Artificial intelligence (AI) has become one of the most widely used technologies in recent years, especially with the development of deep learning techniques that have shown the ability to enhance human productivity.
However, many of these AI models function as “black boxes”, being opaque and difficult for humans to audit. Because of this, explainable AI (XAI) was created. This set of tools aims to open these black boxes with the purpose of making the models more transparent and interpretable.
This guide provides an overview of XAI. Below, you will find its definition, history, applications in various sectors, as well as some of XAI’s limitations to consider for the ethical and responsible development of AI.
What Is Explainable AI About?
Explainable artificial intelligence (XAI) consists of a set of techniques and algorithms to make AI models more transparent and understandable for humans. It allows for effective understanding, auditing, and correction of AI models.
XAI models justify their results with logical reasoning, communicating their internal processes clearly and simply. Additionally, these models detect possible biases and limitations, providing detailed explanations of the reasoning behind each action.
XAI primarily emerged in the 2010s as a response to the increasing opacity of modern deep learning-based AI models. The motivation behind creating XAI was to address the issue of the “black box” in these AI models. Many of the current deep learning models function as “black boxes”, making it difficult to understand how they arrive at their predictions. XAI opens these black boxes by explaining how the models work, their training data, how they make specific predictions, their confidence levels, biases, and limitations.
This allows for the identification of cases where complete reliance on the information provided by AI is not advisable, knowing their weaknesses to mitigate or avoid systematic errors.
Therefore, we can say that XAI generates AI models that are more transparent, fair, and secure, which can be continuously refined, making AI more reliable and beneficial for humans.
The implementation of XAI is crucial in areas where algorithmic decisions can significantly impact people’s lives, such as healthcare, finance, and autonomous driving, among other sectors.
In the healthcare sector, XAI systems that assist in patient diagnosis facilitate the adoption of AI, as they enable doctors to understand the reasoning behind the diagnoses and incorporate them into their own clinical judgment.
Similarly, in financial services, explainability allows for auditing decisions like loan approvals or mortgage application rejections to detect possible biases or fraud.
In the military industry, the use of XAI systems is vital as it helps build trust between personnel and any type of AI tool or language, facilitating human decision-making.
In the autonomous vehicle industry, XAI is essential for passengers to comprehend the vehicle’s actions and to trust it with their safety.
Importance of XAI
Explainability is fundamental to generating greater trust and adoption in AI models, as most people are hesitant to rely on opaque algorithmic decisions that they cannot understand. XAI provides understandable explanations of how an AI model reaches its conclusions, making it more reliable for end users.
Furthermore, the transparency of explainable AI allows for the improvement of AI models by enabling developers to quickly and easily identify and correct any issues. It also safeguards AI models against malicious attacks, as irregular explanations would reveal attempts to deceive or manipulate the model.
Lastly, another key objective of XAI is to explain the processes and attributes in algorithms to detect possible biases or unfair outcomes. This is essential for an ethical and responsible deployment of AI. This has been one of the most controversial topics at the political level, leading to many regulations on AI in various countries such as the USA and the UK.
Although XAI seeks to make AI models more transparent, it has certain inherent limitations. Firstly, the explanations provided may oversimplify highly complex models. This can lead to controversy over whether more interpretable models are needed to accurately model responses.
Additionally, explainable systems often perform less effectively than “black box” models. Training models that not only predict but also explain their decisions add complexity.
Another significant limitation is that explainability alone does not guarantee the trust and adoption of AI. Some users may still trust generalized AI models even if understandable explanations of their potential shortcomings are provided.
Therefore, it is important to recognize that explainability comes with limitations, and an integrated approach is essential to develop reliable and trustworthy AI models for ethical and safe AI adoption.
Explainability is a key feature for the development of trustworthy AI, reducing opacity and enabling auditing, correction, and understanding of the models by humans.
Although XAI can be complex to apply in multiple cases, it is a tool that can help mitigate risks and responsibly harness the potential that artificial intelligence can provide to society.