The new provision on Data Governance: A focus on explainability

The introduction of the Data Governance Act (DGA) and the AI Act in the European Union marks a fundamental milestone in the regulation of data and artificial intelligence (AI). These regulations aim to establish a framework that ensures the ethical and secure management of data while emphasizing the need for explainability in AI systems, a crucial aspect for building trust and promoting transparency in the digital environment.

What is Data Governance?

Data governance, as defined by the DGA, is a management system that seeks to ensure that data is used ethically and responsibly, maximizing its value for society and businesses. In a context where the quantity and diversity of data have grown exponentially, this regulation establishes a set of norms and principles focused on protecting personal data and creating a secure environment that fosters collaboration without compromising users' privacy.

One of the pillars of this provision is the creation of European data spaces that facilitate a controlled and ethical exchange of data across different sectors. The DGA also establishes clear policies regarding transparency in data use and promotes the development of data technologies that respect the fundamental rights of individuals. This means that with the DGA, organizations are not only required to comply with data protection mandates but are also encouraged to adopt a culture of transparency at all levels of data management.

In particular, this regulation is crucial for AI, as access to large volumes of high-quality data is the foundation of many advanced AI systems. With the regulation, organizations are required to enhance their practices regarding data collection, storage, and processing, thereby ensuring that the decisions made by their AI systems are based on reliable and up-to-date data. Data governance, therefore, goes beyond a mere regulatory obligation: it becomes a competitive advantage for those organizations seeking to excel in an increasingly ethics-focused and responsible environment.

Explainability as a central element in Data Governance

One of the most innovative aspects of the AI Act is its focus on the explainability of AI systems. Historically, many AI systems have operated as "black boxes," where the decisions generated by the system are difficult to understand or justify. This "black box" approach has fostered skepticism and distrust, especially in sectors where precision and transparency are essential, such as healthcare, finance, and justice.

Explainability allows for breaking down and understanding how and why an AI system makes certain decisions. Below are the main benefits of this approach:

1. Fostering trust: The ability to explain the reasoning behind an AI system's decisions facilitates the creation of trust among end users. In areas such as medical diagnosis or financial decisions, explainability allows users to understand the factors influencing the system's recommendations, which in turn increases their willingness to accept and adopt the technology.
2. Regulatory compliance: The AI Act imposes strict transparency and accountability requirements for AI systems classified as high-risk. This means that companies integrating explainable AI will be in a favorable position to comply with European Union regulations, thus avoiding potential penalties and strengthening their public image. Explainability, therefore, is not only an ethical choice but also a regulatory requirement that demands a transparent and responsible approach to the development and use of AI.
3. Continuous improvement of models: Explainability benefits not only external users but also developers and engineers working with AI models. By being able to analyze the reasoning behind decisions, development teams can identify areas for improvement and make adjustments that optimize the system's performance. This feedback loop allows for continuous updates to AI models, ensuring they remain effective and aligned with the ethical values of the organization.

Qsimov: Leading the way toward explainability

In this context, Qsimov stands out as a leader in the development of AI solutions that prioritize explainability. Its innovative AI system, Qsimov GreenLightningAI (GLAI), has been designed to offer transparency and clarity, allowing end users to understand the mechanisms that generate the system's decisions. This explainability capability not only ensures compliance with the DGA and the AI Act but also strengthens the trust relationship between technology and its users.

In addition to meeting regulatory demands, Qsimov's focus on explainability also enhances its ability to make continuous improvements to the system. The ability to break down and understand each decision facilitates the identification of optimization and adjustment opportunities, ensuring that Qsimov's AI system is not only powerful but also fair and ethical.

Qsimov integrates this priority for explainability as part of a broader vision in which AI technologies must be accessible and understandable to all. By promoting transparent and ethical AI, the company helps its clients effectively and securely adopt artificial intelligence technologies, preparing them for a future where AI will play a central role in all industries.

The value of explainability in Qsimov's AI

By focusing on explainability, Qsimov not only responds to emerging regulations but also establishes a standard of best practices in the AI industry. In an increasingly competitive market, where users and consumers demand transparency, companies that integrate explainability into their AI systems will be in a favorable position to attract and retain customers.

Qsimov's commitment to transparency and ethics goes beyond mere regulatory compliance. It is about generating trust, enabling responsible use of AI, and contributing to the creation of a secure digital environment. Ultimately, data governance and AI explainability represent a crucial advancement for the responsible and sustainable adoption of technology, and Qsimov leads this transformation, ensuring that AI is powerful, ethical, and accessible.

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