Understanding Explainable Artificial Intelligence (XAI)

XAI, or explainable artificial intelligence, makes it easier for regular people to trust the results of computation and machine interpretations by breaking them into a series of understandable processes and procedures.

XAI aims to make AI systems transparent, understandable, and accountable. (Image: Unsplash/Igor Omilaev)

XAI, short for Explainable Artificial Intelligence, is changing how we interact with AI systems. Traditional AI can be hard to understand, like a black box. XAI helps us see how AI makes decisions, making it easier to trust and use. This article looks at what XAI is, how it started, and the benefits of using it.

Key Takeaways

• Explainable Artificial Intelligence (XAI) makes AI decision-making processes transparent and understandable to humans, enhancing trust and accountability, especially in sensitive fields like healthcare and finance.
• DARPA formalized the concept of XAI with contributions from researchers like Tim Miller, Finale Doshi-Velez, and Been Kim. XAI focuses on making AI more interpretable.
• XAI improves trust, accountability, decision-making, user engagement, and helps detect and mitigate biases in AI systems, making AI applications more reliable and ethical.
• Tools like LIME, SHAP, IBM Watson’s explainability features, Google’s What-If Tool, and FICO’s credit score explanations are practical implementations of XAI that enhance transparency and usability.
• Generative AI focuses on creating new content without needing to explain its processes, making it ideal for creative tasks. In contrast, XAI aims to provide clear explanations for AI decisions, which is crucial for applications requiring transparency and ethical compliance.

Also read: Discussing The Potential Risks and Dangers of Artificial Intelligence. 

What is Explainable Artificial Intelligence (XAI)?

Explainable Artificial Intelligence (XAI) is an AI system that provides explanations regarding its decisions and actions that are transparent, interpretable, and easily understood by humans. XAI is different from traditional AI, which often works as a “black box” with a non-transparent, harder-to-understand decision-making process. XAI helps build trust and ensure accountability when various industries adopt AI.

History of Explainable Artificial Intelligence: Origin and Creator

As AI technology becomes widespread and popular, more people are addressing traditional AI’s limitations, such as its lack of transparency and accountability. XAI gained popularity as more complex AI models like deep learning and neural networks emerged that lacked interpretability despite having high performances.

In the mid-2010s, the Defense Advanced Research Projects Agency (DARPA) was credited with formalizing the term “Explainable AI” through its XAI program. The goal of the program is to create a suite of machine-learning techniques that can produce more explainable models, unlike traditional AI.

Key Contributors

While DARPA played a pivotal role in popularizing XAI, many researchers and institutions have contributed to the development of the field. Prominent figures include:

• Tim Miller: A notable researcher who has extensively studied the importance and methodology of explainability in AI.
• Finale Doshi-Velez and Been Kim: Researchers from Harvard University and Google Brain, respectively, who have contributed significantly to the theoretical and practical aspects of XAI.

Also read: Artificial Intelligence Vs. Machine Learning: Differences and Similarities.

5 Benefits of Explainable Artificial Intelligence

The concept of explainable AI (Image: Birlasoft)

1. Trust and Transparency

Explainable artificial intelligence increases trust as it makes AI systems’ decision-making processes and results transparent and clearer to understand. Trust and transparency are crucial in sensitive industries and areas like healthcare and finance. 

Some people are skeptical of AI due to its “black box” nature, so XAI can help users and stakeholders understand how and why decisions are made, also promoting greater acceptance and adoption of AI technologies.

2. Improved Accountability

As XAI helps processes become more transparent, it will be easier to audit and hold AI systems accountable. One major discussion about the cons of AI is its ethical standards. XAI can help trace and explain AI decisions, ensuring they meet legal and ethical requirements to avoid misuse and harm.

3. Enhanced Decision-Making

XAI can provide insights into the factors influencing AI recommendations, helping identify and correct biases, improve the quality of decisions, and make more informed choices. This is especially valuable in areas like medical diagnosis and financial forecasting, where decisions have significant implications.

4. User Engagement

With its clear explanations, XAI can improve user engagement by allowing users to interact with AI systems more effectively. XAI can benefit engagement-dominant industries like customer service and educational apps, where understanding AI responses can enhance the overall quality of interaction.

5. Bias Detection and Mitigation

XAI allows for the detection and mitigation of biases in AI systems. As developers and users can understand decision-making processes, they can identify biases and address them. Explainable artificial intelligence helps create fairer AI systems, benefiting areas like hiring, law enforcement, and lending.

