What are the top performance metrics for AI software?

The top performance metrics for AI software include accuracy, precision, recall, F1 score, mean absolute error (MAE), mean squared error (MSE), training and inference time, resource utilization, latency, throughput, robustness, and scalability. These metrics collectively evaluate the effectiveness, efficiency, and reliability of AI models across different tasks and applications.

The traditional performance metrics for AI software typically include accuracy, precision, recall, F1-score, and computational efficiency. These metrics evaluate the model's ability to make correct predictions, minimize errors, and optimize resource utilization. In addition to this traditional metrics, AI software can be assessed based on its adaptability to new scenarios, its ability to explain its decisions transparently, and its capacity to learn continuously and autonomously.

???????? ?????????????????????? ?????????????? ?????? ???? ???????????????? ??? ?? ???????????????? ?????? ?????????? ?? ???????????????? measures the AI's correct predictions. It's straightforward but may not reflect complex scenarios, especially with imbalanced data sets. ?????????? ?????????????? like Mean Squared Error (MSE) and F1-Score offer deeper insights into AI performance, helping identify areas for improvement. ?????????????????? & ???????????? balance the trade-off between capturing relevant results and excluding irrelevant ones, crucial for nuanced AI tasks. #AI #SoftwareEngineering #PerformanceMetrics

Top performance metrics for AI software include accuracy, precision, recall, F1 score, mean absolute error (MAE), mean squared error (MSE), R-squared (R2) score, speed, latency, throughput, resource utilization, robustness, generalization, and human-AI interaction metrics. These metrics vary depending on the task and application, providing insights into the effectiveness, efficiency, and reliability of the AI system.

Patient Engagement Metrics: (examples) Patient satisfaction scores: Measure patient satisfaction with the software's usability, functionality, and overall experience. Patient portal usage: Track how frequently patients use the software's portal to access their health records, schedule appointments, or communicate with healthcare providers. Appointment attendance rates: Monitor the percentage of scheduled appointments that patients attend, which can indicate the effectiveness of appointment reminders or scheduling features in the software

???? ???????????????????? ?????? ?????????????????????? ???? ???? ???????????????? ???? ?? ???????????????????? ?? ?????????????? & ???????????????????? gauge the AI system's speed and data processing capacity, highlighting resource utilization and potential optimizations. ?? ?????????????????????? ?? ?????????????? & ???????????????????? reflect the AI system's ability to adapt to increased demands, crucial for maintaining performance under growth. ?? ???????????????????????? ???????????????????? ??? ???????????? & ?????????? ?????????? metrics assist in identifying efficiencies, ensuring the AI system performs optimally without wasting resources. #AI #Efficiency #Scalability #TechInnovation

For health AI SW - It's also important to link the SW performance with health outcomes (the end effects). Here below are some examples. Health Outcomes Metrics (Patient health outcomes) Monitor key health indicators or clinical outcomes for patients using the software, such as improvements in disease management, medication adherence, or preventive care measures. Readmission rates: Measure the rate at which patients return to the hospital or healthcare facility within a certain time frame after discharge, which can indicate the effectiveness of care coordination and follow-up processes facilitated by the software.

I would also focus on metrics from a user perspective like, User satisfaction, engagement, task completion time to see how well the model is performing at the user end.

????????????????????????? ?????? ???????????????????? ???? ???? ???????????????? ??? ?? ?????????????????????? ?? ???????????????? ???????? ???????? & ???????? reflect the AI's consistent performance and durability, crucial for long-term deployment. ??? ???????????????????? ?? ?????????????????????? ???????????????? & ???????????????????? gauge the AI's ability to maintain performance against unexpected challenges, ensuring system integrity. ?? ?????????????? & ?????????????????? ?? ???????? & ???????????????????? ?????????????????? help identify the AI system's reliability under various conditions, enhancing trust in AI applications. #AI #Reliability #Robustness #MachineLearning

Les KPI "conformité" vont devenir indispensable. Les organisations doivent être capables de mesurer l'efficacité du système par rapport aux réglementations et normes en vigueur, ce qui est crucial pour assurer que le système d'IA fonctionne dans les limites établies. La priorité est de mesurer et piloter la gouvernance de la donnée le plus t?t possible dès le développement du model.

As part of the Fairness and Explainabiity, we need to also establish criteria to evaluate the Model transparency. It would be helpful to examine how easy it is to understand the model's internal workings and logic.

????????????????????? ?????? ???????????????????????????? ???? ???? ???????????????? ????? - ?? ???????????????? ??: ???????????? & ?????????????????? ?????????????? evaluate the AI's impartial treatment of various user groups, highlighting inclusivity in AI development. - ?? ???????????????????????????? ??: ???????????????????????????????? & ?????????????? underscore the AI system's ability to make its decision-making process transparent and understandable. - ?? ?????????????? ???? ??: ???????????????????????????? & ?????????????????? ensure the AI system's actions are justifiable and relevant, fostering trust and reliability in AI solutions. #AI #Fairness #Explainability #EthicalAI

Ensuring that the AI system treats everyone fairly and can explain its decisions clearly is crucial. Fairness checks if the AI system treats all individuals or groups fairly and without bias. Explainability, on the other hand, refers to the system's capacity to offer understandable explanations for its decisions and predictions, particularly in important or sensitive scenarios.

For an organization focused ROI, it is also important to measure the Resource efficiency. Establishing metrics to understand the Inference time, energy consumption and Memory footprint will ensure that the resources are set to achieve their maximum efficiently.

?? ???????????????????? ?????? ?????????? ???? ???? ???????????????? ???? - ?? ???????????????????? ??: ?????????????? & ???????????? measure the AI's ability to introduce new solutions, emphasizing the importance of originality and the generation of valuable insights for progress. - ?? ?????????? ??: ?????? & ???????????? ???????? assess the AI system's benefits to businesses and society, highlighting its effectiveness in delivering tangible outcomes and contributing to social improvements. - ?? ???????????????????????????????? ???? ??: ???????????????????? & ???????????????????? emphasize the AI's role in driving innovation and engagement, showing how AI can revolutionize industries and enhance user experiences.

Safety is another crucial aspect of AI performance, especially for systems where mistakes can have serious consequences. A multifaceted approach that considers several key metrics can provide a more comprehensive assessment of AI safety. Here are some practical metrics to consider: Rate of safety incidents: This metric tracks the number of incidents where the AI system caused unsafe outputs. Mean time to failure (MTTF): measures the average time between occurrences of safety incidents in the AI system. Severity of incidents: Considers the consequences arising from the AI system's failure. It can be categorized by the level of harm caused.

One additional thing that I would like to emphasize here is Security and the metric to check is the Data Poisoning Resistance. We should assess the model's ability to withstand training data manipulation aimed at sabotaging its performance.

When working with physical quantities, the RMSE metric is very useful as it preserves the unit. This is particularly helpful when one has a certain intuition for a specific unit.