Introduction
In our previous articles, we have consistently highlighted the potential risks and limitations of machine learning, its implementations, and use cases. We strongly believe that understanding the risks and incidents associated with AI products and models is crucial for their safe and responsible use. As AI becomes more integrated into critical infrastructure, decision-making processes, and everyday life, the potential for harm grows significantly. This harm can manifest as biased outputs, privacy breaches, unexpected failures, and malicious exploitation. Failing to acknowledge and learn from these incidents can have serious consequences, including reputational damage, legal liabilities, and real-world impacts on individuals and society. Transparency regarding AI failures, rigorous incident reporting, and ongoing risk assessment are vital for building trust, mitigating harm, and ensuring that AI benefits humanity safely and equitably.
To further improve our community’s understanding of this issue, today we will focus on the specific risks and real-world incidents related to AI products. This article is neither focused on disencouraging interested persons, businesses or researchers on not continueing their work in this field, but rather to showcase potential dangers from relying only on such systems, not correctly checking or not understaanding its outputs.
Note: This article relies heavily on data provided by ourworldindata. Before we dive into the article itself, we want to highlight their (free and) public world, intending on improving the world.
Data behind Risks and Incidents
In recent years, machine learning and its applications have undisputably made huge leaps, mainly in terms of accuracy and output performance. We can highlight this by giving a few examples:
From these two graphs, we can see that accuracy has been consistently improving, even outperforming human performance in certain areas. There are examples of neuroscientists being less capable of identifiying correct abstracts, OpenAI Five beating human players in the Game of League of Legends, Deepmind AlphaStar beating humans in the game of StaraCraft II and various other examples. In a fairly recent development, it was shown that a neural network could identify nearly 100% of cancer cases, vastly outperforming human doctors in this field. A paper which reviewed thousands of experts from the field of AI also provides an insight on when experts predict that machine learning will outperform humans in their respective fields.
Note: Future projections in this field are subject to inherent limitations and potential biases, influenced by ongoing political, technological, and environmental developments. This paper aims to offer an initial framework for understanding potential future trajectories.
So if the field of artificial is vastly improving, then why are we attempting to talk about risks in this article? I would like to show you a graph, showcasing some of the problems:
Over the years since we have introduced this technologie to the masses, we have also seen increases in how many people aand companies use such technology.
And then, we can also find publically aggregated information on how different systems produce real-world incidents, like realharm or the indicent database. These aggragations aim to make organize incidents by different topics like brand damaging conduct, misinformation and fabrication, bias and discrimination or even crimincal conduct.
There is also the basic issue of ethics and bias in machine learning. This is a topic that we delved into in an earlier article. If youre interested in reading more about it, you can do so here.
For now, we go over the most important factors or issues, that can be used to mitigate real-world issues in the context of machine learning and its applications:
Model Publishers
Model publishers have a range of options available to improve safety for end users. These strategies aim to mitigate risks associated with large language models and ensure responsible deployment.
- Robustness & Adversarial Training: To enhance model resilience, publishers can employ adversarial training. This technique involves exposing models to unexpected inputs, noise, and data designed to mimic deliberate attacks, strengthening their ability to withstand “jailbreaks” 1 2
- Explainable AI (XAI) & Interpretability: In order to provide explainability and interpretability of models and their outputs, we can empower end users to understand what inputs should be avoided in the context of dangerous or malicious outputs. In addition to this, it also builds trust in the model and allows for human oversight alongside human intervention.
Providing explainability and interpretability of models and their outputs empowers end users to understand the factors influencing model behavior and identify potentially problematic inputs. This, in turn, fosters trust and allows for human oversight and intervention. While explainable AI remains a developing field, particularly for complex neural networks, progress is being made. Reasoning models offer a promising avenue for improvement, allowing models to present their chain of thought alongside the final output.
- Formal Verification & Safety Guarantees: Formal verification, a relatively new approach in machine learning safety, uses mathematical methods to identify bugs and ensure models adhere to specified safety constraints. This process aims to provide a higher degree of confidence in model behavior. 3
- Input Validation & Sanitization: To minimize the risk of generating dangerous or malicious outputs, input validation and sanitization techniques are crucial. Input validation checks for and rejects unwanted sequences, while input sanitization removes them before they are processed by the model. 4 5
- Model Monitoring and Drift Detection: Continuous model monitoring and drift detection are essential for maintaining performance and safety. Models require exposure to new data to address problems they haven’s been trained for. However, frequent updates to model parameters can significantly alter how prompts are processed, potentially leading to unexpected or undesirable outputs. 6
Beyond these techniques, leading organizations like OpenAI7 and DeepMind8 have established dedicated safety teams focused on responsible training practices and output restriction. The Hugging Face community is also promoting transparency, encouraging model providers to disclose their safety and risk management procedures, as exemplified by the Gemma3 repository.
