Chennai: Have you ever chatted with a friend about buying a certain item and been targeted with an ad for that same item the next day? If so, you may have wondered whether your smartphone was listening to you.
But is it really? Well, it’s no coincidence the item you’d been interested in was the same one you were targeted with, says Dana Rezazadegan, Lecturer, Swinburne University of Technology.
“But that doesn’t mean your device is actually listening to your conversations it doesn’t need to. There’s a good chance you’re already giving it all the information it needs,” says Dana in an article.
“Most of us regularly disclose our information to a wide range of websites and apps. We do this when we grant them certain permissions, or allow cookies to track our online activities.”
According to Dana, so-called first-party cookies allow websites to remember certain details about our interaction with the site. For instance, login cookies let you save your login details so you don’t have to re-enter them each time.
Third-party cookies, however, are created by domains that are external to the site you’re visiting. The third party will often be a marketing company in a partnership with the first-party website or app.
“The latter will host the marketer’s ads and grant it access to data it collects from you (which you will have given it permission to do perhaps by clicking on some innocuous looking popup). As such, the advertiser can build a picture of your life: your routines, wants and needs. These companies constantly seek to gauge the popularity of their products and how this varies based on factors such as a customer’s age, gender, height, weight, job and hobbies.”
Dana says by classifying and clustering this information, advertisers improve their recommendation algorithms, using something called recommender systems to target the right customers with the right ads.
There are several machine-learning techniques in artificial intelligence (AI) that help systems filter and analyse your data, such as data clustering, classification, association and reinforcement learning (RL).
“An RL agent can train itself based on feedback gained from user interactions, akin to how a young child will learn to repeat an action if it leads to a reward. By viewing or pressing like on a social media post, you send a reward signal to an RL agent confirming you’re attracted to the post or perhaps interested in the person who posted it. Either way, a message is sent to the RL agent about your personal interests and preferences.”
Dana adds: “If you start actively liking posts about mindfulness on a social platform, its system will learn to send you advertisements for companies that can offer related products and content.”
In fact, AI algorithms can help marketers take huge pools of data and use them to construct your entire social network, ranking people around you based on how much you care about (interact with) them.
They can then start to target you with ads based on not only your own data, but on data collected from your friends and family members using the same platforms as you.
For example, Facebook might be able to recommend you something your friend recently bought. It didn’t need to listen to a conversation between you and your friend to do this.