Blog

Making It Count: Why Better Testing of Machine Learning Models Matters

^{13. Mai 2025}

We're AZAV-certified!

^{19. Dezember 2024}

Machine learning models are being implemented on a large scale and are at risk of vulnerabilities if they are not adequately tested.

In today's digital landscape, where Machine Learning (ML) and neural networks (NN) are increasingly being integrated into business operations, the necessity for responsible testing and implementation of these technologies has never been more pressing. The often relaxed attitude towards exploring new technologies, marked by a sense of trial and error, carries inherent risks that cannot be ignored.

Advancements in computing power and the availability of vast data sets have lead to breakthroughs in image and speech recognition that occasionally surpass human capabilities. This progress has sparked our curiosity about conversational AI and natural language processing, as exemplified by models like GPT-3. The promise of transforming nearly every industry has led about 70 percent of organizations to experiment with AI and ML, often without adequate safety measures in place.

The risks associated with such experimental approaches are particularly critical when considering the deployment of ML systems in critical infrastructures like finance, healthcare, and transportation. Unlike traditional software, ML models learn from data and operate on probabilistic assumptions, making them susceptible to specific vulnerabilities. The urgent need for robust testing frameworks is becoming increasingly evident. 

The Shortcomings of Current Testing Models

Traditional testing methods rely on assumptions such as deterministic behavior and predictability, which do not apply to dynamic, data-driven systems like ML models. Rather than adhering to a predefined set of instructions that yield predictable outputs, ML systems work stochastically and respond to patterns, making it challenging to anticipate their reactions and define what constitutes a failure. Consequently, the conventional pass/fail testing model is inadequate for these systems.

Moreover, ML models often lack transparency. They frequently operate as “black boxes,” obscuring their decision-making processes from developers. This complexity makes it nearly impossible to understand why a model made a particular decision and whether that decision can be trusted.

Additionally, ML models can degrade over time as the data they analyze evolves. Even well-performing models may become less accurate if the data they rely on becomes difficult to interpret. Traditional testing methods fail to account for this degradation and often overlook the potential for data manipulation.

The Vulnerability to Manipulation

Like all software, ML models are susceptible to attacks. Adversarial attacks can introduce malicious examples into datasets, steering models toward undesirable behavior. This can lead to discriminatory or unethical decisions, such as biased hiring algorithms.

A particularly concerning scenario involves a self-driving car misinterpreting a stop sign. Such manipulations can have far-reaching negative consequences for organizations, especially those operating in sensitive sectors such as healthcare and automotive.

The Path Toward New Testing Standards

To ensure the quality and security of ML systems, new testing standards must be established. In collaboration with ETSI, Fraunhofer FOKUS has identified several testing methods that deviate from traditional testing approaches and are applicable to ML systems. These are now published in  ETSI TR 103910 and include:

While ML systems hold great promise, the consequences of neglecting proper testing can be severe—from biased hiring algorithms to dangerous errors in autonomous vehicles. We cannot rely on outdated testing methods that are ill-suited for these evolving technologies. By promoting standardized, agile, and context-aware strategies, we can ensure that ML systems remain secure and effective for their intended use cases—today and in the years to come.

In this evolving landscape, the importance of training in this field cannot be overstated; equipping professionals with the necessary skills and knowledge is essential to harness the true potential of machine learning responsibly and effectively.

For excample our training:

• Development of LLM-based Applications and Workflows
• Mastering Large Language Models: Seizing Opportunities, Managing Risks

Our educational institution is AZAV certified, which means we meet the highest quality standards in vocational training and retraining. The Accreditation and Licensing Regulation for Employment Promotion (AZAV) ensures that our programs and measures comply with the strict requirements of the industry.

Through AZAV certification, we guarantee you:

• Quality Assurance: Our offerings are regularly evaluated and meet established quality standards.
• Professional Training: We provide tailored programs designed to meet your needs.
• Transparency: Our certification allows you to rely on the seriousness and professionalism of our educational offerings.

Trust in our AZAV certification and benefit from top-notch training!