Artificial intelligence and algorithmic decision-making systems have become an integral part of our daily lives, impacting various aspects, from e-commerce and entertainment to medical diagnosis and financial domains. While these algorithms offer efficiency and scalability, users often struggle to trust these systems due to the „black box paradox.“ Users increasingly demand transparency in algorithmic decision-making to understand how these systems arrive at their decisions.
The concept of algorithmic transparency has gained significant attention, with scholars exploring various factors influencing users‘ trust and acceptance of AI systems. However, the impact of unethical data collection practices on algorithmic transparency remains less explored. In this research, Muhammad Umair Shah, Umair Rehman, Bidhan Parmar, and Inara Ismail aim to understand the relative impact of algorithmic transparency and ethical data collection practices on user acceptance, comfort, and trust in algorithms.
To investigate this, the researchers conducted two pilot studies to identify real users‘ perspectives on ethical and unethical data collection practices and high and low transparency in algorithms. Armed with these findings, they designed a 2×2 study to examine how transparency and the acceptability of data collection practices influence users‘ trust, comfort, and acceptance of algorithms.
The researchers chose streaming service applications as their testing ground, as these platforms are familiar to most users and involve complex algorithms to personalize user experiences. Participants‘ reactions to different data collection practices and levels of transparency in algorithmic design were analyzed. The results revealed that transparency matters more when data collection practices are acceptable. However, when developers violate moral norms with unacceptable data collection practices, transparency’s impact diminishes significantly.
Streaming services like YouTube, Netflix, and Spotify are rapidly growing in popularity, relying on user information and algorithms for personalized recommendations. The study’s findings shed light on the importance of algorithmic transparency and ethical data collection practices in building user trust and acceptance. Understanding these factors can help developers and firms allocate resources effectively to build user trust and satisfaction.
In conclusion, this research highlights the need to balance algorithmic transparency with ethical data collection practices. Developers and practitioners must consider both aspects to ensure user trust and satisfaction with AI systems. By understanding users‘ perspectives and preferences, technology ethics can be advanced with practical implications for creating more ethical technologies.
Find the article here.