The 4 Challenges of XAI

1. Complexity vs. Interpretability

Of course, XAI will be more complex in terms of interpretability. Advanced AI systems like deep neural networks provide high accuracy but are challenging to interpret. The dilemma is that simplifying the models can make them more understandable but will reduce the performance. Interpretability or performance?

2. Defining Explainability

Like how beauty is subjective, how explainable something is also depends on each person. Different stakeholders may require different levels and types of explanations. Defining what makes a satisfying and perfect explanation will be challenging. What a data scientist considers understandable may be very different from what an end-user considers. 

3. Scalability

It is challenging to ensure explainable artificial intelligence techniques are scalable to large and complex datasets and models. Techniques that work well on smaller datasets or simpler models might not scale effectively to larger, more complex systems.

4. Bias and Fairness

While XAI aims to improve transparency, it can also inadvertently reveal biases present in the AI models. Addressing and mitigating these biases requires additional effort and scrutiny, ensuring that the explanations themselves do not propagate unfair or biased outcomes.

Also read: Artificial Intelligence Vs Human Intelligence Debate Points.

Examples of Explainable Artificial Intelligence

• LIME (Local Interpretable Model-agnostic Explanations): It explains predictions by approximating a complex model with a simpler and interpretable one locally. For example, it can show which parts of an image influenced a model to classify it as a cat.
• SHAP (SHapley Additive exPlanations): SHAP values assign importance to each feature in a model’s prediction. It uses game theory to provide a unified measure of feature importance, explaining how features impact individual predictions.
• IBM Watson: This includes features that explain AI decisions, making its recommendations transparent and particularly useful in fields like healthcare and finance.

Is ChatGPT XAI?

ChatGPT is an advanced language model that can generate human-like responses based on user input. Although it provides coherent and contextually relevant responses, it does not inherently include explainable AI (XAI) features. 

ChatGPT provides explanations and detailed responses based on patterns derived from the data it was trained on, not from an underlying, interpretable model of how it arrived at a particular output.

On the other hand, XAI aims to make the decision-making processes of AI models interpretable and transparent. XAI must provide insights into how and why a model made a specific decision. 

In its current form, ChatGPT operates mainly as a “black box” model, meaning its internal workings and decision pathways are not easily interpretable by humans. 

Although it can provide explanations based on its training data, it isn’t an XAI feature, as ChatGPT does not provide accurate representations of the internal mechanics of the model’s decision-making process.

Generative AI Vs. Explainable AI: What Are The Differences?

Generative AI and Explainable AI are both part of the AI landscape but serve different purposes. ChatGPT is an example of Generative AI, that focuses on creating new content from its training data, such as text, images, etc. As explained previously, ChatGPT is a “black box” model, which is not an important feature of XAI.

Explainable artificial intelligence emphasizes transparency and understanding in AI models by solving the “black box” model traditional AI has. The primary goal of XAI is to make the internal workings and decision-making processes of AI systems interpretable to humans. 

Regardless, both kinds of AI are important and useful depending on each user’s goals. Generative AI is helpful in producing creative outputs that don’t require much explanation of its process, like content generation tasks.

In contrast, XAI is essential for applications where understanding and trust in AI decisions are critical to ensure ethics and accountability. 

Also read: The Role of AI in Mental Health: Enhancing Diagnosis and Treatment.

Conclusion

Explainable Artificial Intelligence (XAI) is essential for enhancing the transparency, understandability, and trustworthiness of AI systems. XAI can offer clear insight into the decision-making process of AI systems, unlike traditional AI models that operate as “black boxes.”

Despite XAI’s many advantages, some challenges exist, such as complexity and interpretability, defining satisfactory explanations for diverse stakeholders, and ensuring scalability and performance while maintaining accuracy. As AI progresses, continuous research and development in XAI are imperative to tackle these obstacles and enhance the trustworthiness and ethical utilization of AI technologies across diverse domains.

References

• DARPA. “Explainable Artificial Intelligence (XAI).” Link
• Miller, Tim. “Explanation in Artificial Intelligence: Insights from the Social Sciences.” Link
• Doshi-Velez, Finale, and Kim, Been. “Towards A Rigorous Science of Interpretable Machine Learning.” Link
• Ribeiro, Marco Tulio, et al. “Why Should I Trust You?”: Explaining the Predictions of Any Classifier.” Link
• Lundberg, Scott M., and Lee, Su-In. “A Unified Approach to Interpreting Model Predictions.” Link
• IBM Watson. “Explainability.” Link
• Google AI. “What-If Tool.” Link
• FICO. “Explainable Machine Learning.” Link
• OpenAI. “ChatGPT.” Link