Building effective and safe AI systems through governance, testing and monitoring.

Introduction

Building effective AI systems requires cultural competencies and business processes to improve AI performance and prevent real-world failures. The goal is to ensure AI systems not only perform well in testing but also function safely and effectively in deployment, avoiding financial losses and harm.

Legal considerations for AI

A crucial foundation for AI development is understanding legal standards. AI creators, like manufacturers of physical products, are obligated to ensure their systems are safe when used in foreseeable ways. Failure to anticipate AI-related harm can constitute negligence. As a guideline for AI makers, the amount of care (C) should be greater than probability of harm (H) multiplied by its potential loss (L). In simple terms C > H x L. This principle should help balance safety efforts with practical constraints.

Legal considerations extend beyond product liability. The U.S. Federal Trade Commission (FTC) emphasizes fairness, transparency, accountability, and mathematical soundness in AI deployment. Similarly, European Union AI regulations, impose strict documentation and monitoring based on risk levels. These regulations influence U.S. AI practices and reinforce the need for robust AI governance.

Range of AI failures

AI failures cause harm. AI safety and model debugging aim to prevent AI failures. 

These issues range from minor inconveniences to serious consequences, such as self-driving car accidents or algorithmic discrimination in healthcare. AI failures can be ethnical discrimination, bias, cybersecurity threats, data filtration, privacy violations, and denial of availability of a service.

Poor planning and security vulnerabilities can lead to AI failures. These cases highlight the need to study past incidents to prevent future ones.

Competencies for AI development

To mitigate AI risks, organizations need strong accountability and risk management, to ensure AI development with a correct balance between risk and speed. Properly test the models to catch AI system design issues before deployment to uncover flaws in real-world conditions. Address AI safety, with teams dedicated to model validation and incident response. Involve domain experts and diverse perspectives to prevent bias. Beyond technical measures, structured governance help mitigate risks and address potential failures.

Anticipating AI Failure Modes

AI failures are often predictable if organizations systematically document and analyze past failures. Resources like the AI Incident Database and brainstorming techniques help anticipate risks and prevent repeating mistakes. To manage risk it is essential to prioritize resources based on system risk levels, document the AI model, monitor the model, maintaining a centralized record of AI systems, perform independent validation and auditing. Additionally, take benefit from well established software engineering and IT security practices, for example security permissions and change management.

Structured response plans are essential to identify and resolve the incidents, and following that analysing them to prevent reoccurrence.