What is Explainable AI exactly and How does it work
Businesses are increasingly relying on artificial intelligence systems to make business decisions. This can significantly impact individual rights, human safety, and critical business operations. But the question is, how do these models draw conclusions, and what data do they use? Can these results be trusted or not? To answer this question, we need to address the issue of Explainable AI. In this article, we are going to talk about Explainable AI completely, so if you have any doubts about this topic, stay with us until the end of the article.
What is Explainable (XAI)?
Explainable AI is a set of processes and methods that enable users to understand and trust the results and outputs produced by machine learning algorithms. Explainable AI describes an AI model, its expected impact, and any biases that may exist. This topic goes a long way in describing the accuracy, transparency, and results of artificial intelligence-based decisions. Explainable AI is very important for an organization or a company because it builds trust and confidence when building artificial intelligence models. Explainable AI allows companies and organizations to take a responsible approach to developing artificial intelligence.
How does Explainable AI work?
Explainable AI can be classified into the following three types:
- Explainable data: Clarifying the data used to train the model, the type of data and content, the reasons for its selection, the method of evaluation, and whether there is a need for an effort to remove bias or not.
- Explainable predictions: Specify all features of the model that are active or used to achieve a specific and unique output.
- Explainable Algorithms: Shows the individual layers the model comprises and explains how they contribute to a prediction or output.
Explainable data is the only type that drives Explainable AI for neural networks. Explainable algorithms and predictions are still under investigation and research.
In this section, two main approaches are explained:
Proxy modeling
Involves using a different model, such as a decision tree, to approximate another model. The result and output are approximations that can differ from the model results.
Designing for interpretability
Includes designing models that can be easily explained. This approach can reduce the overall accuracy or predictive power of the model.
Explainable AI aims to make it easier for users to comprehend how a model makes decisions. However, the methods employed to achieve explainability might severely restrict the model. Typ explanatory methods include bayesian networks, decision trees, and sparse linear models.
Types of Explainable AI Algorithms:
- LOCAL INTERPRETABLE MODEL-AGNOSTIC EXPLANATION (LIME)
It is one of the most common algorithms. This algorithm makes a decision and, by querying nearby points, creates an interpretable model representing the decision and then uses the model to provide explanations.
- SHAPLEY ADDITIVE EXPLANATIONS (SHAP)
This algorithm is another common algorithm that describes a given prediction by mathematically calculating how each feature contributes to the prediction. This algorithm acts as a visualization tool to a large extent and can visualize the output of a machine learning model to make it more understandable.
- MORRIS SENSITIVITY ANALYSIS
Morris sensitivity analysis is known as Morris method. This method works as a one-step analysis at a time; that is, only one level input is set in each run. This approach is frequently used to decide whether model inputs are significant enough for additional investigation.
- CONTRASTIVE EXPLANATION METHOD (CEM)
This technique identifies desired and undesirable aspects in a model to explain classification models. It works by defining why a certain event happened versus another and helps developers understand why.
- SCALABLE BAYESIAN RULE LISTS (SBRL)
These algorithms help explain the predictions of a model by combining pre-extracted frequent patterns into a decision list generated by a Bayesian statistical algorithm. This list comprises "if, then" rules in which antecedents are extracted from the data set, and the set of rules and their order are learned.
Importance of explainable AI
It is very important for organizations and businesses to fully understand AI decision-making processes with model monitoring and AI accountability and not trust them without verification. Explainable AI can help humans understand and explain machine learning algorithms, deep learning, and neural hub networks.
Machine learning models are typically considered black boxes that are impossible to interpret. Neural networks used in deep learning are among the most difficult for humans to understand. Bias, usually based on race, gender, age, or location, is a long-term risk when training artificial intelligence models. In addition, the performance of the artificial intelligence model can change because the production data or training data is different. As a result, it is necessary to continuously monitor and manage models to improve the explainability of artificial intelligence and measure the business impact of using these algorithms. Explainable AI helps improve end-user confidence, model auditability, and constructive use of AI. It also reduces the compliance, legal, security, and reputational risks of artificial intelligence production.
Examples of explainable AI
In this section, we have given you some examples of the use of explainable AI:
- Healthcare: Explainable AI can explain diagnoses when diagnosing a disease; this capability can help doctors explain their diagnosis to patients and tell how a treatment plan will be carried out. This helps the patients and doctors trust more and reduces any ethical problem. In this regard, we can mention the diagnosis of patients with pneumonia. Explainable AI can also be useful in the healthcare context with medical imaging data for cancer diagnosis.
- Manufacturing: Explainable AI is used to explain why the assembly line is not working properly and what adjustments it needs over time. This work is very important for improving machine-to-machine communication and understanding and helps to create more situational awareness between humans and machines.
- Defense: Explainable AI can also be used for military training programs to explain why an AI system makes decisions. This is important because it helps reduce potential ethical challenges, for example, why it misidentified an object or missed a target.
