Siri Co-Founder Adam Cheyer on Data, Bias, and the Future Role of AI

Adam Cheyer—co‑founder of Siri and Viv Labs, former Engineering Director of Apple’s iPhone team, and Chief Architect on SRI’s CALO project—is a leading voice in artificial intelligence and technology innovation. He has shaped products that have impacted millions and continues to advise major tech ventures.

With over 60 peer‑reviewed publications and 25 patents to his name, Cheyer blends technical genius with real‑world ingenuity. Beyond AI, he’s co‑founded Change.org and even performs award‑winning magic on stage—bringing both vision and charisma to each discussion.

In this interview with The Champions Speakers Agency, Cheyer discusses how smart data management, bias mitigation, and safeguarding privacy are cornerstones of building trustworthy AI—offering essential insights for business leaders navigating the next wave of AI-driven transformation.

Q1. Organisations often describe data as the “lifeblood” of artificial intelligence. From your perspective, how critical is data management in enabling AI capabilities and bringing that data to life in a meaningful way?

Adam Cheyer: “AI is literally algorithms plus data, and the algorithms are relatively well known. So data is the lifeblood of any AI system, and having the ability to organise and manage that data is essential for any organisation trying to build AI systems.

“And with the explosion of AI over the last 15 years – but really over the last six months – this is going to become more and more an important and essential capability within every organisation.”

Q2. With large-scale AI deployments, the issue of data privacy becomes unavoidable. What approaches do you take to ensure sensitive information is protected while still enabling powerful AI capabilities?

Adam Cheyer: “Your first tendency is, well, we just record everything. But I think that’s the wrong approach. You really need to be thoughtful about what is saved, especially if it involves personal information, and what is not saved.

“So step one for me is just… you know, it’s only an issue for privacy if it’s stored somewhere. Make sure you’re only storing things that you’re actually going to use and actually need to provide value to your customer. So that, for me, is step one: really analysing the data, what you have and why you need it.

“And that gets into a principle that I’ve used all the way through, which is transparency and control by the user. Where storing data, it shouldn’t just be some terms and conditions somewhere. They should know what is stored and why.

“I’ll give you a very simple example. With Siri, you could say, “Find restaurants near my house.” Well, Siri would come back and say, “Where do you live?” You would say, “Okay, I live here.” And then it would say, “Do you want me to remember that?” And the user could have a decision. If I say yes, it’ll be stored – some privacy risk – but there’s also convenience. Now I can say, “Movies near my house.”

“So being able to have that choice of what gets stored, as much as possible, and then to give them the control that maybe they stored it, but they want to change their mind later. At every point when it’s stored, it’s stored in a way that the user can see, transparent, and also control.

“So that trade-off of value versus privacy, I always try to do that. Obviously best practices around data storage and doing it in a secure way. Having audits – we had a whole team at Apple, separate from the engineering and product teams, whose job was to almost go in as hackers and validate every assumption.

“So that if Apple ever gets pulled into Congress or something, they say, “We are guaranteed sure this data has been secured in the way that it was said it would be done.” So having the organisational aspect.

“For me, those are the ideas: get just the data you need, make it transparent and give user control as much as possible to show the value. Use best practices both on the system side, storage, ETL, but also organisational side – have audit teams and really checking that compliance is being met.”

Q3. Bias remains one of the most pressing concerns in AI development. From your experience, what are the most effective ways to identify, reduce, and manage bias within AI models?

Adam Cheyer: “Algorithms aren’t biased. They’re just kind of pattern-matching, pattern-seeking devices, and they’re looking for patterns, and that’s their goal, their job. They’re looking for useful patterns in data – that’s how machine learning works.

“But the data that you give will have patterns, and some of them are going to be desirable and some of them are not desirable. And society and morality and all of this goes into it. Machines don’t know about any of that. They just will take the data you have.

“So when you are going through this process of data collection – the data that you’re going to feed to an AI to try to discover hopefully useful patterns – there are a number of techniques you need to consider to, as best as possible, ensure that your data sufficiently represents the customer base you’re trying to meet. That’s what it comes down to.

“If the data is not well representative, it’s a problem. So you need to have, first of all, teams who are looking and working explicitly on bias. They know there’s going to be some patterns in there.

“Is it well represented according to our morals, according to our values, according to what we want to be able to give to the customer? If you just take random data and throw it in, it’s not enough. You need to have an active investigation, active tools, by people to do that within your organisation.

“And of course, diversity is often one of the areas that bias is an issue. So have a very diverse team, maybe a team trained and understanding what to look for.

“The other thing is, as best as possible, even if you have an organisation trained to try to reduce any problems of data bias, you may not capture everything.

“So being able to allow and listen to and be responsive to end user feedback – if they raise an issue, to say we hear you, we care, and we’re doing something about it. That will ultimately get you a better solution. You have more eyeballs on it, more perspectives, etc.

“And the last thing, for me, when I deal with AI, there’s bias issues but there’s also safety issues. And the same topics apply, especially with these large language models. There are things you don’t want an AI system to say for safety reasons, not for bias reasons.

“For instance, you don’t want to be able to ask a question, “How can I create a bomb to eradicate the human race?” and have it say, “Here, happy to give you the recipe.” Maybe not the best thing from a safety perspective.

“So the similar techniques you use for bias, I think you also need an organisation using the best tools, best practices, to protect against safety issues as well.”

Q4. Do you believe AI poses more of a threat to human work, or will it become a tool to assist us?

Adam Cheyer: “AI is a tool to help humanity solve big problems, achieve more with our lives. AI does not have the ethics and morals and values that we do, so you need people to teach a machine what… so that when it reflects back, we’ve given it to us, we don’t get offended.”

This exclusive interview with Adam Cheyer was conducted by Mark Matthews of The Motivational Speakers Agency.