Product Development are typically excite about exploring the potential of new AI technologies. However, they’re often less focuse on issues of data privacy. This can lead to ethical blind spots and roadblocks.
One common roadblock is having a blanket privacy statement for certain technologies, such as generative AI. Because generative AI can be the foundation for many use cases, it’s important to address each one differently to understand mobile database what the tech is doing.
For example, if generative
AI creates a summary of a customer interaction, is a human involve in a review of its output? Or is it being use to help evaluate a customer for a loan? Those are very different in terms of customer impact and change, depending on what the generative AI is doing.
Evaluate each AI-driven process individually, base on:
The scope of AI usage: How is AI influencing announce it like you would any business processes and customer interactions?
Human oversight: Does a human review AI-generat outputs, or are decisions fully automate?
Risk tolerance: The ethical and legal implications vary depending on AI applications.
Privacy compliance: How will the company balance evolving regulations while fostering innovation?
Many companies struggle to maintain the necessary transparency throughout product development because they can’t explain how their AI models generate outputs. At Genesys, our privacy office is closely aligne with product teams throughout the full development process.
Creating a Framework to Safeguard Data
Genesys develop a structure framework to safeguard customer and company data while adhering to regulatory requirements. This framework includes safeguarding the data with rigorous information security controls as well as several external bahrain lists audits that are built on privacy. Let’s take a look at the key tenets of this framework.
We encrypt data as it moves between systems. To allow even more control, we let customers bring their own keys if they want to be the only ones with access to transcripts and recordings. For example, for training AI models, we have an opt-in process if customers agree to share their transcripts to help some of our models. And that data is anonymiz before it is use for training the models.
As a global company, we monitor many different industries and countries around the world to be aware of various laws and aim to ensure that we’re meeting all regulatory requirements.
As part of product development, we require annual compliance training that covers types of data and what’s consider personally identifiable information (PII), as well as acceptable uses of customer data. That’s because PII data is more than personal information, like social security numbers or biometric data. It could be how long agents are working and how many agents are on the system.
Natural language systems within AI inherently have bias with content from multiple humans, so we take steps to understand the risk of bias and how it’s created. Annotators from various backgrounds test our models to guard against models making unintend decisions, especially with the potential for system drift over time.
These models are meant to augment, give tips or feedback, for example, not necessarily make decisions in real time. That’s why we start early and monitor continuously.
Trust Define by Transparency
AI is a power you can wield if you’re confident of what it’s made of — what makes it work, and what makes it break. Transparency is a defining feature of the Genesys AI ethics approach.
While some businesses might be hesitant to commit to transparency and reveal to customers how they work with AI, consumers today are becoming more comfortable interacting with it. In fact, in “Customer experience in the age of AI,” 37% of CX leaders survey said their organization proactively communicates how they’re using AI-relatd data.
AI ethics isn’t just a regulatory requirement — it’s a strategic advantage. Organizations that prioritize AI transparency, fairness and accountability can be better position to build trust, drive innovation and maintain compliance. It’s good business and good for your customers.
For more insights, watch our on-demand webinar and Q&A session “Putting ethical AI into practice: Principles and strategies for success.”