Artificial intelligence (AI) is revolutionizing various sectors of our society due to its ability to detect patterns and trends in complex data and automate tasks that previously required highly specialized human expertise. Businesses increasingly rely on AI to make decisions that affect individual rights, human safety, and critical business operations.
How do AI systems arrive at conclusions? What data do they use? Can we trust their results? While having a functionally robust AI model is sometimes enough for its adoption, in certain cases, it is also necessary to understand how these models work to grasp how they generate predictions or decisions — that is, their "explainability." According to a McKinsey report, companies with an EBIT of 20% associated with AI use are more likely than others to adopt practices that enable the explainability of their AI systems. The explainability of an AI model allows decisions (or predictions) to be accompanied by a rationale. For example, in the banking sector, where decisions about granting loans often involve AI, it is crucial for stakeholders to understand why credit is approved or denied.
Modern AI models are becoming increasingly complex, often turning into "black boxes" that are difficult to comprehend. The internal workings of these models are not directly accessible to users and, in many cases, not even to the developers themselves. This is where "Explainable Artificial Intelligence (xAI)" comes into play. XAI comprises a set of methods and processes that allow users to understand the results produced by AI models.
XAI transforms "black boxes" into "glass boxes," making the operation of a model understandable, transparent, and trustworthy. Two widely used methods are LIME and SHAP values, which provide both a global understanding of how an AI model works and a local understanding of how it generated a specific response for a given dataset. This characterization helps evaluate an AI model's accuracy, data processing methods, and potential biases. It is essential to identify circumstances under which a model may fail, understand how a specific prediction was generated, and determine the model's suitability for its task. By enabling stakeholders to understand black-box models, xAI allows them to inspect, validate, or even challenge decisions made by these systems. XAI helps technical teams monitor an AI model's performance and enables business teams to better understand its potential.
XAI is particularly relevant in sectors dealing with sensitive data and strict regulations, where decision-making processes benefit from clear and understandable AI models. To comply with GDPR norms, particularly Article 22, which restricts automated decision-making, it becomes necessary to implement xAI to ensure that AI model outputs are accompanied by human-comprehensible explanations. XAI thus serves as a gateway for companies adopting responsible AI principles.
Leading solution providers incorporating xAI into their products include Microsoft, which integrates it into its global AI strategy with Azure solutions; IBM, with its Watson OpenScale product; and Google, through its Explainable AI tool implemented in its cloud-based AI services. These tools have applications in sectors such as healthcare, where diagnostic methods involving AI must provide explanations for the presence of certain diseases. Without these explanations, medical professionals are forced to evaluate predictions manually, reducing or nullifying the usefulness of AI. Seldon Technology Limited integrates xAI into its healthcare-focused products.
In the banking, finance, and insurance sectors, credit risk assessment benefits from xAI methods that inform risk analysis processes, as seen in solutions offered by Zest. PayPal is another example, using xAI to incorporate AI models for detecting fraudulent transactions. FICO's Falcon Fraud Manager product, with its Reason Reporter feature, also leverages xAI. With it, evaluators can better understand why a transaction was flagged as fraudulent and, if necessary, review or overturn the decision, providing a valid explanation to the client.
Another significant application of xAI is in autonomous driving, which relies heavily on AI models. Here, it is critical to ensuring the safety of autonomous vehicles and building trust with users. An xAI model can analyze sensor data to make decisions such as when to brake, accelerate, or change lanes. This is particularly important in accidents, where there is a moral and legal need to understand their root causes.
Why should companies adopt xAI? Better understanding of how an AI model works can lead to new business insights, providing competitive advantages while facilitating AI adoption and accelerating innovation processes. In sectors where AI is used for automating decision-making or supporting it, there is also a need to meet legal transparency and accountability requirements. XAI implementation is thus crucial for organizations adopting a responsible approach to AI development and implementation, aligning with principles of ethical data use and protection.
XAI is not just a step toward explainable AI models; it is also a bridge between process automation and the human quest for justification, trust, and fairness.
Article written by Henrique Carvalho, Senior Data Scientist at DXspark's AI Factory.