The Black Box Emergency | Javier Viaña | TEDxBoston

TEDx Talks
22 May 202304:49

TLDRJavier Viaña at TEDxBoston addresses the global emergency of black box AI, which lacks transparency and understanding of its decision-making processes. He emphasizes the risks of relying on AI without knowing its reasoning, especially in critical areas like healthcare and business decisions. Viaña introduces eXplainable AI as a solution, advocating for algorithms that provide human-understandable reasoning. Despite the challenges of size, unawareness, and complexity, he urges developers and companies to adopt explainable AI to ensure trust, supervision, and regulation. He also highlights the importance of linguistic explanations in making AI understandable, presenting 'ExplainNets' as a step towards this goal.

Takeaways

  • 🚨 The global emergency of 'black box' AI: Javier Viaña highlights the excessive use of AI based on deep neural networks, which are high performing but complex and often opaque in their decision-making processes.
  • 🤖 Lack of transparency: AI algorithms with thousands of parameters are hard to understand, leading to a 'black box' where we don't know what's going on inside a trained neural network.
  • 🏥 Critical applications: The example of a hospital using AI to estimate oxygen needs for intensive care patients illustrates the risks of not understanding the AI's decision-making process.
  • 💼 Decision-making in business: CEOs making decisions based on 'black box' AI recommendations without understanding the logic can lead to the machine, not the human, making the decisions.
  • 🧠 The rise of eXplainable AI (XAI): XAI advocates for transparent algorithms that provide reasoning understandable by humans, contrasting with the current black box models.
  • 🔍 Importance of explainability: In critical applications like healthcare, explainable AI could provide the reasoning behind decisions, which is essential for trust and safety.
  • 📈 Adoption barriers: Three main reasons for not using explainable AI are the size of existing AI pipelines, unawareness of alternatives, and the complexity of achieving explainability.
  • 📚 The challenge for developers: The speaker calls on developers, companies, and researchers to start using explainable AI to build trust, enable supervision, and facilitate validation and regulation.
  • 📋 Regulation and fines: The GDPR requires companies to explain their reasoning process to end users, and non-compliance results in significant fines, yet black box AI continues to be used.
  • 🙌 Call to action: Consumers are encouraged to demand explanations for AI used with their data, emphasizing the urgency of adopting explainable AI to prevent failures and maintain trust.
  • 🛠️ Two approaches to explainability: A bottom-up approach involves developing new algorithms, while a top-down approach modifies existing ones to improve transparency.
  • 🌐 ExplainNets as an example: The speaker introduces 'ExplainNets,' an architecture using fuzzy logic to generate natural language explanations of neural networks, aiming to pave the way towards explainable AI.

Q & A

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

    -Javier Viaña discusses the global emergency of the excessive use of black box artificial intelligence, which is difficult to understand and interpret due to its complexity.

  • What are the implications of using black box AI in critical decision-making scenarios like healthcare?

    -The implications include the risk of incorrect decisions with no clear understanding of the reasoning behind them, which can lead to potentially harmful outcomes for patients.

  • What is the main challenge Javier Viaña identifies with AI today?

    -The main challenge is the lack of transparency and understandability in AI algorithms, particularly deep neural networks, which are high performing but opaque in their decision-making process.

  • What is eXplainable Artificial Intelligence (XAI) and how does it differ from black box AI?

    -eXplainable Artificial Intelligence (XAI) is a field of AI that promotes the use of transparent algorithms whose reasoning can be understood by humans, as opposed to the non-transparent, complex black box models.

  • Why might a CEO rely on a black box AI's recommendation without fully understanding it?

    -A CEO might rely on a black box AI's recommendation due to the system's historical accuracy, even without understanding the logic behind the recommendation, highlighting a potential over-reliance on AI.

  • What are the three main reasons people are not using explainable AI, according to Javier Viaña?

    -The three main reasons are the size of existing AI pipelines which are deeply rooted in businesses, unawareness of the alternatives to neural networks, and the complexity of achieving explainability in AI.

  • How does Javier Viaña suggest we can trust, supervise, validate, and regulate artificial intelligence?

    -He suggests that the adoption of explainable AI is the only way to fully trust, supervise, validate, and regulate AI, ensuring transparency and accountability in AI decision-making.

  • 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 in that it mandates transparency, which black box AI fails to provide.

  • 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, advocating for the adoption of explainable AI to prevent blind trust in AI outputs.

  • What are the two approaches to adopting explainable AI that Javier Viaña mentions?

    -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 Javier Viaña's concept of ExplainNets and its purpose?

