How To Trust AI: Ensuring Trustworthiness and Security in the Age of Generative AI
Artificial Intelligence (AI) has rapidly become an integral part of our daily lives, earning our trust with its ability to correct our spelling and provide accurate search results. As the new generation of generative AI tools, like ChatGPT, continue to advance, it is crucial that we exercise a healthy dose of skepticism. In a recent Forbes article, Lou Senko, Chief Availability Officer (CAO) at Q2, highlights the need for safeguards amidst the increasing sophistication and ubiquity of AI.
The potential dangers of data poisoning with generative AI cannot be overlooked. Training data can unintentionally or intentionally be corrupted, posing significant risks. While the ease of use and seemingly accurate answers of AI tools can make us complacent, it is essential to address issues such as bias, drift, and corruption. The consequences of overlooking these issues can range from predictive phrasing mishaps in emails to more severe problems in critical areas like drug trials and GPS tools.
The challenge lies in finding a balance between allowing AI models to evolve through adaptive learning and avoiding the wrong lessons. Even a logically sound and accurate AI model can inadvertently perpetuate bias learned from training data and human interaction, leading to harmful outcomes. To address this, we must develop strategies that mitigate bias and drift, both accidental and deliberate, and build trustworthy and secure AI models.
Two emerging approaches are gaining traction in the quest for trust in AI: algorithmic fairness and zero-knowledge machine learning (ZKML). Algorithmic fairness focuses on understanding and correcting biases that unintentionally discriminate against certain groups. The National Institute of Standards and Technology (NIST) is taking steps to establish trust through their NIST Trustworthy & Responsible Artificial Intelligence Resource Center (AIRC), which provides a standard for identifying and managing bias in AI from a socio-technical perspective.
On the other hand, ZKML, rooted in zero-knowledge proof (ZKP), allows verification of results while maintaining data privacy and security. ZKML has the potential to revolutionize machine learning models by enabling training on private data without revealing it to creators or users. It also verifies the use of specific datasets for predictions without divulging details of the data or model used, and ensures that a model remains unaltered.
Ensuring the reliability of AI models requires more than just combating data poisoning; it also involves anchoring the AI tool in an internal knowledge base. Establishing customer-facing copilot platforms that combine large language models (LLMs) with proprietary knowledge models can prevent data drifting or poisoning, as the answers provided are drawn from internal knowledge usage.
As AI continues to evolve and be integrated into various functions, guaranteeing the delivery of untainted data is paramount. Carefully avoiding biases and errors, intentional or otherwise, ensures that users can make informed decisions based on reliable information. Trustworthiness and security should be prioritized as we navigate the ever-expanding AI landscape.
In conclusion, as AI becomes increasingly ingrained in our lives, it is essential to approach it with healthy skepticism. Safeguards against data poisoning, algorithmic fairness, and zero-knowledge machine learning are crucial aspects of building trustworthy and secure AI models. By taking these steps, we can ensure that AI remains a valuable tool while minimizing the risks associated with bias and corrupted data.
