In this week's Radar Podcast, O’Reilly’s Mac Slocum chats with Sara Watson, a technology critic and writer in residence at Digital Asia Hub. Watson is also a research fellow at the Tow Center for Digital Journalism at Columbia and an affiliate with the Berkman Klein Center for Internet and Society at Harvard. They talk about how to optimize personalized experience for consumers, the role of machine learning in this space, and what will drive the evolution of personalized experiences.
Here are a few highlights:
Accountability across the life cycle of data
One of the things I'm paying a lot of attention to is how the machine learning application of this changes what can and can't be explained about personalization. One of the things I'm really looking for as a consumer is to say, "Okay. Why am I seeing this?" That's really interesting to me. I think more and more we're not going to be able to answer that question. Even so, now I think a lot of times we can only provide one piece of the answer as to why I'm seeing this ad, for example. It's really going to get far more complicated, but at the same time, I think there's going to be a lot more need for accountability across that life cycle of data, whether we're talking about following data between the data brokers and the browser history, and my kind of preference model as a consumer. There's got to at least be a little bit more accountability across that pattern. It's obviously going to be a very complicated thing to solve.
...Honestly, I think accountability is going to be demand oriented, whether that is from a policy side or a consumer side. People have started to understand there is something happening in the news feed. It's not just a purely objective timeline. It's not linear. Just that level of knowledge has changed the discussion. That's why we're talking about the objectivity of Facebook's news feed and whether or not you're seeing political news on one side or the other, or the trending topics. Being part of the larger discussion, even if that's not reaching a huge range of consumers, is making consumers more educated toward caring about these things.
Empowering the consumer
The ideal is not far off. It's just that in practice we're not there yet. I think a lot of people would probably agree that ideal personalization is about relevancy. It's about being meaningful to the consumer and providing something that's valuable. I also think it has to do with being empowering, so not just pushing something onto the consumer, like we know what's best for you or we're anticipating your needs, but really giving them the opportunity to explore what they need and make choices in a smart way.
Shaping the conversation
One of the things we talk about on the data side of things is 'targeting' people. Think about that word. It's like targeting? Putting a gun to a consumer's head? When you think about it that way, it's like, okay, yeah, this is a one-way conversation. This is not really giving any agency to the person who is part of that conversation. I'm really interested in trying to open up that dialog in a way that's beneficial to all parties involved.
...I think a lot about the language that we use to talk about this stuff. I've written about the metaphors we use to talk about data—with metaphor examples in talking about data lakes, and data's the new oil, and all these kinds of industrial-heavy analogies that really put the focus on the people with the power and the technology and the industry side of things, without necessarily supporting the human side of things. ...It shapes whatever it is you think you're doing, either as a marketer or as the platform that's making those opportunities possible. It's not very sensitive to the subject, really.