You may have heard that Artificial Intelligence models are like a black box. We know what’s going on outside, that is, we know what it’s doing; but we don’t know what goes on inside: why it does what it does or how it does it. This entails both mistrust on the part of users and diagnostic difficulty for experts in order to correct errors and improve the models. Explainable Artificial Intelligence (XAI) refers to the development of AI algorithms and methods that can provide understandable and transparent explanations of its decisions and predictions. Its goal is to bridge the gap between the inherent complexity of AI systems and the need for human interpretability.
Why is Explainable Artificial Intelligence important?
The application of technology to the real world, in this case of artificial intelligence, always implies considering the human factor of interaction of societies with it. In this sense, one of the critical factors in the implementation of AI is trust in it. A study from the University of Queensland found that 61% of people are neutral or suspicious about the use of AI. If a machine is going to do or help a human do it, we need people to be able to trust the models, both in terms of their accuracy and robustness, and in areas like bias, discrimination, or unethical behavior. In the same way, both companies and public bodies are governed by regulations that must be complied with. Using XAI to ensure compliance with them enables organic integration into the organization’s processes.
On the other hand, the use of AI is becoming easier, which is why many non-expert users are starting to apply these techniques on their own. In this sense, the user’s understanding of what is happening needs less technical knowledge. At the same time, it allows the interest and capacity of these tools to be communicated to profiles focused on business management or investment.
This can help speed up the transformation process towards Industry 4.0. Finally, the XAI promotes the ethical development of AI, helping developers to mitigate biases and organizations and governments to demand quality standards in the results presented with a view to the neutrality of the models in political, medical or social aspects.
Techniques and methods of Explainable Artificial Intelligence
In Explainable Artificial Intelligence (XAI) various methods and techniques are used to provide interpretability and transparency to AI models. To understand them, we need to understand that models learn to recognize features in the data they train on. In other words, a model that is learning to recognize oranges will learn several characteristics from them: their shape (round), their color (orange), their texture (porous and shiny). By doing so, you will be able to recognize that an apple (red or green color) is not an orange, or that a pear (non-round shape) is not an orange either. From this idea, the following methods are applied:
- Rule-based models: These models generate decision rules based on predefined conditions and criteria, making the decision-making process transparent and interpretable.
- Feature importance analysis: These methods help identify the most influential features and provide insight into how the model makes decisions.
- Surrogate models: Surrogate models are simplified and interpretable models that approximate the behavior of other more complex models. By training a surrogate model on the predictions of the original model, it is easier to understand and interpret the decision-making process of the surrogate model.
- Post hoc interpretability techniques: Post hoc interpretability techniques attempt to perform a feature importance analysis for a set of individual data that the model predicts. To do this, statistics and game theory are used to understand why the model makes each individual decision.
- Model-Agnostic Approaches: Model-agnostic approaches are not specific to any particular model type and can be applied to any black-box model. Partial dependency plots illustrate how the model’s predictions change as a specific feature varies, marginalizing the effect of other features. Global feature importance techniques, such as feature permutation importance, measure the impact of variation of each feature on model performance.
Real World Applications for Explainable AI
Here are some of the applications of explainable AI:
Explainable Artificial Intelligence is used in medical diagnostics to provide interpretable explanations for disease predictions. This helps clinicians understand the reasoning that emerges from AI-generated diagnoses. It helps identify characteristics and patterns that contribute to disease risk, improving accuracy and transparency in decision making, and even helping to discover new diagnostic methods.
XAI techniques are applied in the credit scoring and loan approval processes to provide transparent explanations for credit decisions. Helps assess the factors that influenced an individual’s creditworthiness, enabling fair lending practices, regulatory compliance, and reduced bias or discrimination.
The XAI plays a crucial role in the development of autonomous cars. Helps solve security problems, such as explaining why a vehicle performed a certain action or maneuver, increasing user confidence
Explainable artificial intelligence is used in risk assessment models, such as insurance underwriting or cybersecurity threat detection. It provides explanations for risk predictions, helping insurers and cybersecurity analysts to understand the factors that contribute to a particular level of risk. This improves transparency and accountability in risk assessment processes.
Energy and sustainability
Through XAI, explanations of energy use patterns are also offered, identifying energy saving opportunities and helping users understand the impact of their actions on energy consumption, promoting more sustainable practices. We leave you a project on sustainable agriculture i4.0 that could benefit from the use of XAI.
In conclusion, Explainable Artificial Intelligence (XAI) is an essential field within AI research and development. It addresses the need for transparency, interpretability, and trust in AI systems, mitigating concerns around biased decision making, lack of accountability, and limited user understanding. Although XAI brings significant advantages, it also has limitations, such as the trade-off between interpretability and performance, the challenges posed by complex deep learning models, scalability issues, and the need for standardized approaches. Addressing these limitations through continued research and collaboration is crucial to moving XAI forward and realizing its full potential.
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