While benchmarking is a valuable approach for evaluating the safety and accountability of general AI systems, it comes with certain limitations and drawbacks. One limitation is the potential bias in the selection of benchmarks, which can lead to an incomplete assessment of the system's capabilities. To address this limitation, it is essential to use diverse and representative benchmarks that cover a wide range of tasks and scenarios to ensure a comprehensive evaluation.
Another drawback of benchmarking is the lack of standardization in benchmark datasets and metrics, which can make it challenging to compare results across different evaluations. To overcome this limitation, efforts should be made to establish common standards for benchmarking in AI, ensuring consistency and comparability in evaluation results.
Furthermore, benchmarking may not always capture the ethical implications and societal impacts of AI systems, focusing primarily on technical performance. To address this limitation, the evaluation framework can incorporate additional criteria for assessing the ethical considerations and societal implications of AI systems. This can include evaluating the system's fairness, transparency, and accountability in decision-making processes.