The Black Box Emergency | Javier Viaña | TEDxBoston

TEDx Talks
22 May 202304:49

TLDRJavier Viaña discusses the global emergency of 'black box' AI, which is complex and lacks transparency. He emphasizes the need for eXplainable AI (XAI) that offers understandable reasoning to humans, contrasting with the current opaque models. Viaña highlights the challenges of integrating XAI due to size, unawareness, and complexity. He calls for action, urging developers and companies to adopt XAI for trust, supervision, and regulation. Viaña introduces 'ExplainNets,' a top-down approach using fuzzy logic to generate natural language explanations of neural networks, advocating for a future where AI is controlled by humans, not the other way around.

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, leading to a 'black box' problem in AI.
  • 🏥 In critical applications like healthcare, the lack of AI transparency can have serious implications if the AI's output is incorrect.
  • 💡 Explainable AI (XAI) is a solution that promotes algorithms whose reasoning can be understood by humans.
  • 🔮 The adoption of XAI is crucial for trust, supervision, validation, and regulation of AI systems.
  • 📈 Companies often avoid XAI due to the size of their existing AI infrastructure, unawareness of alternatives, and the complexity of achieving explainability.
  • 📚 The field of explainability AI is still in its early stages, lacking a standard method for achieving transparency.
  • 🛑 GDPR requires companies to explain their reasoning process to end users, but black box AI usage persists despite hefty fines.
  • 🙌 Consumers should demand transparency from AI systems that use their data, advocating for the use of XAI.
  • 🔄 Two approaches to achieving XAI are the development of new algorithms or modifying existing ones to improve transparency.
  • 🌐 Javier Viaña's 'ExplainNets' is an example of a top-down approach using fuzzy logic to provide natural language explanations of neural networks.

Q & A

  • What is the main issue discussed in Javier Viaña's TEDxBoston talk?

    -The main issue discussed is the excessive use of black box artificial intelligence, which is complex and difficult to understand, posing a global emergency.

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

    -Using black box AI in healthcare can lead to serious consequences if the AI's output is incorrect, as there is no way to understand the reasoning behind its decisions.

  • What is the difference between black box AI and eXplainable Artificial Intelligence (XAI)?

    -Black box AI refers to AI models whose decision-making processes are opaque and not understandable by humans, while XAI advocates for transparent algorithms whose reasoning can be understood by humans.

  • Why might a company CEO rely on a black box AI's recommendation without understanding its logic?

    -A CEO might rely on a black box AI's recommendation because the system is often correct, but this reliance can lead to the machine making decisions instead of the human.

  • 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, unawareness of alternatives to neural networks, and the complexity of achieving explainability in AI.

  • What is the role of eXplainable Artificial Intelligence (XAI) in terms of trust and regulation?

    -XAI is crucial for building trust, allowing supervision, validation, and regulation of AI systems, ensuring that humans maintain control over AI decisions.

  • How does the General Data Protection Regulation (GDPR) relate to the use of AI and explainability?

    -The GDPR requires companies processing human data to explain their reasoning process to the end user, implying a need for explainable AI to comply with such regulations.

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

    -Javier Viaña urges consumers to demand that the AI used with their data provides explanations, promoting the adoption of explainable AI to prevent blind trust in AI outputs.

  • 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, and a top-down approach, which involves modifying existing algorithms to improve transparency.

  • Can you explain what Javier Viaña means by 'ExplainNets' and how they contribute to explainable AI?

    -ExplainNets are algorithms developed by Javier Viaña that use fuzzy logic to generate natural language explanations of neural networks, helping to understand the reasoning process behind AI decisions.

Outlines

00:00

🚨 The Challenge of Black Box AI

Jenny Tayar discusses the global emergency of black box artificial intelligence, which is characterized by deep neural networks that are high-performing but complex and opaque. She emphasizes the lack of understanding of the internal workings of these AI systems, which poses a significant risk in critical applications such as healthcare and corporate decision-making. The lack of transparency in AI decisions raises questions about accountability and the true decision-makers in scenarios where AI is heavily relied upon.

🔍 Introducing Explainable AI

The speaker introduces eXplainable Artificial Intelligence (XAI) as a solution to the black box problem. XAI promotes the use of transparent algorithms that provide reasoning understandable by humans. The potential benefits of XAI are illustrated with the example of an oxygen estimation problem in a hospital, where XAI could provide not only the required oxygen amount but also the rationale behind it. The speaker also addresses the current underutilization of XAI due to the size of existing AI pipelines, unawareness of alternatives, and the complexity of achieving explainability.

📚 The Importance of Explainability in AI

The speaker urges developers, companies, and researchers to adopt explainable AI to ensure trust, supervision, validation, and regulation of AI systems. She highlights the relevance of the General Data Protection Regulation (GDPR), which mandates companies to explain their reasoning processes to users, and points out the fines incurred due to non-compliance. The speaker calls for consumer demand for explainable AI to prevent a future where AI indirectly controls humanity without proper oversight.

