Artificial Intelligence is transforming the world of business, offering advanced solutions for data analysis, process automation and business strategy optimization. However, the growing complexity of AI models has raised a crucial issue: the comprehensibility of decisions made by algorithms. This is where the concept of Explainable AI (XAI) comes into play, i.e. the set of techniques and methodologies that make AI models more transparent and interpretable for users.
An opaque AI system can generate distrust and hinder its adoption, especially in regulated sectors such as finance, healthcare and law.
In fact, companies must be able to explain how and why an algorithm made a certain decision, both to comply with regulations on ethics and transparency and to guarantee customers and stakeholders greater security and control.
Benefits of Explainable AI in Business Decisions
The adoption of Explainable AI systems offers numerous benefits, including greater reliability of machine learning models and a reduction in the risks associated with automated decisions. Thanks to the ability to understand the logic behind an algorithm, companies can:
- Identify and correct errors: if an AI model makes an incorrect decision, understanding the underlying motivations allows for timely intervention.
- Improve regulatory compliance: many regulations, such as the GDPR in Europe, require that companies be able to explain automated decisions that influence users.
- Increase user and stakeholder trust: providing clear explanations on AI models’ predictions and recommendations improves relationships with customers, employees and investors.
- Optimize the decision-making process: understanding the factors that influence AI predictions helps companies make more informed and strategic decisions.
Thanks to these benefits, Explainable AI not only improves transparency, but becomes a competitive asset for companies that want to integrate artificial intelligence into their decision-making processes.
Methods to make AI more understandable
There are several techniques to make AI models more interpretable, ensuring greater transparency and trust in decision-making systems. Among the main strategies we find:
- Inherently interpretable models: some machine learning models, such as decision trees and linear regressions, are intrinsically more transparent than deep neural networks or deep learning algorithms.
- Post-hoc methods: techniques such as LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations) allow to analyze the decisions made by complex models, identifying the factors that have most influenced the output.
- Intuitive user interfaces: interactive dashboards and graphical visualizations can help non-experts understand AI predictions and their impact on business strategies.
- AI audit and continuous monitoring: implementing processes for the constant review of AI models helps to identify any biases or anomalies in the data used.
These techniques are fundamental tools to ensure that the use of artificial intelligence is increasingly aligned with the principles of fairness, transparency and accountability.
A constantly changing frontier
The benefits of Explainability are such that they continuously stimulate research on this important topic.
Recent developments, therefore, are pushing for the evolution of the Human-in-the-Loop concept, where the human decision maker supervises the outcome of the models and provides feedback on the quality of the prediction, towards mutual learning logics, in which both AI and humans adapt to each other.
New logics are emerging, such as the “Centaur” model (1) from Harvard University, a hybrid human-algorithm system that combines human intuition and algorithmic analysis to improve the decision-making process. Unlike the traditional Human-in-the-Loop, the Centaur model merges human expertise with machine learning, instead of limiting itself to simple human control. The Centaur model learns from human intuition, but at the same time improves the human decision-making process with AI information.
Application areas are diverse and include decision-making problems with incomplete data, uncertainty, or time constraints, or with critical choices, such as choosing the most relevant diagnoses and best treatment plans.
Intellico’s Approach to Explainable AI
For companies that want to integrate artificial intelligence into their strategies, it is essential to choose solutions that guarantee transparency and reliability. Intellico develops tools based on Explainable AI to help companies make more informed decisions, reducing the risks associated with opaque machine learning models.
Through the use of advanced technologies and interpretability methodologies, Intellico provides its customers with AI systems capable of offering clear and understandable insights. In this way, companies can fully exploit the potential of AI without sacrificing transparency and trust in decision-making processes.
Sources:
- Soroush Saghafian, Lihi Idan, Effective Generative AI: The Human-Algorithm Centaur, 2024
