BI & Data Science Analyst at 10 Senses
The recent boom in Artificial Intelligence (AI) is the upshot of the remarkable success of Machine Learning models (ML models) and their immense potential to provide precise foresights.
AI development is not stopping. Moreover, they promise to bring even more autonomous systems that will better understand, learn, and even act on their own. Nevertheless, their effectiveness is currently limited by the system’s inability to provide accurate explanations for their decisions and actions to human users.
It has led to the emergence of the Explainable Artificial Intelligence (XAI) concept. We already described in our introduction to XAI. The idea of explainable AI can fulfill the need for more intelligent, symbiotic, and autonomous systems. Nonetheless, what are other reasons why we need explainable AI?
How does Explainable AI (XAI) work in brief?
Explainable AI tries to unravel the mystery of how black box models work and AI makes decisions. It studies and tries to provide users with steps in decision-making processes, such as:
- how an AI model makes a specific decision or prediction,
- why didn’t an AI system choose another option,
- how an AI model works, when it succeeds and fails,
- can we treat all decisions made by an AI system with confidence?
One way that XAI tries to increase AI explainability is by using ML algorithms that are inherently explainable. By using them, it bridges the gap between an AI model’s computer science computation and human comprehension.
In fact, simple ML models, like decision trees or linear regression, can already provide a certain level of transparency and traceability in the decision-making process without compromising the performance or accuracy of these models.
Nonetheless, more complex deep learning models, like neural networks, need to sacrifice a certain level of transparency and explainability for performance, power, and accuracy.
Explainable AI core concepts
In fact, the Explainable AI idea is based on three core concepts, such as:
- Interpretability – the ability to generate understandable explanations for their outputs,
- Transparency – visibility and comprehensibility of the inner workings,
- Trustworthiness – confidence among human users in the decision-making capabilities and making sure that the results are reliable and unbiased.
Why do we need explainable AI?
Increasing user adoption
First, Explainable Artificial Intelligence can significantly increase user adoption of AI systems. In fact, in many cases, users’ confidence in AI technologies can be hindered by the perception that AI is a “black box” that cannot be understood.
XAI tries to bridge this gap by providing insights into how AI systems work, making them more accessible and user-friendly. As a result, it contributes to increased user engagement and a better understanding of model behavior.
Interpretability, inclusiveness, and transparency
Explainable Artificial Intelligence also assists in building interpretable, inclusive, and transparent AI systems by:
- implementing tools explaining models for these tools,
- detecting and resolving bias, drift, and other gaps.
As a result, it equips data professionals and other business users with insights into why a particular decision was reached. Truth be told, it is crucial to promoting the trustworthy AI concept within organizations.
In fact, in certain use cases, such as healthcare, finance, and criminal justice, decisions made by AI algorithms can have significant real-world impacts. XAI helps us understand how these decisions are made, building trust, transparency, and accountability.
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Maximizing the performance of the model
Explainable Artificial Intelligence tools can also help developers and Machine Learning engineers diagnose and debug issues in machine learning algorithms by iteratively refining the predictions of the model and its performance.
What is more, if an AI system produces unexpected or incorrect results, explainability techniques can help identify the root causes of errors. The better you understand the potential weaknesses of the models, the easier it gets to optimize their accuracy.
It is also worth mentioning that Explainable Artificial Intelligence also allows for effective management of AI tools by implementing streamlined monitoring of model performance and training. As a result, organizations can continuously monitor the predictions of the model on multiple AI platforms and optimize model performance.
Such capabilities of XAI also promote effective collaboration between humans and AI systems. When users can understand an AI application’s reasoning, they can provide valuable corrections, or insights that improve the system’s performance.
Deploying AI systems with confidence
What is more, Explainable AI allows end users to appropriately trust and improve the overall transparency of AI applications by providing interpretable machine learning explanations. When AI practitioners deploy a model, they can get an instant prediction and a score showing how much a specific factor impacted the final output.
Even though explanations, like post-hoc explanations, don’t reveal any fundamental relationships in data samples or populations, they can be useful for:
- detecting patterns in the data,
- learn about the underlying processes,
- familiarize yourself with factors that contribute to certain decisions,
- compare model predictions.
Legal and ethical compliance
Moreover, implementing XAI techniques, which serve as artificially intelligent machine partners, can also help companies create responsible AI solutions, increase compliance with their AI solutions, and meet legal requirements set by regulatory bodies.
These days, many industries are subject to legal and ethical regulations that require explanations for decisions made by AI systems. XAI assists in meeting these regulatory requirements by providing human-language justifications for AI-driven outcomes.
Bias and fairness assessment
AI models can inadvertently learn biases present in the training data, leading to unfair or discriminatory decisions. XAI tools can help identify these biases and provide insights into why certain decisions may favor certain groups, allowing for bias mitigation and fairness enhancement.
Moreover, Machine Learning techniques can effectively reduce the impact of erroneous results and help identify the root causes of mistakes by providing an explanation for the results. It can be especially useful in decision-sensitive fields such as Medicine Industry, Credit risk assessment, Finance, or Legal, which are highly affected in the case of incorrect predictions.
Educated decisions
Finally, Explainable Artificial Intelligence algorithms also enable educated decisions. Apart from generating, for example, sales predictions, they can equip domain experts with insights about the main drivers of sales. Such information can be later used by business experts to adopt specific strategies and boost revenues.
Therefore, XAI allows for an automated and educated decision-making process, delivering valuable analytical insights to data professionals. Later, they can be visualized in reports and empower decision makers to adopt better strategies.
It can be especially important in high-stakes applications, such as autonomous vehicles or medical diagnosis. Understanding an AI’s decision process is crucial for identifying potential risks and devising strategies to mitigate them.
In summary, Explainable Acritical Intelligence is essential to enable transparent, ethical, and effective AI applications in various domains. It leads to improved trust, increased user confidence, better predictive power and prediction accuracy, accountability, fairness, and collaboration between humans and Artificial Intelligence. If you want to know more about XAI methods, read our introduction to XAI here.
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