Artificial intelligence is rapidly evolving from an experimental tool to a business-critical technology. Companies are using AI systems to automate processes, identify risks and tap into new business areas. But with increasing use comes increasing responsibility: unreliable or unfair systems can not only cause financial damage, but also undermine the trust of customers and partners. Quality in AI therefore means more than just technical performance – it encompasses accuracy, robustness, fairness, transparency and compliance with regulatory requirements.
What is AI quality?
AI quality describes the extent to which an AI system operates reliably, trustworthily, efficiently and in accordance with ethical and regulatory standards.
It can be divided into different dimensions.
Functional quality
Accuracy and precision:
How correct are the AI’s results compared to a known truth or a defined goal?
A good example of accuracy and precision is AI-assisted skin cancer detection. In dermatology, skin lesions are photographed and then analysed using AI software. The AI is designed to distinguish between benign and malignant skin changes. Both accuracy, i.e. the percentage of all correct predictions, and precision, i.e. the proportion of cases that are actually malignant among all cases identified as ‘malignant’ by the AI, are important. High accuracy reduces the risk of skin cancer being overlooked (false negatives), and high precision reduces the risk of harmless moles being incorrectly classified as malignant (false positives).
Robustness:
How well does the system respond to disturbances, unusual inputs or noise in the data?
A clear example of robustness is the use of AI in traffic sign recognition. Autonomous vehicles use cameras and neural networks to recognise traffic signs. In everyday life, however, signs are often not perfectly visible because they may be dirty, partially obscured, damaged or weathered, or recognition may be impaired by bad weather. A misinterpreted ‘stop’ sign, for example, could have fatal consequences. Robust systems recognise the stop sign despite adverse conditions. This is achieved, for example, through data augmentation in training, i.e. the targeted insertion of dirt, shadows, distortions and other disturbances into training images. In addition, ensemble models or sensor fusion help to avoid misinterpretations.
Generalisation ability:
Can the AI also deliver good results with new, previously unknown data?
A good example of this is AI for detecting flood areas using satellite images. The training data can be images of floods in Central Europe, for example, i.e. typically rivers with wide floodplains and predominantly summer and autumn images with moderate cloud cover. If an AI system trained in this way is confronted with satellite images showing, for example, floods in Southeast Asia with narrower rivers and dense vegetation, taken during the rainy season with heavy cloud cover and coastal flooding after cyclones, only an adequately generalising AI model will produce sufficiently good results.
Technical quality
Performance:
What are the key performance parameters, such as response times, computing power and scalability?
AI-supported route planning in a navigation app is a good example that illustrates the importance of performance. Such an app uses AI to calculate the fastest route for users. The AI processes real-time traffic data, weather information, data on roadworks, road closures, etc. If the route calculation takes too long, i.e. more than a few seconds, the user may lose interest and stop using the app. With millions of parallel routing requests, the algorithms must work efficiently. Performance-optimised AI can perform the same calculations with less server power. In addition, under certain conditions, requests can increase dramatically, e.g. during major events or in crisis situations. Good scalability means that the AI remains stable and fast even under increased load.
Maintainability:
How easy is it to update, improve or debug the model?
AI-supported detection of building damage after natural disasters can serve as an example here. If, for istance, an AI system is used to identify buildings damaged by natural disasters using satellite and drone images, the AI system must be kept up to date. If the development, building types or types of damage have changed over time, the model must be retrained in order to continue to achieve effective building detection. To this end, the AI model should be modular in design and the training data and model parameters should be well documented. Then only the damage classifier can be retrained with new sample data. Updates may take a few days rather than months.
Traceability:
Are the essential features of the AI system well documented, e.g. the system architecture, training data, parameters or changes made?
A good example in this context is an AI system for parcel route optimisation at a delivery service. The goals of such a company are short driving times, low fuel consumption and punctual delivery. If there are disruptions in route optimisation, it is very important for the company to be able to trace which errors led to these disruptions, as the financial risks are high. For example, were version changes to the AI, incorrectly integrated traffic data sources or faulty parameter changes the cause of the disruptions? Good traceability in practice means that all changes to the AI are fully documented. This includes timestamps, responsible developers, the reason for a change, data sources used and their update status, or a comparison of old and new model performance before rollout. If a problem arises, it is possible to revert to a previous stable version within a short period of time, identify the exact error and correct it in a targeted manner.
Trust and security
Explainability:
Is it possible to understand why the AI makes a particular decision?
This can be illustrated well using the example of AI-supported lending at a bank. When a bank uses such a system, various decision-making criteria are taken into account, such as income, expenses, debt level, payment history, employment status and much more. If a customer’s AI-processed loan application is rejected and the assessment result cannot be explicitly justified, neither the customer nor the bank staff can understand in detail why this decision was made. This not only leads to potential mistrust of the bank on the part of customers, but also means that regulatory requirements may not be met. If the bank uses an explainable AI model, e.g. using SHAP values or LIME, it can justify the rejection, for example as a result of an excessive debt ratio or insufficient length of employment.
Fairness and bias control:
Are discriminatory biases identified and minimised?
