Powering the responsible use of AI.
The hype around AI may mask the real concerns about how it’s being built and operated. The debate about throttling AI garners more attention, with the ethics of AI taking a back seat. But, hype or not, AI exists in society and will continue to permeate more aspects of our lives going forward.
At HTC, we understand the additive role of AI and its potential to propagate with adverse consequences. We have, therefore, brought together the finer aspects of Ethical and Usability Principles to create Humane AI, our ethical guide to working with AI. Our solutions follow the Humane AI guide and are tested against 7 fundamental principles.
Our solutions follow the Humane AI guide and are tested against 7 fundamental principles.
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:
Today, AI applications are often used in ways they weren’t designed for, assuming they possess general intelligence and their outputs are unquestionably true. However, recent instances of AI hallucinations and unexpected outcomes due to usage creep highlight the necessity of robust governance for AI engines. This entails thorough monitoring, the capacity to resort to “dumb” processes when AI fails, and the ability to reproduce AI results consistently.
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.
Our solutions follow the Human AI guide and are tested against 7 fundamental principles.