Modified Self is a project that explores the subjective sense of identity in an algorithmic era.
Our digital behavior is being monitored and captured. This captured data is categorized into assumptions about our identity, and built into an ‘algorithmic profile’. Based on this profile, we receive personalized content.
Being exposed to tailored information has an impact on our subjectivities and sense of self. In fact, there’s a reciprocal relationship between our subjective identities and our algorithmic ones: our behavior shapes our algorithmic self, and at the same time our algorithmic self serves us with personalized content that shapes us in return.

Instead of resisting this powerful feedback loop, can we instead use this apparatus for our own good? ‘Identity Modifier’ is an experimental tool that takes advantage of this mechanism to make desired identity adjustments, giving people the power to train their algorithmic profile in order to make desired changes in their subjectivity.

The process starts when the participants define and maps their identity. They are asked to list as many elements or aspects of their identity as they can and to set their size according to significance. Then, the participants can get to know their algorithmic profile. This observation - taking advantage of digital platforms’ “Ad interests” and personalization features - allows self-observation through another's eyes, while they are “learning” us from our unaware digital behavior. Lastly, the participants can choose their desired identity change through the tools' manipulation on search engines in order to train the algorithms accordingly.
Eventually, the loop is completed: The trained algorithmic profile will serve the participants' customized content, and this content bubble will affect their identity accordingly.