Feminism Quote by Michael DiBaggio
““For example, one programmer argued vociferously for the inclusion of a question to female users: "How expensive is your perfume?" The answer would factor into the algorithm by increasing a female user's "femininity" variable if she purchased expensive perfume. Generally speaking, the higher the "femininity" variable, the better the male dates the woman would end up with. DateEx users quickly realized that fact, and the word spread through online forums: start buying more expensive perfume (or lying about it, which would still create the expectation among the general population that perfume was important). The programmer who came up with that question now works for L'Oreal. But,””
About This Quote
The quote illustrates how data collection can embed gender stereotypes into algorithmic scoring, turning personal consumption into a manipulable metric that reinforces biased dating outcomes.
In simple terms: Algorithms can amplify gender bias through engineered variables.
Key Takeaway
Beware of hidden bias in data-driven scoring.
Themes
bias
gender
algorithmic design
social engineering
data ethics
Mood
ironic
critical
cautious
Type
satire
cautionary example
When to use this quote
- online dating platforms
- marketing research
- product recommendation systems
- social media influence
Key Concepts
femininity metric
dating algorithms
consumer behavior
feedback loops
Practical Applications
- audit and redesign scoring models
- implement bias mitigation policies
Questions to Reflect On
- How can we detect hidden bias in user‑generated data?
- What safeguards can prevent exploitation of personal preferences?
A Different Perspective
While the example is satirical, real systems can unintentionally encode similar biases, but not all data-driven decisions are inherently discriminatory.
