With artificial intelligence (AI) playing a more and more important role in our lives, recommending products we should buy or diagnosing diseases, these sophisticated systems need to be interpretable. And this is where Explainable AI (XAI) steps in. Ok, but what is an Explainable AI, and why is it so important to us that we need to use this environment? Let’s dive in.

What is Explainable AI?

Simply put, explainable AI is The methods and applications of artificial intelligence technology in such a way that the solution can be understood by human experts. This could be differentiated from the idea of a ‘black box’ in machine learning where it is not possible by even its creator to provide a plausible reason for a decision taken by AI.

The goal of creating XAI models is not to make the entire AI system from collecting data to producing insights transparent and understandable.

Why is Explainable AI Important?

Trust and Adoption – People need to trust AI as these systems are starting to make life-changing decisions. As trust is gained, transparency drives the adoption of AI tech.

How decisions are made — an outcome-centric viewThere has also been a movement towards increased transparency and explainability in high-stakes sectors such as healthcare, finance, or criminal justice. For instance… With XAI, errors or biases can be traced back to their sources.

Debugging/Improvement: Once we understand why an AI system reached certain conclusions, it dials together errors and biases in the model without going back to the drawing board.

Regulatory Compliance Most industries are mandated by laws to provide transparency in their decision-making process. XAI also supports compliance with these regulations.

For example, XAI can be used to guarantee AI systems are ethical by design and not reinforcing destructive prejudices.

Techniques for Implementing Explainable AI

There are many methods and tools to address this challenge and make AI more explainable.

Feature Importance: This technique ranks the input features according to their importance in predicting model outputs. It provides an insight into what factors are more important to make decisions on.

LIME (Local Interpretable Model-agnostic Explanations): Localized explanation for any classifier can be provided using LIME where a linear regression model is fitted around the data to explain the prediction.

SHAP (Shapley Additive exPlanations): A game theory approach to measure the importance value of each feature concerning a specific prediction.

Partial Dependence Plots (PDP) — These types of plots show the marginal effect or partial dependence one or two features have on our predicted outcome from a machine learning model.

Decision trees are often less accurate than many of the more complex models discussed here, but they have the distinct advantage (when implemented well) that they can be very easy to understand and follow their decision-making process.

These are used in deep learning models, especially the one that is being deployed at NLP taken for attention model that tells where to put more focus while passing input so they can highlight exactly what we need from large data.

Challenges in Implementing Explainable AI

We know the advantages of XAI, but as has been demonstrated there are a plethora of hurdles in its way to implementation:

The Complexity vs. Interpretability Trade-off — The best performing model is usually deep neural networks that are typically the most complex and hard to interpret [boschlearning at GitHub, n.d.] There are often tradeoffs between traditional model accuracy and interpretability, making this a hard problem to solve.

There Are No Standard Metrics: It is very hard to compare various XAI approaches since there are no standard measurements in place to quantify explainability.

Human Cognition Limitations: Even if an AI system can give the inference explanation, it might be too complex to understand for humans (and we still need to do this at a massive scale).

The Wall: Explanations could in theory be biased or mask biases, and hence users can be misled by the way explanations are designed.

The Future of Explainable AI

Given the ongoing rise and incorporation of AI, XAI is set to only become more paramount in practice. We can expect to see:

Standardization: Industry standard to make explainability in AI systems more versatile.

Regulatory Frameworks: Broader regulations that require explainability in AI systems (i.e. with high stakes)

Better Techniques: Evolution of XAI techniques that can explain even more intricate AI models.

Embedding XAI into the AI Development Lifecycle rather as an afterthought.

Explanation Interfaces for Human-in-the-Loop: Development of interface displaying step-by-step explanations based on the interactors’ function and its layers straight from high or intermediate down to low level.

Conclusion

The challenge with explainability is not just a technical one, it’s also fundamentally an issue of society. As the number of decisions delegated to AI systems increases, we need to be able to deconstruct and understand those decision-making processes for us to trust, and validate them. This is why, XAI serves as the fundamental factor that our AI-driven future is opaque to none and renders an impartial advantage to all. If we can make these more complex algorithms a little less mystifying, then potentially we’re not just making AI systems better — but creating an environment in which humans are significantly smarter and stronger for dealing with AIs.