The development of artificial intelligence is unstoppable. Models analyze images, make decisions, support medicine, optimize production and process enormous amounts of data. However, with this growing scale, one key question arises: can we truly trust AI?
To answer this question, technology organizations and regulators have developed a set of seven requirements that "trustworthy AI" systems should meet. This is the foundation of a responsible approach to model development.
Below, we present these requirements - described in an accessible way, but based on industry standards, best practices and the experience of companies that provide data infrastructure, such as Sky Engine AI.
Requirement #1 - Transparency
The first step in building trust is transparency—including how the model was trained, how it operates, the logic behind its decisions and the data used to create it. Users should be able to understand the key elements of the system's operation and organizations must be able to explain why the model behaved as it did.
Transparency isn't just about documentation. It's also about how AI is designed to ensure its decisions are traceable.
Requirement #2 - Data Reliability and Bias-Free
Models learn from what we give them. If the data is uneven in quality, contains errors, replicates human biases, or is incomplete, the model will reproduce them.
That's why using high-quality data (both real and synthetic) is so important. This is where providers like Sky Engine AI come into play, offering controlled, bias-free synthetic dataset generation, allowing models to be trained more responsibly and safely.
Requirement #3 - Security and resilience
AI should perform predictably and stably, even under challenging, unexpected conditions. The system must be resilient to erroneous input data, manipulations (e.g., adversarial attacks), hardware failures and non-standard scenarios.
This is why the training process utilizes:
- Diversified data (different environments, conditions, edge cases),
- Rigorous simulation testing,
- Model versioning and benchmarking.
Without resilience, there is no trust.
Requirement #4 - Privacy
Protecting personal data is one of the pillars of responsible AI. Models must not reveal or reproduce user information and training methods must comply with regulations such as GDPR.
Therefore, data minimization, anonymization and synthetic data techniques are playing an increasingly important role. These techniques allow companies to train models without having to process real and sensitive information.
Requirement #5 - Accountability and auditability
AI cannot operate in a vacuum - someone must be accountable for its results. Therefore, it is essential to define processes through which humans control, supervise and, if necessary, correct the model.
Accountability also includes clear audit rules, risk control and ensuring that each component (data, models, pipelines) can be traced and verified.
Requirement #6 - Accessibility and Inclusivity
AI systems should operate fairly for all user groups. This means designing models that take into account social, cultural and technological diversity - so that they do not discriminate against any group and are accessible to the widest possible audience.
- Inclusivity in AI is not a slogan! It is a practice related to data selection, testing and user interaction.
Requirement #7 - Sustainability
Developing AI requires enormous computational resources, energy and time. Therefore, increasing attention is being paid to building models that are energy-efficient, optimized and trained in an environmentally sustainable manner.
Solutions such as synthetic data and modular MLOps processes reduce the cost of training and testing models while maintaining high quality.
Summary
Trust in AI doesn't just happen - it must be carefully built. The seven requirements above provide the foundation upon which to build systems that are not only effective but also responsible, secure and compliant with regulations.
In an era of rapid AI development, organizations that understand that data quality, transparency and accountability are not optional extras - they are a necessity will gain the upper hand. Technologies like synthetic data and advanced DataOps/MLOps platforms only accelerate the path to AI we can truly trust.