The Black Box Emergency | Javier Viaña | TEDxBoston

TEDx Talks
22 May 202304:49

TLDRThe urgent need for explainable AI is highlighted in the transcript, emphasizing the risks of relying on 'black box' AI models that lack transparency. The speaker, an AI expert, presents a compelling case for adopting explainable AI, which provides clear reasoning behind its outputs. She outlines the challenges in implementing such systems, including the size of existing AI pipelines, unawareness of alternatives, and the complexity of creating transparent algorithms. The speaker advocates for a shift towards explainable AI, suggesting both bottom-up and top-down approaches, and introduces 'ExplainNets', a concept for algorithms that use fuzzy logic to offer natural language explanations. The call to action is clear: for the benefit of trust and control, the AI community must embrace explainable AI.

Takeaways

  • 🚨 The global emergency of black box AI: The excessive use of AI based on deep neural networks, which are high performing but complex and often not understood, poses a significant challenge.
  • 🏥 Consequences of AI errors: In critical applications like healthcare, where AI estimates oxygen needed for patients, incorrect outputs can lead to severe consequences due to the lack of understanding of the AI's decision-making process.
  • 🏢 AI in decision-making: CEOs and companies relying on black box AI for decision-making may inadvertently allow machines to make decisions without understanding the rationale, raising questions about accountability.
  • 🤔 The need for explainability: There is a growing need for eXplainable Artificial Intelligence (XAI) that provides transparent algorithms and reasoning understandable by humans.
  • 🔄 Challenges in adopting XAI: The size of existing AI pipelines, unawareness of alternatives, and the complexity of creating explainable models are the main reasons why many are not yet using XAI.
  • 📈 The impact of GDPR: The General Data Protection Regulation (GDPR) requires companies to explain their reasoning processes, but many still face fines for non-compliance and continue to use black box AI.
  • 🏛️ The call to action: Consumers should demand that the AI used with their data is explainable, as a step towards ensuring that AI serves humanity and is not indirectly controlling it.
  • 🔄 Two approaches to XAI: A bottom-up approach involves developing new algorithms, while a top-down approach modifies existing ones to improve transparency and explainability.
  • 🧠 ExplainNets as a solution: An example of a top-down approach, ExplainNets uses fuzzy logic to generate natural language explanations for the reasoning process of neural networks, aiming to make AI more understandable.
  • 🛣️ The path to explainable AI: Human-comprehensible linguistic explanations of neural networks are essential for advancing the field of XAI and ensuring that AI remains a tool controlled by humans.

Q & A

  • What is the main issue with black box AI according to the transcript?

    -The main issue with black box AI is that its internal workings and decision-making processes are not understandable or transparent to humans, which poses challenges in terms of trust, supervision, and accountability.

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

    -The complexity of deep neural networks is a problem because they have thousands of parameters, making them high performing but extremely difficult to understand, which means we cannot easily grasp how they arrive at their conclusions.

  • How does the lack of explainability in AI impact healthcare, as mentioned in the transcript?

    -In healthcare, if an AI used for estimating oxygen needed for a patient in an ICU provides incorrect output, the lack of explainability means medical professionals cannot understand the reasoning behind the AI's decision, potentially leading to critical mistakes with serious consequences.

  • What is the role of eXplainable AI in addressing the challenges of black box AI?

    -eXplainable AI aims to provide transparent algorithms that can be understood by humans. It offers the ability to not only provide outputs but also explain the reasoning behind those outputs, which is essential for trust, validation, and regulation of AI systems.

  • What are the three main reasons why companies are not using explainable AI, according to the transcript?

    -The three main reasons are: 1) Size - many companies have large AI pipelines deeply integrated into their businesses, making changes difficult and time-consuming; 2) Unawareness - neural networks are so prevalent that there is often a lack of motivation to explore alternatives; 3) Complexity - achieving explainability in AI is a challenging mathematical problem and the field of explainability AI is still in its early stages.

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

    -The GDPR requires companies that process human data to explain the reasoning process behind their decisions to the end user. This regulation has led to fines for non-compliance, highlighting the need for more transparent and explainable AI systems.

  • What are the two approaches to developing explainable AI mentioned in the transcript?

    -The two approaches are: 1) A bottom-up approach, which involves developing new algorithms that replace neural networks; and 2) A top-down approach, which focuses on modifying existing algorithms to improve their transparency.

  • What is the significance of the 'ExplainNets' architecture mentioned in the transcript?

    -ExplainNets is a top-down approach to understanding neural networks. It uses mathematical tools like fuzzy logic to study the network model, learn from it, and generate natural language explanations for the reasoning process of the network, aiming to make AI more understandable to humans.

  • What potential consequences does the transcript suggest if we do not adopt explainable AI?

    -If we do not adopt explainable AI, there could be a loss of trust in AI and humans, blind following of AI outputs leading to failures, acceptance of some failures as non-failures, and an indirect control of humanity by AI, instead of humans controlling AI.

  • How does the transcript propose that consumers can play a role in promoting explainable AI?

    -The transcript suggests that consumers can demand that the AI used with their data provides explanations for its decisions, thereby pushing for greater transparency and adoption of explainable AI systems.

  • What is the importance of linguistic explanations in the development of explainable AI?

    -Linguistic explanations are crucial in explainable AI because they provide human-comprehensible reasoning behind AI decisions, which is essential for trust, validation, and regulation of AI systems, paving the way towards more transparent and understandable AI.

