As AI assistants become increasingly common in our daily lives, protecting user privacy and ensuring safe interactions have become top priorities for technology companies. In a move to strengthen security for its AI chatbots, Microsoft has unveiled new “prompt policing” tools on its Azure platform. These neural network nannies aim to shield conversations from manipulation while keeping users out of harm’s way.
Microsoft chief product officer Sarah Bird provided insights into the motivations behind these new safety features. She explained that as AI systems become more advanced, so too must the techniques for testing and monitoring them. “Customers rightfully expect their data and interactions with our services to remain private and beneficial,” said Bird. “These tools help fulfill our responsibility of rigorous oversight without compromising the user experience.”
Rather than a one-size-fits-all approach, Microsoft designed the features with personalization and transparency in mind. Bird acknowledged that not all users have expertise in techniques like prompt injection attacks, so the tools simulate potential issues to provide vulnerability scores and outcomes. At the same time, options are given to customize filtering according to individual needs.
This balanced strategy seeks to avoid past controversies where AI systems generated unrealistic or inappropriate responses. From deepfakes of public figures to images depicting historical inaccuracies, generative models have faced backlash over unintended outputs. As AI continues advancing, proactive risk management becomes ever more paramount.
So how exactly do these new “prompt policing” tools work their magic under the hood? The monitoring system first evaluates any inputs, whether directly from users or external data, to detect prohibited language or concealed commands. Dubbed “Prompt Shields”, these defenses block documents aiming to instruct models against their training specifications.
Once inputs are deemed safe, the response is analyzed for instances of “hallucinated” information through “Groundedness Detection”. This identifies details not actually present in the original prompt but rather imagined by the model. Such unrealistic replies can undermine trust if left unaddressed. The tools effectively form an AI interrogation room to weed out undesirable directives or fabricated responses.
In addition, “Safety Evaluations” provide stress tests to pinpoint model vulnerabilities. According to Bird, these are automatically integrated with Microsoft’s GPT-4 and other popular neural networks. However, less widely-used open-source systems may require manual configuration. The goal is ensuring all hosted models benefit from the protective prowess of these prompt policing packages.
Two more features are also in development. One tracks prompts to identify potentially problematic users attempting to trigger unsafe outputs. Such functionality helps system administrators differentiate experimenters from exploiters. The other aims to guide models towards secure responses through personalized control settings.
While some concerns exist around companies dictating appropriate AI behavior, Bird emphasized customizability as a key focus. Filters for hate speech or graphic content, for example, can be enabled or disabled according to individual circumstances. This grants users agency over how models moderate interactions, balancing safety with expression.
For Microsoft, these prompt precautions represent an ongoing commitment to responsible innovation. By staying vigilant against manipulation and hallucination, conversations can flow freely without fear of unintended harms or privacy breaches. With neural network nannies now standing guard, the future of friendly and forthright feedback with AI assistants looks bright. As technology progresses, proactive protection ensures users and their data remain firmly in control of any prompted world.
