How Personal is Machine Learning Personalization?

Machine learning personalization reduces people to "feature vectors" — mathematical representations of data points — rather than recognizing the complexity of human identity, which includes moral values and social relationships that algorithms cannot capture.

Current ML personalization may not reflect who we actually are as persons. The paper raises a critical question: when algorithms make predictions about our choices and behaviors based on past data patterns, do these recommendations actually represent our authentic preferences and values, or do they simply extrapolate from historical patterns that may not define our true selves?

There's a gap between how ML systems define "personalization" and how humanistic perspectives understand the person. While ML treats personalization as statistical accuracy in predicting individual behavior, humanistic views emphasize that being a person involves moral agency, social identity, and the capacity for change — dimensions that algorithms typically ignore.

The paper proposes new evaluation criteria for personalization systems. Rather than measuring success solely through prediction accuracy, the authors suggest we need multiple dimensions to assess whether ML personalization actually respects human dignity and identity, contributing to broader conversations about algorithmic bias, transparency, and fairness.

As human behavior becomes increasingly digitized and analyzed, we should question whether the decisions made about us (and by us through algorithmic recommendations) genuinely reflect our values and autonomy. This is essential for ensuring ML systems support rather than undermine human well-being.