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Matilde Alves
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12 August 2024
Maximizing AI Value by Managing Risks
It’s easy to get lost in the hype surrounding AI. Some view it as a miracle solution, while others see it as a looming threat. At Deeploy, we adopt a more balanced view. We recognize AI’s potential to improve efficiency, enhance decision-making, and predict trends. However, we believe the key to unlocking this potential lies in effectively managing the associated risks. This risk-based approach is also aligned with the EU AI Act and other upcoming regulations, which aim to ensure the safe, ethical, and transparent use of AI.
For companies to fully realize AI’s potential, it’s crucial to integrate AI risk management into their strategy. Join us as we look into key considerations and tools for achieving valuable AI outcomes.
The gap between the expectations and realities of AI
Although the idea of reaping the benefits of AI solutions might seem straightforward, the reality is more complex. Around 90% of AI models developed never make it into production.
Gathering and cleaning the data, and training a model requires multiple steps, and throughout these steps, development and management teams need to consider a multitude of risks. Deeploy helps mitigate these risks and ensures that the efforts put into data preparation and model development lead to value in production.
What Makes AI Risky?
Any technology requires managing different risks to ensure safe and effective deployment. But what makes AI a special case? Here are some of the most important factors:
Misspecification:
AI systems need to stay “fresh”: Unlike traditional deductive methods, AI operates inductively, making errors an inherent part of the machine learning process. This means that errors can lead to unintended consequences, but also that AI models can become outdated or “stale” over time, requiring ongoing monitoring and updates to ensure their continued effectiveness.
AI rely on data: AI solutions are only as good as the data they were trained on. While data-driven approaches enable complex problem-solving and adaptability, they also introduce the potential for biases, incompleteness, errors, and manipulation within the AI ecosystem.
AI solutions are novel and complex: The intricacies of implementing these systems can lead to mistakes, whether through underreliance, overreliance, or misinterpretation of AI behaviors. AI’s complexity also often means they are opaque, making it difficult to understand what’s happening. Clear responsibility and transparency are essential for trust and safety.
What risks need to be managed to fully leverage AI?
The above factors introduce different risks that need to be managed in all phases of the AI lifecycle. Here are some of the most important ones to try to address in any risk management efforts for AI systems:
Misspecification:
Legal risks: Compliance with regulations is a risk that needs to be managed when implementing AI systems. In Europe, for instance, the EU AI Act requires high-risk AI systems to adhere to stringent standards for transparency, accountability, and risk management to ensure safety and fairness.
Transparency risks: The lack of transparency of AI systems makes it challenging to maintain proper oversight and control. This can hinder efforts to ensure accountability and make it difficult for users to trust and validate the AI’s outputs. Transparency is needed at various levels: public transparency involves communication about where AI systems are used; operational transparency involves communication and documentation about how the AI system was developed and its current status; and algorithmic transparency involves applying explainable AI (XAI) techniques to clarify how the AI system works.
Bias and ethical risks: AI systems can repeat or increase existing biases present in the training data, leading to unfair or discriminatory outcomes. These risks need to be managed by ensuring the data is thoroughly tested for existing biases, ensuring proper transparency in the AI system to identify potential sources of bias, and involving many stakeholders in the development of AI systems.
Security risks: AI systems can be vulnerable to security threats, including adversarial attacks, where malicious inputs are designed to deceive the AI, and availability attacks, where an AI system is flooded with requests in an attempt to shut it down temporarily. Additionally, the use of AI can introduce new security challenges, such as ensuring the integrity of the AI system and protecting against data breaches.
Addressing these risks requires a combination of organizational policies, frameworks, and procedures, as well as technical controls to mitigate the effects of unwanted AI behavior.
Certain tools, such as Deeploy, facilitate these processes and allow organizations to stay on top of their AI systems while minimizing potential risks to stakeholders and society at large.
Deeploy for AI risk management in the operational phase
Deeploy helps organizations move from model development to model deployment by providing a scalable solution for AI operationalization that easily integrates with an organization’s existing technical ecosystem.
By implementing risk management and governance features directly into the AI system’s operational environment, Deeploy provides a centralized tool accessible to all key stakeholders in the AI lifecycle, from data scientists to compliance officers. This helps ensure that AI systems deliver the value promised from the initial concept.
Key features, such as an AI registry, tools for explainable AI (XAI), simplified onboarding for existing models, performance monitoring, human feedback, and alerting systems, make it simple for teams to operationalize AI models and manage their risks effectively.
By directly addressing AI-related risks—such as legal issues, transparency, bias, and security—Deeploy empowers organizations to maintain full control over their AI models through a single, centralized platform, ensuring that the value of AI is effectively realized.
About the author
Anna Dollbo
Research & Implementation Engineer
Anna is a Research & Implementation Engineer, leveraging her skills in human-centered machine learning to help customers deploy AI responsibly and in compliance with EU regulations.
