Deploying AI insights requires more than just pushing buttons and hoping for the best.
During the deployment phase, the intersection of technology and ethics becomes crucial. Transitioning AI models from development to real-world applications emphasizes the importance of prioritizing trustworthiness.
It’s not just about algorithms; it’s about understanding how AI affects individuals and societies based on the principles guiding its implementation.
Deploying AI insights marks the fourth step in a series of blog posts outlining the five key steps of the AI life cycle. These steps – questioning, managing data, developing models, deploying insights, and decisioning – emphasize the significance of thoughtful consideration to create an AI ecosystem aligned with ethical and societal expectations.
This phase necessitates more than a purely technical perspective; it requires a thorough exploration of the broader ethical dimensions woven into the AI life cycle.
Transparency is a fundamental concept here – how can we ensure a clear understanding of the processes involved in implementing AI insights and maintain this transparency consistently throughout the deployment life cycle?
Questions like the following five should be posed to facilitate a smooth and secure model deployment:
How do you monitor the AI’s performance metrics, such as accuracy, post-deployment?
When deploying AI insights, it’s crucial not to simply launch and leave. Monitoring key performance indicators like accuracy after the AI model goes live is essential to ensure continued reliability and trustworthiness over time. This practice allows for early detection and correction of biases or drifts, maintaining the effectiveness and trustworthiness of the AI system.
As time passes and conditions change, are you evaluating training data to be still representative of the operational environment?
Regularly reassessing training data for its ongoing representativeness in the current operational environment is crucial as conditions evolve. Ensuring that the training data remains relevant allows the AI system to adapt to changes, maintaining accuracy and fairness. This step is essential for preserving the integrity and effectiveness of the AI deployment.
What actions will you take to ensure your model’s reliability and transparency throughout its life cycle?
Maintaining the reliability and transparency of the model is an ongoing commitment throughout the AI life cycle. Implementing specific steps such as regular updates, thorough documentation, and open feedback channels helps to uphold these critical qualities. Creating a continuous dialogue between the model’s performance and stakeholders ensures consistent performance, understandability, and accountability.
Fig 2: Trustworthy AI life cycle workflow
How will you test and strengthen your model’s defenses against adversarial attacks or manipulations?
Adversarial testing involves simulating attacks and manipulations to identify vulnerabilities and enhance the model’s defenses. This continuous process is essential for maintaining the model’s integrity and preparedness to resist and recover from potential exploitation.
Did you consider a way to roll back the model, if necessary?
Having a rollback strategy in place allows for quickly reverting to a stable version of the AI model in case of unexpected performance issues or unintended consequences. This proactive approach ensures the reliability and integrity of the deployed model, minimizing disruptions and potential harm.
