"XAI"
A GAME CHANGER FOR IIOT
Today, a growing number of industries and organizations are using artificial intelligence to make informed decisions. As technologies advance and become more complex, users face the difficulty of understanding and tracing the decision-making process of « AI » solutions that operate like a « Black box ».
This opacity creates the following major challenges that hinder the potential of businesses:
- Increased Legal Risks: Potential non-compliance with crucial regulations such as GDPR and the AI Act, exposing companies to costly penalties.
- Insidious Biases: Opaque algorithms can generate discriminatory or erroneous results, negatively impacting reputation and performance.
- Opaque Decisions: Impossible to understand the « why » behind « AI » actions, undermining trust and adoption.
- Barrier to Adoption: The mistrust of domain experts (engineers, technicians, etc.) towards incomprehensible « AI » limits its integration and benefits.
"XAI" : a response to the challenges of black box AI
Faced with these challenges, a revolution, initiated by DARPA (US Defense Advanced Research Projects Agency ) is underway: “Explainable Artificial Intelligence” also called « XAI », adopts a « White box » approach, where understanding is at the heart of the process. Imagine intuitive interfaces that make data and predictions crystal clear, allowing your teams to validate, correct, and even improve your « AI » models.
Unlike deep neural networks and other « Black boxes », with « XAI » you can finally understand the logical path of your algorithms, thereby strengthening the trust of your team, it’s the key to unlocking the true potential of « AI » in your industry.
Focus on "XAI" techniques that make a difference
The integration of « XAI » is more accessible than ever thanks to the following techniques:
Prediction Accuracy
Accuracy is a critical factor in determining how effectively AI performs. By running simulations and comparing the outputs of « XAI » with the training dataset results, we can measure prediction accuracy.
Traceability
Traceability is another essential aspect of XAI. It involves restricting decision-making processes and defining a narrower scope for machine learning rules and features.
In addition, end users need to be educated on how and why AI makes decisions, avoiding to see AI as a black box.
"XAI ": a strategic lever for your industrial performance
« XAI » is not just a passing trend; it is the catalyst for your success in Industry 4.0 and a transformation of the Industrial Internet of Things « IIoT ». Faced with the increasing volume of data generated by « IIoT », « XAI » identifies the influential variables in data flows, thereby optimizing resource allocation and decision-making.
By integrating XAI, you:
- Secure your regulatory compliance: By documenting and justifying every action of the « AI ».
- Minimize operational and financial risks: By detecting and correcting potential biases.
- Strengthen the trust of your teams: By giving them the keys to understand and validate decisions made by « AI ».
- Accelerate the adoption of « AI »: By overcoming mistrust through understanding. By making AI decisions interpretable, « XAI » helps operators adjust processes in real-time. This is particularly the case in smart factories, where « XAI » can explain the predictions of predictive maintenance, enabling more effective intervention.
- Optimize your processes: By obtaining clear and actionable insights for continuous improvement.
Explainable AI (XAI) is not just a technological evolution , it’s a critical enabler for unlocking the full potential of AI in IIoT. By making decisions transparent, traceable, and understandable, XAI mitigates risks, builds trust, and accelerates adoption. In an era where industrial performance depends on reliability and compliance, XAI stands as a strategic lever to transform data into value and prepare your organization for the challenges ahead.