Violation of the right to personal information, both for an individual or a corporate entity, is a growing concern. With data being consumed by AI engines at a rapid pace and consumers being co-opted to give away their right to data with lengthy user agreements, data misuse is possible. Ensuring that an LLM creating content, code, visuals, or designs is not violating private data is important. Here’s how we ensure privacy compliance:

Effective IT platforms have traditionally incorporated control systems capable of auditing and tracing performance issues or failures. The same principle applies to AI systems. However, AI is frequently developed within black boxes, where failures are considered impenetrable glitches, rendering them unfixable.

Tech security is an ever-evolving battleground, and AI systems introduce heightened risks. These risks extend beyond data breaches to encompass data-driven decisions that can have tangible consequences. Consequently, security measures for AI must undergo extra scrutiny during implementation and necessitate ongoing attention and updates to address the ever-shifting threat landscape.

Accountability is a fundamental need in IT systems built on established principles and best practices. However, AI poses unique challenges due to the complexity of data and the opacity of underlying mathematical algorithms. When AI systems deviate from their governing parameters, linking their performance (not just technical performance) to human control structures within organizations becomes a distinct challenge for AI projects.

AI is often credited with intelligence surpassing human decision-making capabilities. While it is true that AI systems can evaluate parameters and data beyond human capacity or understanding, these inputs frequently stem from historical human networks. Consequently, our biases become embedded in the AI systems we create. As AI systems are put into operation, they can veer further away from their initially well-designed intentions. Therefore, removing bias is a crucial aspect of developing ethical AI systems.

AI systems inherently possess a dense and extensive nature. Evaluating their effectiveness primarily relies on observing outcomes. However, achieving transparency can be challenging, especially when these systems deviate significantly from established ranges. Even within those parameters, subtle drifts can occur, making detection and transparency difficult. Therefore, it is crucial to incorporate explainable capabilities into AI systems to address this requirement effectively.