User Perceptions of Algorithmic Decisions in the Personalized AI System:Perceptual Evaluation of Fairness, Accountability, Transparency, and Explainability

By Donghee Shin. Journal of Broadcasting & Electronic Media, 2020

345 citations recorded by the source corpus.

ABSTRACT With the growing presence of algorithms and their far-reaching effects, artificial intelligence (AI) will be mainstream trends any time soon. Despite this surging popularity, little is known about the processes through which people perceive and make a sense of trust through algorithmic characteristics in a personalized algorithm system. This study examines the extent to which trust can be linked to how perceptions of automated personalization by AI and the processes of such perceptions influence user heuristic and systematic processes. It examines how fair, accountable, transparent, and interpretable people perceive the use of algorithmic recommendations by digital platforms. When users perceive that the algorithm is fairer, more accountable, transparent, and explainable, they see it as more trustworthy and useful. This demonstrates that trust is of particular value to users and further implies the heuristic roles of algorithmic characteristics in terms of their underlying links to trust and subsequent attitudes toward algorithmic decisions. The processes offer a useful perspective on the conceptualization of AI experience and interaction. User cognitive processes identified provide solid foundations for algorithm design and development and a stronger basis for the design of sensemaking AI services.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Safety Research

View Paper: https://www.semanticscholar.org/paper/da9cc5a14f5ce517fd11d4e291a61e069be81870

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Indro Spinelli
2. Amir Hussain
3. Mufti Mahmud
4. Vikas Hassija
5. Simone Scardapane
6. Abhinandan Singal
7. Atmesh Mahapatra
8. D. Goel

In Cognitive Neuroscience

1. Amir Hussain
2. Mufti Mahmud
3. Simone Scardapane

In Safety Research

1. Amir Hussain
2. Kaizhu Huang

Citations in the corpus

• Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2023, background): “Consumer technologies have undergone extensive research and are constructed using a number of predetermined principles, including justice, accountability, and transparency [179].”
• Bisociative Pivoting as a Method for Moderating User Response to Text Similarity in Next-item News Recommendation (2026, unknown): “That semantic distance significantly reduced responses across all measured outcomes—evaluation, engagement behavior, and adoption attitude— indicates its impact extends across multiple psychological processes relevant to recommender system perception [25] and trust [29].”