1. Bias in AI systems

Challenge: AI algorithms can inadvertently perpetuate and amplify biases.

Example: Consider a loan-approval model that discriminates based on gender due to biased training data. This not only leads to unfair practices but also damages the credibility and ethical standing of AI technologies.

2. AI errors and safety concerns

Challenge: Errors in AI systems can have serious – even life-threatening – consequences.

Example: An autonomous vehicle experiencing a system failure could result in a collision, raising questions about the reliability and safety of AI-driven vehicles.

3. Data privacy and security

Challenge: AI systems often require access to sensitive data, which must be protected.

Example: A medical diagnostic bot trained on sensitive patient data that is stored insecurely poses a significant privacy risk.

4. Inclusivity and accessibility

Challenge: AI solutions must be designed to cater to diverse needs.

Example: A home automation assistant lacking audio output fails to serve visually impaired users, highlighting the need for inclusive design in AI applications.

5. Transparency and trust

Challenge: Users need to understand and trust AI systems.

Example: An AI-based financial tool that makes opaque investment recommendations can lead to mistrust among users if the decision-making process isn’t transparent

6. Liability and accountability

Challenge: Determining responsibility for AI-driven decisions can be complex.

Example: If facial recognition technology erroneously convicts an innocent person, the question arises: Who is liable – the developers, the users, or the AI itself?

Addressing these challenges requires ethical and responsible development and an AI governance model aligned with the upcoming EU AI Act. The process begins with building an inventory of where AI is used or being prepared for use across your organization. You then need to conduct a risk analysis and categorize AI applications in accordance with the risk categories outlined in the EU AI Act. Regular audits for bias, robust testing for safety and reliability, and stringent data security measures will form the core of the AI governance model. Moreover, inclusive design principles and clear communication – both internally and externally – about how AI systems are being used in your organization can build user trust and ensure wider accessibility. 

AI risks pose a unique challenge to decision makers, such as EU institutions. It’s not easy to address rapidly developing technologies through legislative processes that are inherently slow and inflexible. Indeed, in the coming years, we can expect a constantly changing regulatory framework in Europe and elsewhere, and organizations will need to follow this closely. It’s no longer enough for companies to react to laws once they are finalized – in order to manage risks and make use of new technologies, you need to be agile and start preparing for new regulations well in advance before they are formally adopted.

The road ahead is a collaborative effort

The journey to a responsible AI future is not a solitary one – it demands collaboration and a continuous dialogue between developers, users, ethicists, and policymakers. By understanding and addressing the challenges outlined above, we can responsibly harness AI's potential, steering this powerful tool toward a future that benefits us all.
While a properly structured AI governance model will help your organization manage risks and compliance with rapidly changing laws, it’s first and foremost a proactive approach to ethical, transparent, and trustworthy use of AI.

✍️ This blog was a joint effort between Jonne Sjöholm, Vincit's Data Architect and Petja Piilola, Partner at Blic, a consulting agency specializing in societal analysis and lobbying.