Model Users
To ensure AI systems are fair, reliable, and aligned with human values, a multifaceted approach is required throughout the development and deployment lifecycle. The following strategies are critical for mitigating risks and fostering trust.
- Bias Mitigation and Continuous Monitoring: Despite diligent efforts to curate unbiased training datasets, subtle biases can still permeate a model’s decision-making processes. Adressing this issue involves regularly evaluating model outputs across diverse scenarios, not just on standard performance benchmarks. When unexpected or biased outputs are detected — for example discriminatory language, or inaccurate classifications — a systematic process should be initiated. Documenting these interventions and their effectiveness is vital for building a transparent and accountable system.
- Human-in-the-Loop Design & Oversight: Maintaining meaningful human oversight and control is paramount, especially in high-stakes applications. Human-in-the-Loop design goes beyond simple monitoring; it integrates human judgment into the ndeep learning system’s workflow. This can involve establishing approval workflows where critical decisions require human validation, implementing override mechanisms to allow humans to correct AI outputs, and establishing clear escalation paths for resolving complex or ambiguous situations.
- Thorough Testing & Validation Across Diverse Scenarios: Rigorous testing and validation must extend far beyond simple performance metrics to assess the robustness and reliability of deep learning systems under a broad spectrum of conditions. This includes testing on diverse datasets representing a wide range of user demographics, input types, and environmental factors. Particular attention should be paid to identifying and addressing edge cases—unusual or unexpected inputs that can expose vulnerabilities and lead to unexpected or harmful behavior.
- Red Teaming & Adversarial Testing for Vulnerability Identification: Red teaming and adversarial testing involve employing independent teams of experts to deliberately attempt to break or compromise AI systems. These teams, often with backgrounds in cybersecurity or ethical hacking, leverage a variety of techniques — including crafting misleading inputs and exploiting system vulnerabilities — to uncover hidden weaknesses. The findings from red teaming exercises should be carefully documented and used to inform iterative improvements to system design, training data, and security protocols.
- Incident Response Planning for Rapid and Effective Resolution: Despite best efforts, incidents involving machine learning systems can and will occur. Having a well-defined incident response plan is crucial for minimizing harm and swiftly restoring system functionality. This plan should outline clear procedures for identifying, reporting, investigating, and resolving AI-related incidents, including detailed steps for root cause analysis, containment, and corrective action. Post-incident reviews, with a focus on learning and continuous improvement, are also vital.
In addition to the two groups above, neural network safety can also be increased governance efforts, for example with clear accountabilities and responsibilities, regulatory frameworks, ethical guidelines and more. A good example for a governance effort to support machine learning safety is the EU Artificial Intelligence Act.
Overreliance in Machine Learning Systems
In addition to the facts given so far, overreliance on AI systems, while promising efficiency and innovation, carries the potential for increasingly dangerous situations. As we delegate critical decision-making to algorithms, we risk eroding an oversight component and critical thinking skills, leaving us vulnerable to unforeseen consequences. System failures, biases embedded within training data, and susceptibility to malicious manipulation can all lead to flawed outputs and potentially harmful actions. A diminished ability to recognize and correct these errors, coupled with a decreased capacity for independent judgment, creates a precarious dependence that could compromise safety, security, and even ethical considerations across various sectors, from healthcare and transportation to finance and defense. Ultimately, blindly trusting AI without maintaining a robust oversight component and a critical awareness of its limitations is a pathway to significant and potentially irreversible risk. 9
TL;DR
AI safety isn’t just a technical challenge; it’s a societal imperative. As AI becomes increasingly interwoven into our lives, the potential for harm escalates. Developers can mitigate risks by incorporating robustness training, explainable AI principles, formal verification techniques, input validation, and continuous monitoring. Users need to prioritize human oversight, maintain a critical mindset, and actively participate in red teaming and incident response planning.
The risk vs. safety argument isn’t about avoiding AI systems altogether – it’s about embracing it responsibly. With proactive measures, we can harness AI’s transformative potential while safeguarding against its inherent dangers. The future of AI isn’t predetermined; it’s shaped by the choices we make today. By embracing a culture of safety and transparency, we can build an AI-powered future that benefits all of humanity.