve transparency.

ExplainNets, a top-down approach, uses fuzzy logic to generate natural language explanations of neural networks, aiding in understanding AI reasoning.

Human-comprehensible linguistic explanations of neural networks are key to moving towards explainable AI.

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