This thesis explores the concept of “algorithmic blackboxing” in dating apps and argues that it is essential to eradicate this practice in order to achieve “algorithmic transparency”. The author proposes a framework for mapping the dating app experience, which includes four phases: DISCOVERY, SELF-PRESENTATION, GAMEPLAY-LOOP, and SELF-DISCLOSURE. They argue that dating apps are designed as attention traps that prioritise engagement and serving ads over the proposed end goal of meaningful relationships. To address this problem, the author suggests using hard data to streamline the self-presentation process and improve the quality of matches. They propose using a music streaming service API to match users based on common music preferences, which would help users self-disclose and communicate with like-minded individuals. The author argues that using data for matches would help to eradicate “algorithmic blackboxing” and achieve “algorithmic transparency” in dating apps.
La tesi esplora la presenza di “algorithmic blackboxing” all’interno dell dating app e sostiene sia essenziale estirpare questa pratica da queste ultime per ottenere il concetto di “algorithmic transparency”. L’autore propone un framework con cui mappare l’esperienza utente nelle add di dating, il quale comprende 4 fasi: DISCOVERY, SELF-PRESENTATION, GAMEPLAY-LOOP e SELF-DISCLOSURE. Si sostiene che le dating app siano progettate comn l’intenzione di renderle trappole per l’attenzione dell’utente, dando priorità all’engagement e all’esposizione di questi ultimi a pubblicità invece di offrire mezzi per relazioni sociali significative. Per raggiungere il concetto di “algorithmics transparency” l’autore suggerisce l’utilizzo di hard-data, semplificando il processo di SELF-PRESENTATION e migliorando la qualità percepita dei match su queste piattaforme. Si propone l’utilizzo delle API di una piattaforma di streaming musicale, abbinando gli utenti in base ai propri gusti musicali, aiutando gli utenti nella fase di SELF-DISCLOSURE. Si sostiene che l’utilizzo di hard-data nel contesto della dating app possa eliminare il concetto di “algorithmic blackboxing” e il raggiungimento della “algorithmic trnsparency”.
Eradicating Algorithmic Blackboxing in Dating Apps: The Road to Transparency and Better Connections
Husoschi, Octavian Danut
2021/2022
Abstract
This thesis explores the concept of “algorithmic blackboxing” in dating apps and argues that it is essential to eradicate this practice in order to achieve “algorithmic transparency”. The author proposes a framework for mapping the dating app experience, which includes four phases: DISCOVERY, SELF-PRESENTATION, GAMEPLAY-LOOP, and SELF-DISCLOSURE. They argue that dating apps are designed as attention traps that prioritise engagement and serving ads over the proposed end goal of meaningful relationships. To address this problem, the author suggests using hard data to streamline the self-presentation process and improve the quality of matches. They propose using a music streaming service API to match users based on common music preferences, which would help users self-disclose and communicate with like-minded individuals. The author argues that using data for matches would help to eradicate “algorithmic blackboxing” and achieve “algorithmic transparency” in dating apps.File | Dimensione | Formato | |
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https://hdl.handle.net/10589/211762