Outlines

00:00

🚨 The Global Emergency of Black Box AI 🚨

This paragraph discusses the critical issue of the excessive use of black box artificial intelligence, which is prevalent in today's AI systems based on deep neural networks. These complex algorithms, with thousands of parameters, are high performing but not easily understood, leading to a lack of transparency in their decision-making processes. The speaker, having worked on this problem for years, identifies it as the biggest challenge in AI today. The potential risks of relying on such systems, especially in critical areas like healthcare and corporate decision-making, are highlighted. The speaker emphasizes the need for explainable AI that provides transparent reasoning understandable by humans, contrasting it with the current black box models. The lack of adoption of explainable AI is attributed to the size and integration of existing AI pipelines, unawareness of alternatives, and the complexity of the mathematical problem it poses. The speaker advocates for the development and adoption of explainable AI to ensure trust, supervision, validation, and regulation of AI systems.

Mindmap

Keywords

💡Black Box Artificial Intelligence

Black Box Artificial Intelligence (AI) refers to AI systems whose internal workings are not understandable or interpretable by humans. These systems, typically based on deep neural networks, have complex structures with thousands of parameters, making it difficult to comprehend how they make decisions. In the context of the video, the speaker emphasizes the risks and challenges posed by relying on such opaque systems, especially in critical applications like healthcare or business decision-making, where understanding the rationale behind AI's decisions is crucial.

💡Explainable Artificial Intelligence (XAI)

Explainable Artificial Intelligence (XAI) is a field within AI that focuses on creating transparent algorithms whose decision-making processes can be understood and interpreted by humans. XAI contrasts sharply with black box AI models by aiming to provide insights into the reasoning behind AI's decisions. The video highlights XAI as a necessary evolution to ensure AI systems are trustworthy, comprehensible, and can be effectively supervised and regulated. Examples like the oxygen estimation scenario underscore the importance of explainability for validating AI-driven decisions in sensitive contexts.

💡Deep Neural Networks

Deep Neural Networks (DNNs) are a class of AI algorithms that mimic the way human brains operate, allowing machines to recognize patterns and solve complex problems. Despite their high performance, DNNs are a primary example of black box AI due to their intricate architectures and the vast number of parameters. The speaker in the video discusses DNNs to underline the challenge of achieving high performance while maintaining transparency and understanding in AI systems.

💡Transparency

Transparency in AI refers to the ability to understand and trace how AI systems make decisions. The video emphasizes transparency as a crucial component of explainable AI, contrasting it with the opacity of black box models. Transparent AI models allow users and developers to comprehend the rationale behind AI decisions, fostering trust and enabling more informed and ethical use of AI technology.

💡Neural Network Parameters

Neural network parameters are the internal variables of a neural network that are adjusted through training to help the model learn from data. The complexity and high number of these parameters contribute to the black box nature of many AI systems. The video discusses parameters to illustrate why deep neural networks are so difficult to understand and interpret, setting the stage for the need for explainable AI.

💡GDPR

The General Data Protection Regulation (GDPR) is a legal framework set up to protect individuals' personal data in the European Union. It requires companies to explain their processing of personal data to consumers. The video mentions GDPR to highlight the regulatory challenges and potential financial penalties faced by companies using non-transparent AI systems, emphasizing the urgency for adopting explainable AI to comply with such regulations.

💡Algorithmic Decision-Making

Algorithmic decision-making involves using AI systems to make decisions based on data analysis. The video brings up examples like healthcare oxygen estimation and business decisions to demonstrate the potential risks and implications of relying on AI without understanding its decision-making process. This context stresses the importance of explainable AI in enabling human oversight and ensuring that algorithmic decisions are ethical and justified.

💡Complexity

In the context of the video, complexity refers to the intricate nature of AI systems and the mathematical challenges involved in making these systems explainable. The speaker attributes the slow adoption of explainable AI to the complex problem of unraveling and articulating the reasoning processes of deep neural networks. This complexity is one of the main barriers to transitioning from black box models to transparent, understandable AI.

💡Fuzzy Logic

Fuzzy logic is a form of many-valued logic that deals with reasoning that is approximate rather than fixed and exact. In the video, the speaker introduces fuzzy logic as a mathematical tool used in their ExplainNets architecture to interpret and explain the decisions of neural networks. Fuzzy logic's role in creating explanations showcases a method for bridging the gap between complex AI decisions and human comprehension.

💡ExplainNets

ExplainNets is presented in the video as an innovative architecture developed by the speaker to provide natural language explanations for the decisions made by neural networks. Utilizing fuzzy logic, ExplainNets exemplifies a practical approach to enhancing the transparency and interpretability of AI systems. This example underscores the potential for research and development to create more explainable and trustworthy AI technologies.

Highlights

Global emergency due to excessive use of black box AI

AI based on deep neural networks are high performing but complex

Lack of understanding of inner workings of trained neural networks

The challenge of AI today is to make its processes understandable

Example of a hospital using AI for oxygen estimation in ICU

Uncertainty in decision-making when AI output is incorrect

The dilemma of whether humans or machines are making decisions

Introduction of eXplainable Artificial Intelligence (XAI)

XAI advocates for transparent algorithms understandable by humans

Explainable AI would provide reasoning behind its outputs

Current AI lacks explainability despite its value

Three main reasons for not using XAI: size, unawareness, complexity

The field of explainability AI has barely started

Call to action for developers, companies, and researchers to use XAI

GDPR requires companies to explain reasoning process to end users

Consumers should demand AI transparency regarding their data

Vision of a world without XAI leading to failures and loss of trust

Two approaches to adopting XAI: bottom-up and top-down

ExplainNets, a top-down approach using fuzzy logic for explanations

Natural language explanations are key to achieving explainable AI