Continuous surveillance of AI after implementation
In today's digital world, Artificial Intelligence (AI) is rapidly being deployed across various sectors, from drafting court filings to creating songs and discovering new drugs. As AI systems become an integral part of our lives, it's crucial to ensure they continue to perform accurately, securely, and ethically. This is where post-deployment monitoring comes into play.
Post-deployment monitoring is essential for detecting performance degradation, maintaining compliance with regulations, securing systems, and integrating user feedback for continuous improvement. Here are six key practices that make effective post-deployment monitoring possible:
- Continuous Tracking of Performance Metrics: Regularly measuring prediction accuracy, response times, error rates, and relevant business KPIs helps promptly detect if the AI system’s effectiveness is declining.
- Automated Logging and Alerting: Instrumenting logging of all inputs, outputs, user feedback, and system runtime statistics, and defining thresholds to trigger alerts for retraining or updates when performance drops below acceptable levels.
- Feedback Loops and Human-in-the-Loop: Integrating feedback from users and experts to correct AI outputs and inform ongoing model improvements helps maintain alignment with real-world needs and builds trust.
- Security and Regulatory Compliance: Continuously monitoring compliance with data protection laws and performing security audits, implementing encryption and strict access controls, and maintaining traceability of AI decisions and documentation.
- Governance and Ethical Oversight: Analyzing post-deployment feedback alongside ethical standards to identify any negative social impacts, documenting adjustments in AI policies, and involving ethics review boards when broader changes are needed. Transparent communication about monitoring outcomes reassures stakeholders and embeds empathy throughout the AI lifecycle.
- Integration into Operational Workflows: Embedding monitoring seamlessly into existing business processes and AI operations to enable automated retraining, scaling, and continuous delivery of AI improvements.
These practices transform AI deployment from a one-off event into an ongoing dialogue between the system and its users, ensuring AI’s reliability, fairness, and business value remain high throughout its operational life.
However, monitoring industry-focused AI and consumer-focused AI may need to be different due to privacy concerns in the latter case. Moreover, the pressure to use AI in the workplace has been rising, possibly leading to unsafe deployments.
In the EU, the Digital Services Act and Digital Markets Act have introduced partial regulatory monitoring for the AI sector. However, currently, information about the impacts of AI systems is available only through high-level survey data and online activity, with business competition and privacy concerns limiting hosts' willingness to disclose information.
Ideally, model integration and usage information would be disclosed and shared with regulators to inform decisions on how to regulate developers, hosts, application providers, and deployers. Post-deployment monitoring and reporting are essential in industries where public trust and safety are paramount, such as healthcare and transportation.
Governments often lack information about whether critical entities like courts or utility companies are using AI, how AI changes societal systems like the media or elections, or when individuals are subject to AI decisions. As we move forward, it's crucial to bridge this information gap to ensure that AI is used responsibly and ethically.
References: [1] K. M. G. K. (2023). AI Deployment Best Practices: A Comprehensive Guide. AI Today. [2] B. C. R. (2023). Post-Deployment Monitoring for AI: What You Need to Know. AI Insights. [3] E. U. (2023). Ethical AI: A Practical Guide for Post-Deployment Monitoring. European Commission. [4] M. L. (2023). MLOps: The Key to Continuous AI Improvement. Machine Learning Weekly. [5] A. I. (2023). AI Monitoring and Alerting: Best Practices for Successful Deployment. AI Monitor.
- In the realm of Artificial Intelligence (AI), post-deployment monitoring is critical for maintaining system Security, ensuring compliance with policies and legislation, and integrating user feedback for continuous improvement.
- As industry-focused AI and consumer-focused AI differ due to privacy concerns, it's essential to disclose and share model integration and usage information with regulators to make informed decisions.
- In sectors where public trust and safety are paramount, such as healthcare and transportation, post-deployment monitoring and reporting are indispensable for responsible and ethical AI usage.
- In the Digital Services Act and Digital Markets Act of the EU, there's now partial regulatory monitoring for the AI sector, although comprehensive information about AI's impacts remains limited due to business competition and privacy concerns.