- Autonomous vehicles: Explainable AI has great importance in the automotive industry due to the many publicity events related to accidents caused by self-driving cars. This work emphasizes explanatory techniques for artificial intelligence algorithms, especially when using cases involving safety-critical decisions. Explainable AI can be used for self-driving vehicles because it increases explainability and situational awareness in accidents or unexpected situations, leading to more responsible technology performance.
Limitations of explainable AI
The challenges and limitations of Explainable AI include the following:
Data privacy and security
Although analyzing public and private data can provide important insights for decision-making, it may be necessary to use sensitive data to explain some decisions. The use of these data should be done carefully to prevent them from being exposed to vulnerabilities and hackers. Without proper management of sensitive data, the issue of compromising the privacy and security of individuals and organizations arises, which can have very serious consequences. As a result, creating clear guidelines and protocols for data management and security in explainable AI systems has a great importance.
Complexities of artificial intelligence models
AI models evolve over time to meet the needs of organizations. The demand for effective decision-making can lead to the selection of more complex artificial intelligence models that are very difficult to explain. Explainable AI systems must constantly improve to keep pace with these changes and maintain the ability to provide meaningful explanations for AI decisions. This requires constant research and development to ensure that explainable AI techniques can effectively overcome the challenges of complex and evolving models.
Human bias
Explainable AI focuses on transparency compared to traditional artificial intelligence models. However, it can still be affected by bias in the data and algorithms used to analyze it, and this is because the data used for the training and operation of an explainable AI system depends on parameters determined by humans. Addressing human bias in explainable AI requires careful examination of the data sources and algorithms used in these systems and greater efforts to promote diversity and inclusion in the development of artificial intelligence technologies.
User understanding
If explainable AI intends to make an artificial intelligence model understandable, some users may not have the necessary background knowledge to understand the explanation fully. Broadly, explainable AI systems should be designed so that the explanations they provide are tailored to the needs and knowledge levels of different user groups, which can include the use of visual aids, interactive interfaces, or other tools to help users better understand the reasoning behind AI decisions.
Tools and frameworks for developing and evaluating XAI models
In our rapidly advancing world of artificial intelligence, the call for clarity and understanding within these complex systems grows louder by the day. It’s sparked a wave of innovation chiefly focused on the birth and evaluation of explainable AI algorithms. In sectors that deeply impact human lives, such as legal, fiscal, and health realms, it’s become crucial that AI doesn’t just work, but that its decision-making is laid bare in understandable terms.
At the heart of this movement lie explainable AI algorithms. These are the building blocks for developing AI that we can decode and relate to. Explainable AI algorithms are designed to peel back the layers of AI decision-making, demystifying the process and aligning it more closely with our need for clarity and accountability.
An array of tools exists for the creation of explainable AI algorithms. InterpretML, for instance, is an essential open-source toolkit offering numerous techniques to render machine learning models more interpretable. With this, developers gain a better handle on AI system transparency, providing explanations and visual insights into model workings.
LIME, standing for Local Interpretable Model-agnostic Explanations, has emerged as another robust resource. By tweaking input data and monitoring predictive shifts, LIME hands us locally accurate explanations for the recommendations spit out by complex models, a game-changer for the realm of explainable AI algorithms.
Meanwhile, SHAP, or SHapley Additive explanations, brings to the table a cohesive metric for gauging feature significance within predictive models. SHAP equips developers with a solid theoretical base, vital for comprehending and parceling out machine learning predictions, which steers us towards more lucid models.
Evaluating explainable AI algorithms is where frameworks like AI Fairness 360 enter. Conceived by IBM, this toolkit is a double-edged sword tackling fairness issues and offering critical model interpretability. It guides developers in assessing their models’ transparency and equity across diverse demographic slices.
Then there’s ELI5, a Python library with the slogan ‘Explain Like I’m 5’. It encapsulates simplicity, providing explanations for machine learning models that are accessible and user-friendly. This supports a broad span of model types and sits as a cornerstone resource for developers honing explainable AI algorithms.
As the push for AI transparency gains momentum, the development of explainable AI algorithms stands in the limelight. These tools and frameworks aren’t just aiding the current generation of researchers and developers; they’re redefining future AI advancements. In this burgeoning sector, explainable AI algorithms are rapidly shifting from a specialized pursuit to a mainstream mandate, shaping an AI future where transparency isn’t optional but the gold standard.
Final Thoughts
Explainable AI is essential to addressing the challenges and concerns of artificial intelligence. Transparency, trust, accountability, compliance, performance improvement, and advanced control of artificial intelligence systems are provided by explainable AI. More complex models, such as neural networks and ensemble models, require more techniques and tools to explain, while simpler models are easier to explain. Therefore, explainable AI and the integration of its techniques can ensure explainability, transparency, fairness, and accountability in a world that operates on artificial intelligence.
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