AI Governance: Challenges and Perspectives

Artificial intelligence has ceased to be a mere laboratory experiment and has become a driving force for global transformation. It diagnoses diseases, optimizes supply chains, and even writes code. However, as machines learn to make decisions once reserved for humans, an uncomfortable question arises: who watches the watchers? The answer is not to limit technology but to build a robust governance framework that serves as the "nervous system" of its development.

This dilemma transcends the technical realm to become a social imperative. AI does not just optimize processes; it also reproduces historical biases, threatens privacy, and concentrates power in the hands of a few. Governments, businesses, and citizens face an unprecedented challenge: building bridges between rampant innovation and the values that must be preserved. Talking about AI governance is no longer optional—it is a collective responsibility to prevent technological progress from becoming a disruptive force without direction.

This article explores the pillars of AI governance, analyzes how different countries are addressing its regulation, and highlights the role of platforms like Google Cloud in implementing these strategies in a practical and scalable way.

What is AI governance?

AI governance is a set of principles, tools, and regulations designed to ensure that artificial intelligence systems operate ethically, transparently, and in alignment with human values. It is not about bureaucracy but about creating an environment where innovation and responsibility coexist.

Key Components:

• Ethics: Avoiding bias, discrimination, or harm to vulnerable groups.

• Transparency: Understanding how systems make decisions.

• Compliance: Adapting to laws such as the General Data Protection Regulation (GDPR) or the EU AI Act.

• Security: Protecting data and preventing malicious use.

An example occurred in 2022 when a tech company had to disable its recruitment system because it discriminated against women. The problem arose because the historical data used to train the AI reflected an existing gender gap in the sector.

Global challenges in AI governance

1. Algorithmic bias: When AI reflects human prejudices

AI models learn from historical data, which often contains structural inequalities

Technical Solution:

• Data Audits: Tools like Google Cloud’s What-If Tool allow users to simulate how model decisions change when adjusting variables like gender or ethnicity.

• Diversity in Development Teams: Including experts in ethics and social sciences in technical processes.

2. The Black Box: Understanding Opaque Decisions

Many advanced models, such as deep learning, function as "black boxes." This creates distrust, especially in critical sectors like healthcare. In these cases, doctors may hesitate to rely on AI-based diagnoses due to a lack of transparency in their internal logic. However, technical solutions like Galen, SNGULAR’s generative AI platform for hospital environments, are changing this paradigm.

How does Galen address model opacity?

By leveraging the power of advanced medical models like Google’s MedLM and Gemini Pro, Galen provides healthcare professionals with a controlled environment where they can interact with specialized conversational assistants. This platform not only answers clinical questions but also explains its reasoning with a level of accuracy comparable to human experts. This facilitates medical decision-making and improves efficiency in hospital processes.

Galen allows healthcare professionals to access detailed reports, justifying each diagnosis based on patient history, medical images, and relevant scientific literature. This not only enhances trust in automated systems but also facilitates their integration into clinical workflows.

To better understand the real impact of AI in the healthcare sector, you can access this webinar featuring practical and real cases on the impact of AI in health and pharma.

3. Data protection: Privacy in the era of mass AI

AI requires vast amounts of data, but mishandling this information can expose sensitive details, creating security risks, privacy violations, and misuse of personal data. In sectors such as healthcare, finance, and retail, protecting this data is essential to maintaining user trust and complying with regulations.

Protection Techniques:

• Advanced Anonymization: Using techniques like differential privacy, which adds mathematical "noise" to data to prevent individual identification without compromising analytical utility.

• End-to-End Encryption: Protecting data at all stages, even during processing, to prevent unauthorized access.

• Granular Access Control: Implementing permissions and reinforced authentication to ensure that only authorized users access sensitive information.

• Federated Learning: An innovative approach in which AI models learn from distributed data without transferring private information to a central server.

Who leads AI governance in an organization?

Within an organization, AI governance oversight is not the responsibility of a single person or team but involves multiple leadership levels and specialized areas. The effective implementation of a governance framework requires a clear distribution of responsibilities to ensure the safe, ethical, and strategically aligned use of AI.