    -ExplainNets is a concept developed by Javier Viaña that uses fuzzy logic to generate natural language explanations of neural networks, aiming to provide a reasoning process that humans can understand, thus contributing to the field of explainable AI.

Outlines

00:00

🚨 The Global Challenge of Black Box AI

The speaker, Jenny Tayar, highlights the pressing issue of the excessive use of black box artificial intelligence (AI) systems, which are based on complex deep neural networks. These systems are high-performing but lack transparency, making it difficult to understand their decision-making processes. The speaker emphasizes the risks associated with relying on these systems in critical areas like healthcare and business decision-making, where the lack of understanding can lead to significant consequences. The crux of the problem is the inability to discern whether humans or machines are making decisions, and the potential for AI to control humanity without proper oversight.

Mindmap

Keywords

💡Black Box Artificial Intelligence

Black Box AI refers to artificial intelligence systems that are highly complex and not easily understandable, much like a 'black box' where inputs are fed in and outputs are produced without any clear insight into the internal processes. In the context of the video, it is described as a global emergency due to its overuse and the lack of transparency in decision-making processes, which can lead to unforeseen consequences and a lack of accountability.

💡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 makes them difficult to interpret. The video emphasizes the challenge of understanding what happens within these networks, which is a central theme of the talk.

💡eXplainable Artificial Intelligence (XAI)

XAI is an emerging field within AI that focuses on creating algorithms that can provide clear and understandable explanations for their decisions and actions. The video argues that XAI is essential for building trust and ensuring that humans can supervise and validate the outputs of AI systems, contrasting with the current prevalent use of black box AI.

💡Algorithm

An algorithm in the context of AI is a set of rules or procedures for solving a problem or performing a task. The video discusses the need for algorithms that are transparent and can be understood by humans, which is a fundamental aspect of XAI.

💡Intensive Care Unit (ICU)

The ICU is a special department within a hospital that provides care for patients who are critically ill and require constant monitoring. In the video, it is used as an example to illustrate the potential risks of relying on black box AI for critical decisions, such as estimating the amount of oxygen needed for a patient.

💡Supervision

In the context of AI, supervision refers to the ability of humans to oversee and understand the decisions made by AI systems. The video argues that without the ability to supervise AI, we risk allowing machines to make decisions without human understanding or control.

💡Regulation

Regulation in the video pertains to the rules and guidelines that govern the use of AI, particularly in relation to explainability and the protection of human data. The speaker cites the GDPR as an example of regulation that requires companies to explain their reasoning processes to end users.

💡General Data Protection Regulation (GDPR)

The GDPR is a regulation in EU law that focuses on data protection and privacy for all individuals within the European Union. The video mentions GDPR as an example of existing regulation that is relevant to the use of AI and the need for explainability in AI systems.

💡Consumer

In the video, the term consumer is used to refer to individuals whose data is being processed by AI systems. The speaker calls for consumers to demand transparency and understandability from the AI systems that use their data.

💡ExplainNets

ExplainNets, as mentioned in the video, is a term coined by the speaker to describe a top-down approach to improving the transparency of neural networks. These algorithms aim to generate natural language explanations of the reasoning processes within neural networks, using fuzzy logic as a mathematical tool.

💡Fuzzy Logic

Fuzzy logic is a form of logic that deals with approximate reasoning, which is useful in dealing with the uncertainty and imprecision found in human reasoning. In the context of the video, fuzzy logic is used as a tool within ExplainNets to help understand and explain the behavior of neural networks.

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 high performing but extremely complex to understand.

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

AI in hospitals could provide incorrect oxygen amounts without any explanation for its decisions.

The CEO of a company might unknowingly let a black box AI make decisions for them.

Without understanding AI logic, it's unclear who is truly making decisions: human or machine.

eXplainable Artificial Intelligence (XAI) advocates for transparent algorithms understandable by humans.

Explainable AI could provide reasoning behind AI decisions, such as oxygen estimation for patients.

Current AI lacks explainability, despite its value.

Three main reasons for not using explainable AI: size of existing AI pipelines, unawareness, and complexity.

Explainability in AI is not an easy problem and the field has barely started.

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

GDPR requires companies to explain the reasoning process to the end user, but black box AI still prevails.

Consumers should demand explanations for AI used with their data.

Failure to adopt explainable AI could lead to a world of blindly following AI outputs and loss of trust.

Two approaches to adopt explainable AI: developing new algorithms or modifying existing ones for transparency.

ExplainNets, a top-down architecture, uses fuzzy logic to generate natural language explanations of neural networks.

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