The Effects of Explainability and User Control on Algorithmic Transparency: The Moderating Role of Algorithmic...

Area of Science:

• Human-Computer Interaction
• Digital Platform Studies
• Algorithmic Studies

Background:

• Algorithms significantly influence digital platform user experiences, raising concerns about opacity and negative impacts.
• Platform transparency features like recommendation explanations and user controls are emerging responses.
• Limited empirical research exists on the effectiveness of these features and their interaction with user characteristics.

Purpose of the Study:

• To investigate the impact of algorithmic explainability and user control on user perceptions of algorithmic transparency, legitimacy, and platform satisfaction.
• To examine how users' algorithmic literacy moderates the effects of these transparency mechanisms on short-form video platforms.

Main Methods:

• A 2x2x2 between-subjects experiment involving 240 participants on a simulated short-form video platform.
• Independent variables: explainability (present/absent), user control (present/absent), and algorithmic literacy (high/low).
• Dependent variables: perceived algorithmic transparency, legitimacy, and platform satisfaction.

Main Results:

• A significant three-way interaction was found across all dependent variables.
• Both explainability and user control positively influenced transparency, legitimacy, and satisfaction.
• Algorithmic literacy significantly impacted outcomes only when at least one transparency feature was present; it had no effect when neither was present.

Conclusions:

• Algorithmic literacy plays a critical role in moderating the effectiveness of platform transparency mechanisms.
• Transparency features enhance user perceptions, but their benefits are unequally distributed based on users' literacy levels.
• Findings suggest algorithmic literacy creates a new digital divide, impacting how users experience platform transparency and potentially informing platform design and policy.