Components of Trustworthy AI: Why Accuracy Matters

In case you missed it, Generative AI is the new norm in the business world. In the first 18 months or so of GenAI’s rise to ubiquity, back-office uses have been the most common. But as the technology has advanced beyond simple content creation help and into more autonomous, external-facing engagement solutions, brand-safe guardrails have become more and more important.

As major buyers of AI solutions, Marketing leaders in particular recognize the importance trustworthy AI.

The quality of experience a prospect or customer encounters with a brand significantly influences their likelihood to convert and remain loyal. Marketers need to be sure the conversational AI platform they choose can deliver personalized, two-way interactions, fostering exceptional experiences while ensuring accuracy and security.

To help Marketers evaluate the solutions on the market, we’ve distilled Trustworthy AI for Marketers into three key components. A Conversational AI solution—especially one that is interacting with your prospects—must demonstrate accuracy, align with the brand identity, and prioritize safety and security. Any solution failing to meet these criteria should be excluded from consideration.

Today, we’re diving into the concept of Accuracy in AI.

Breaking Down Accuracy

For a conversational AI solution to be considered accurate, it needs to not only be able to comprehend the underlying intent of a conversation, but also effectively classify it. This involves deciphering the nuanced meanings, implicit cues, and context within each interaction.

The AI also needs to seamlessly transition towards the most suitable action, based on the overarching goal of the conversation.

According to Salesforce, accuracy ranks as one of the highest concerns marketers have today with AI—and rightfully so. Inaccurate AI has the potential to make headlines. Just look at the Air Canada chatbot mishap, which resulted in substantial legal fees.

Marketing invests heavily in understanding the buyer’s journey and developing campaigns and content that support that journey. And, since buyers have done most of their research before getting to your brand, you must be able to provide them with timely information that is relevant and accurate.

How to Evaluate Accuracy in an AI Solution

If you’re considering a solution that leverages generative AI, make sure you ask these questions.

What types of models are they using? Are they up to date?

You need to know about the underlying models employed by a conversational AI solution to gauge its potential accuracy and effectiveness. A critical consideration is whether the solution relies solely on a single public Large Language Model (LLM)—think just leveraging GPT, for example. While public LLMs are valuable resources, depending solely on them may limit the solution’s capabilities and accuracy.

Solutions that adopt a multi-model approach are a much safer bet for Marketers. Multi-model solutions use a strategic blend of both public and private LLMs, harnessing the strengths of each. Public models offer broad knowledge bases and pre-trained capabilities, while private models can be fine-tuned to specific use cases and tailored to individual needs.

By opting for a multi-model approach, organizations can leverage the collective strengths of diverse AI models, optimizing accuracy, adaptability, and performance in conversational interactions.

How does it factor in error handling?

No AI solution is perfect; the world is too complex for every situation to fit in a neat box. So when the AI encounters a scenario where it struggles to comprehend the user’s intent or determine the next appropriate action, you need to make sure there are robust guardrails in place within your solution.

This is where the difference between a seamless experience and a problematic one becomes obvious.

Proper guardrails to your system ensure that the AI doesn’t resort to arbitrary responses when uncertain. Instead, it should have mechanisms in place to handle such situations responsibly.

One effective approach is to look for a system that flags such conversations for review rather than allowing the AI to make uninformed decisions autonomously.

Moreover, having a human in the loop becomes indispensable to maintaining AI accuracy and user trust. Human oversight ensures that challenging scenarios are addressed appropriately, whether it involves clarifying user intents, guiding the AI’s responses, or intervening when necessary.

In essence, prioritizing solutions with robust guardrails and human oversight mechanisms not only lessens the risk of undesirable outcomes but also fosters a dependable and trustworthy conversational AI experience

How does the tool receive feedback from users? Are there ways to see how the AI made its decision? Are there ways to provide feedback to the tool?

Conversational AI systems, such as LLMs, often operate as black boxes, leaving users in the dark about the rationale behind their decisions.

Auditability in your solution is crucial—meaning you can truly see what decision the AI made for each exchange and also provide feedback if something doesn’t align with your expectations. Since AI systems are in a perpetual state of learning, having control over the decision-making process becomes paramount for ensuring accuracy and relevance.

An even bigger plus is if the solution provider has an internal team auditing conversations on your behalf—combining auditability with proactive auditing support, users can leverage AI technology effectively while maintaining control and trust in its decision-making processes.

Conversica’s Approach to Accuracy

Conversica’s conversational AI platform achieves unparalleled accuracy by harnessing the capabilities of multiple AI models, capitalizing on decades of conversational insights, and embracing a Human-in-the-Loop approach.

This holistic strategy empowers our solutions to not only comprehend the underlying intent of conversations but also effectively classify them by deciphering nuanced meanings, implicit cues, and contextual intricacies. With smooth transitions toward the most suitable actions, our AI ensures alignment with the overarching goals of each conversation.