Heatmap $1$: Risk Profile per AI Risk $($R$1-$R$33)$

Heatmap $2$: Risk Distribution by AI Lifecycle Phase and Impact

Percentage of Risks per Category:

Planning: $8$ out of $28$ risks $(28\mathrm{.}57\%)$

Data: $11$ out of $28$ risks $(39\mathrm{.}29\%)$

Development: $15$ out of $28$ risks $(53\mathrm{.}57\%)$

Operation: $14$ out of $28$ risks $(50\%)$

Ethical: $12$ out of $28$ risks $(42\mathrm{.}86\%)$

Legal: $10$ out of $28$ risks $(35\mathrm{.}71\%)$

Technical: $4$ out of $28$ risks $(14\mathrm{.}29\%)$

Societal Impact: $8$ out of $28$ risks $(28\mathrm{.}57\%)$

Economic Impact: $2$ out of $28$ risks $(7\mathrm{.}14\%)$

Key Insights:

Development Phase is Most Risk$-$Prone: The development phase emerges as the most critical stage in the AI lifecycle, with the highest number of risks present $(53\mathrm{.}57\%).$ This underscores the need for rigorous testing, validation, and security measures during development to mitigate potential vulnerabilities and biases.

Data Phase is Critical: The data phase is also crucial, as the quality and nature of the data used to train AI models significantly impact their behavior and potential risks. Ensuring data accuracy, fairness, and privacy is essential.

Operation Phase Requires Ongoing Attention: While fewer risks originate in the operation phase, the potential impact of operational risks $($e$.$g$.,$ model subversion, member reasoning attacks$)$ can be significant. Continuous monitoring, security measures, and human oversight are crucial during this phase.

Ethical and Legal Considerations Span Multiple Phases: Ethical and legal risks are present across different phases, emphasizing the need for ethical AI development practices and adherence to legal and regulatory frameworks throughout the entire AI lifecycle.

Technical Risks are Concentrated: Technical risks are primarily concentrated in the development and operation phases, underscoring the importance of robust technical solutions and expertise in mitigating these risks.

Societal and Economic Impacts are Significant: These impacts are not limited to specific phases but can arise throughout the lifecycle, highlighting the need for a broader perspective that considers the wider implications of AI beyond its technical aspects.