Investigating Awareness and Perception of Bias in AI Driven Platforms: A Survey-Based Study

International Journal of Humanities and Social Science

© 2025 by SSRG - IJHSS Journal

Volume 12 Issue 5

Year of Publication : 2025

Authors : Aishaani Agarwal

10.14445/23942703/IJHSS-V12I5P110

How to Cite?

Aishaani Agarwal, "Investigating Awareness and Perception of Bias in AI Driven Platforms: A Survey-Based Study," SSRG International Journal of Humanities and Social Science, vol. 12,  no. 5, pp. 97-113, 2025. Crossref, https://doi.org/10.14445/23942703/IJHSS-V12I5P110

Abstract:

Artificial Intelligence (AI) increasingly influences decisions in everyday life, from education/health care/and homework to employment/social media. While AI offers multiple advantages of efficiency and innovation, anxiety about fairness, accountability, and transparency in AI systems remains at the forefront of conversation both in the news and public debate. Researchers have raised these issues, pointing to the fact that bias can be introduced at multiple levels in AI—from the training data the AI is trained on to the design choices the AI developer made to the way the user interacts with the AI decision-making system. Researchers have posited important questions related to how AI systems can affect society and people's responses to these AI systems. As such, this study examines how participants perceive and experience bias in AI systems (i.e., decision-making systems) with a focus on trust, accountability, and demographic characteristics such as age, gender, and education. Based on survey data from the youth participants, this research examines where participants perceive bias in AI systems, how participants feel about AI systems making day-to-day decisions in their daily lives, and who participants think should be liable when there is bias in AI systems. The research not only contributes to the ongoing dialogue about fairness and trust in AI but also has implications for design, governance, and public engagement.

Keywords:

Accountability, AI bias, Artificial Intelligence, Human-Computer Interaction, Perception Bias.

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