The CEO and senior executives play a fundamental role in integrating AI into corporate strategy. They are responsible for establishing a robust governance framework and defining a clear vision for AI usage within the company, prioritizing transparency and ethics.

The legal and compliance department analyzes regulatory risks and ensures that AI solutions comply with applicable laws. They also anticipate potential legal implications and develop mitigation strategies to prevent future issues.

Audit and risk management teams are essential for monitoring AI model performance, verifying accuracy, and ensuring fairness in results. They conduct periodic assessments to detect biases, errors, or failures in algorithmic functions.

While each of these roles is crucial, AI governance should not rest on a single department. It is a shared responsibility where the entire organization must commit to using AI responsibly.

Best practices for implementing AI governance

Para aplicar la gobernanza de la IA de manera efectiva, es necesario adoptar una visión integral que involucre la cooperación de distintas partes interesadas y abarque diversos ámbitos dentro de la organización. A continuación, se presentan algunas buenas prácticas para su implementación:

Documentation

Keeping detailed records of the entire AI system lifecycle is fundamental for ensuring transparency, reliability, and accountability. This involves documenting data sources, quality, potential biases, training methodologies, and decision-making processes.

Leadership and cultural commitment

AI governance should be promoted from top management, as business leadership is responsible for establishing clear guiding principles and ensuring that AI use aligns with organizational values and strategic objectives.

Stakeholder participation

The success of AI governance also depends on active stakeholder participation, including employees, customers, regulators, and business partners. Establishing an open and ongoing dialogue with these actors fosters trust and allows for the identification of risks and opportunities.

Continuous supervision and evaluation

Periodic monitoring of AI systems is necessary to detect errors, biases, or deviations. Auditing and evaluation mechanisms help correct failures and continuously improve the reliability and security of models.

Training and awareness

To ensure effective AI governance, employees must be knowledgeable about the risks and opportunities associated with this technology. Providing ongoing training in AI ethics, algorithmic fairness, and applicable regulations will strengthen responsible adoption.

Regulatory compliance

Organizations must ensure that their AI systems comply with existing regulatory frameworks in each country or sector. Adopting international standards and regulations helps reduce legal risks and promotes the security and reliability of AI models.

Google Cloud and AI Governance

Google Cloud has developed advanced strategies to strengthen AI governance, providing tools that ensure data protection and compliance with global regulatory frameworks.

Some key Google Cloud initiatives in this area include:

• Data Residency and Security: Google Cloud offers infrastructure in multiple global regions, including the Spain Cloud Region, allowing companies to ensure data residency and comply with local regulations.

• Sovereignty Controls: Enables the immediate deployment of sovereign infrastructures with access audits and European support options for greater data control.

• Advanced Encryption: Implementing Google Cloud Key Management Services and External Key Management to provide additional security and justify access to sensitive information.

• Industry-Specific Solutions: Google Cloud adapts its tools for highly regulated industries like healthcare, banking, and government, facilitating compliance with sector-specific requirements.

To learn more about how Google Cloud drives responsible AI governance, you can access its resource platform at the following link: Google Cloud - Responsible AI.

Towards responsible AI governance

Artificial intelligence has ceased to be just a technological tool and has become a reflection of our decisions as a society. Its progress should not be halted but rather redefined as a process of responsible and collaborative innovation. However, real change will not occur solely in research laboratories or regulatory frameworks but in organizations’ ability to embrace distributed governance. When a CEO drives social impact audits, an engineer adjusts parameters to eliminate bias in a predictive model, or a doctor validates diagnoses with clear algorithmic explanations, they are all contributing to the creation of a more transparent, fair, and secure AI ecosystem.

The future of AI will not only require stricter laws and regulations but also spaces for experimentation where governments, businesses, and citizens work together to find real-time solutions. It is not just about controlling technology but redesigning the institutions that govern it. This means rethinking engineering education, where ethics are as important as programming, and transforming business models so that profitability coexists with metrics of fairness and algorithmic responsibility. In the end, the key question is not whether AI can imitate human intelligence but whether we can ensure that it also reflects our ability to learn, correct mistakes, and evolve.