🛠️ Approaches to Achieving Explainable AI

Two approaches to achieving explainable AI are presented: a bottom-up approach that involves developing new algorithms to replace neural networks, and a top-down approach that modifies existing algorithms to enhance transparency. The speaker shares her work on a top-down architecture called ExplainNets, which uses fuzzy logic to generate natural language explanations of neural networks' reasoning processes. She believes that such human-comprehensible explanations are crucial for the advancement of explainable AI.

🏆 The Future of Explainable AI

The speaker concludes by reiterating the necessity of adopting explainable AI and the potential of her developed architecture, ExplainNets, to provide linguistic explanations of neural networks. She sees this as a key step towards making AI more understandable and controllable by humans, thereby preventing the dystopian scenario of AI controlling humanity without proper understanding or regulation.

Mindmap

Keywords

💡Black Box Artificial Intelligence

Black Box Artificial Intelligence refers to AI systems that are highly complex and not easily understood, much like a 'black box' where inputs go in and outputs come out, but the internal workings are not transparent. In the video, Javier Viaña discusses the global emergency of relying on such systems without understanding their decision-making processes, which poses risks in critical areas like healthcare and business decisions.

💡Deep Neural Networks

Deep Neural Networks are a subset of machine learning algorithms modeled loosely after the human brain that are composed of layers of artificial neurons. They are known for their high performance but also their complexity. The speaker uses this term to describe the type of AI that is often opaque and difficult to interpret, which is a central concern in the video.

💡eXplainable Artificial Intelligence (XAI)

eXplainable Artificial Intelligence, or XAI, is an emerging field that focuses on creating AI systems whose decision-making processes are understandable and interpretable by humans. Javier Viaña emphasizes the importance of XAI as a solution to the black box problem, advocating for algorithms that provide transparency in their reasoning.

💡Algorithm

An algorithm in the context of AI refers to a set of rules or procedures used by a computer system to perform a specific task. The video script discusses the need for algorithms that are not only effective but also understandable, contrasting the complexity of current AI with the desired transparency of XAI.

💡Intensive Care Unit (ICU)

An Intensive Care Unit is a special department within a hospital that provides highly focused and intensive care to patients with severe health issues. In the script, the ICU is used as an example to illustrate the potential dangers of using black box AI in life-critical decision-making scenarios.

💡Decision-making

Decision-making in the video pertains to the process of selecting a course of action from among multiple alternatives based on a set of values and preferences. The speaker highlights the importance of understanding the logic behind AI-assisted decisions, especially when they impact significant areas such as corporate strategy or patient care.

💡Regulation

Regulation in this context refers to the rules and directives that govern the use and operation of AI systems, particularly with respect to data protection and transparency. The General Data Protection Regulation (GDPR) is mentioned as an example of such regulation that requires companies to explain their processes to end users.

💡GDPR

The General Data Protection Regulation (GDPR) is a legal framework that sets guidelines for the collection and processing of personal information from individuals who live in the European Union. Javier Viaña uses GDPR as a point of discussion to highlight the fines and penalties companies face for non-compliance, which indirectly relates to the need for explainable AI.

💡Consumer

In the video, a consumer is anyone whose data is being used by AI systems. The speaker calls for consumers to demand transparency from AI systems that utilize their data, advocating for their right to understand how decisions affecting them are made.

💡ExplainNets

ExplainNets, as introduced by Javier Viaña, is a term for a type of algorithm designed to provide natural language explanations for the behavior of neural networks. It uses fuzzy logic to analyze, learn, and articulate the reasoning process of a neural network, serving as an example of a top-down approach to improving AI transparency.

💡Fuzzy Logic

Fuzzy logic is a form of logic that deals with approximate reasoning, which allows for more human-like decision-making in AI systems. In the context of the video, fuzzy logic is used as a mathematical tool within ExplainNets to help generate understandable explanations of neural network 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 high performing but extremely complex to understand.

The challenge of understanding what happens inside a trained neural network is the biggest challenge in AI today.

AI decisions in hospitals, such as estimating oxygen needed for patients, can have serious consequences if wrong.

The lack of transparency in AI can lead to companies blindly following AI recommendations without understanding why.

The question arises: who is really making decisions, humans or machines, when AI lacks explainability?

eXplainable Artificial Intelligence (XAI) advocates for transparent algorithms that can be understood by humans.

Explainable AI would provide reasons behind AI decisions, such as in oxygen estimation for patients.

Current AI lacks explainability, which poses a significant risk in critical decision-making scenarios.

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

The field of explainability AI has barely started, and there is no standard method yet.

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

The GDPR requires companies processing human data to explain the reasoning process to the end user.

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

Failure to adopt explainable AI could lead to a world where AI indirectly controls humanity instead of the other way around.

Two approaches to adopting explainable AI are the bottom-up approach, developing new algorithms, and the top-down approach, modifying existing ones.

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

Human-comprehensible linguistic explanations of neural networks are essential for the path towards explainable AI.

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