An example of this aspect is AI-supported applicant selection. If an AI system suggests applicants for interviews and the underlying AI model has been trained with the company’s ‘historical’ hiring data, this can lead to unintended bias in the results. If, in the past, a disproportionate number of men were hired for management positions, this may lead to applications from women being rated less favourably by the AI on average today. The reason for this is not the qualifications of the female applicants, but the data set used. Appropriate ‘bias control’ is therefore important in practice. AI models should therefore be checked accordingly before they are used, i.e. selection quotas by gender, age, origin, etc. should be compared and bias metrics such as ‘demographic parity’ or ‘equal opportunity’ should be used. If necessary, further training with more balanced data must be carried out or sensitive characteristics must be anonymised.
Security and data protection:
Is the system sufficiently hardened against attacks and is sensitive data protected?
For example, if an insurance company uses voice AI in customer service to answer customer enquiries by telephone, the conversations often contain sensitive personal data such as name, address, date of birth, insurance number or even health information. If the audio data were then processed unencrypted, an external service provider would have unnecessarily extensive access to the data, or if customer data were spied on and published on the darknet in the event of a cyber attack, considerable damage would be caused. Technical, organisational and legal protective measures for such AI systems are therefore essential to prevent misuse and data breaches. Such measures include, for example, end-to-end encryption of voice data during transfer and storage, pseudonymisation or anonymisation of personal data, access rights based on the ‘need-to-know principle’, regular penetration tests to identify security gaps, and appropriate or legally prescribed storage periods.
Ethical and social quality
Transparency:
Are the limitations and risks of the AI system disclosed?
Social media is an example from everyday life. The companies involved use AI to automatically moderate content. This allows hate speech or fake news, for example, to be identified and excluded. If this process is not transparent, users do not know why a post has been deleted or on what criteria. Appropriate guidelines that define which content is prohibited play an important role here. In this way, the AI decision can be explained to the user, and there is an opportunity for review and complaint.
Accountability:
Who is responsible for what in the event of errors or damage?
What happens if a logistics company uses autonomous delivery vehicles and one of these vehicles causes an accident because the AI misinterpreted the traffic situation? This example shows the importance of responsibility in AI systems. Without clear responsibility, it remains unclear whether the vehicle manufacturer, the AI developer or the operator is liable. This results in risks, including in terms of corporate reputation. Therefore, these liability issues should be regulated by contract, damage documentation should be good and insurance aspects should be clarified.
Sustainability:
Is the AI system energy-efficient and resource-saving in terms of training and operation?
In the age of large language models, the corresponding resource consumption is playing an increasingly important role. If a company does not have a suitable sustainability strategy and does not measure or optimise its resource consumption, this results in a high carbon footprint. A sustainability strategy can, for example, result in the use of renewable energy in data centres, the optimisation of training through more efficient algorithms or less redundant computing runs, or the reuse of pre-trained models. As a result, resource consumption is reduced and the company’s public image is improved.
Why is AI quality important?
The quality of AI systems is a decisive factor in their economic benefits and long-term acceptance in the market. Reliable and robust models deliver consistent results, increase customer satisfaction and thus promote willingness to enter into long-term contracts – a clear competitive advantage.
In addition, high-quality AI contributes significantly to reducing liability and reputational risks. Faulty decisions or discriminatory results can not only cause financial damage, but also permanently damage trust in the brand.
Another key aspect is compliance with legal and regulatory requirements. With the increasing regulation of AI systems – in the EU primarily through the AI Act – the ability to demonstrate compliance at an early stage is becoming increasingly important. Companies that systematically ensure their AI quality avoid penalties, lengthy audits and costly retrofits.
Quality is also a key factor from a technical perspective. Scalable and maintainable systems can be efficiently transferred to new markets and use cases without having to be completely reworked each time. This significantly reduces the effort required for adjustments while increasing the speed of innovation.
Last but not least, tested and verifiably fair AI creates an advantage in building trust. Companies that can demonstrate transparency and security strengthen their brand image and position themselves as responsible market leaders in an increasingly competitive environment.
Conclusion: Quality is crucial in AI systems
The quality of AI systems is not a marginal issue, but the basis for success. It determines whether applications work reliably, transparently and in accordance with legal and ethical requirements. Functional dimensions such as accuracy, robustness and generalisability ensure that models deliver correct results in practice and work reliably even under adverse conditions. This is complemented by technical aspects such as performance, maintainability and traceability, which are crucial for stable operation and efficient further development.
In addition, trust and security are becoming increasingly important. Explainable AI models, active bias control and transparent documentation create the necessary basis for ensuring acceptance among users, customers and regulatory authorities. At the same time, ethical and social factors such as responsibility and sustainability ensure that AI is used in a way that is not only economically viable but also socially sustainable.
For providers, this means that high-quality AI systems are a competitive advantage. They increase customer satisfaction, reduce liability and reputational risks, ensure regulatory compliance and facilitate scaling into new markets.
Those who demonstrably develop fair, secure and sustainable AI build trust and position themselves as reliable partners in an increasingly regulated and competitive environment.