Responsible Conversational AI
Conversational AI opens an amazing new channel for companies to interact with their customers. To reach its full potential, conversational bots need to be developed in a way that earns people’s trust.Explore responsible AI principles
The intelligence behind conversational AI comes from its developers who require both technical skills and ethical acumen to ensure their bots interact responsibly.
We have over two decades of research behind our conversational AI platform, which combines the latest in technology advancements with a trusted approach.
A set of guidelines and resources for developers to help address the challenges presented by today’s rapidly increasing capabilities of conversational AI.
Intuitive interactions with AI
Conversational AI is built around a behaviour innate to humans: conversations. When computers can speak ‘human’, every human can speak to computers – making conversational AI the new UI.
Technical details for Responsible Conversational AI
Logic for a bot depends on the use case and its suitability for automation. Clearly defining the purpose and scope for any new bot along with potential limitations can help mitigate risks such as bias during agent interactions. Also, use human feedback loops and reliability metrics to monitor performance.
- Language Understanding service allows applications to understand what a person wants in their own words, applying custom machine-learning to a user’s conversational, natural language text to predict overall intents and extract relevant, detailed information.
- QnA Maker creates a question and answer service from semi-structured content like FAQ (Frequently Asked Questions) documents or URLs and product manuals.
- Project Personality Chat adds user-friendly tones to chats by responding to common small talk in a consistent tone. It is even possible to choose from multiple default personas to match your brand to your bot.
- Project Conversation Learner enables you to build and teach conversational interfaces that learn directly from example interactions.
Understanding verbal requests accurately is a critical component of conversational AI. When applying these technologies, consider creating a code of conduct or applying language and content filters. And, as with any other potential solution you may be designing, design for accessibility.
- Speech to Text converts spoken audio to text with standard or custom models tailored to specific vocabulary or speaking styles of users, while accommodating the expected acoustic environment, such as with background noise.
- Machine translation systems use machine learning to translate large amounts of text to any supported languages. Custom translations can help build neural translation systems that understand the terminology used in specific business and industry.
As speech synthesis becomes more sophisticated, it’s important to reinforce transparency in bot design. Developers must ensure that users know they are interacting with a computer program. There are several design options to encourage this understanding without undermining the user experience.
- Text to Speech services let your application talk back to the user, converting text to audio in near real time with the choice of over 75 default voices. Or create new custom voice models for a unique and recognisable brand voice tuned to specific recordings.
Homomorphic Encryption (HE)
HE technology allows computations to be performed directly on encrypted data. Using state-of-the-art cryptology, you can run machine learning on anonymised datasets without losing context.
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