Keywords
algorithms
decision-making
digital literacy
polling
How to Cite
Abstract
Algorithms have become an integral part of everyday life, particularly in entertainment, shopping, and navigation. This study examines how algorithms influence individual decision-making. Data were collected through an online poll involving 200 respondents, selected using a statistical sampling method. The results indicate that 55% of respondents perceive algorithms as having a significant influence on their decisions, while 28% report a moderate impact. A confidence interval analysis (95%) has been included to ensure statistical accuracy. The study highlights the importance of digital literacy in mitigating algorithmic bias and suggests future research on how socio-cultural factors shape algorithmic perceptions. This research contributes to understanding the extent of algorithmic influence on daily decision-making and raises user awareness of technology’s impact. The implications include the importance of digital literacy to mitigate dependency and bias in algorithm usage and the potential to develop more transparent and ethical algorithmic systems. Future research could explore the relationship between users' awareness of algorithms and their behaviors in various contexts and evaluate ways to enhance public understanding of how algorithms function in the evolving digital ecosystem.
References
S. Eliyas and P. Ranjana, "Recommendation Systems: Content-based Filtering vs Collaborative Filtering," in 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 2022: IEEE, pp. 1360-1365.
M. Frey, Netflix Recommends: Algorithms, Film Choice, and the History of Taste. University of California Press, 2021.
N. Seaver, Computing Taste: Algorithms and the Makers of Music Recommendation. University of Chicago Press, 2022.
N. Pajkovic, "Algorithms and Taste-Making: Exposing the Netflix Recommender System's Operational Logics," Convergence, vol. 28, no. 1, pp. 214-235, 2022.
F. Schröter Freitas, "The Impact of Google Maps' Reviews and Algorithms on Young Adults' Choices of Museums to Visit in Prague," 2023.
M. J. Kochenderfer, T. A. Wheeler, and K. H. Wray, Algorithms for Decision Making. MIT press, 2022.
L. Wei and Z. Xia, "Big Data-Driven Personalization in E-Commerce: Algorithms, Privacy Concerns, and Consumer Behavior Implications," International Journal of Applied Machine Learning and Computational Intelligence, vol. 12, no. 4, 2022.
L. Paripati, V. R. Hajari, N. Narukulla, N. Prasad, J. Shah, and A. Agarwal, "AI Algorithms for Personalization: Recommender Systems, Predictive Analytics, and Beyond," Darpan International Research Analysis, vol. 12, no. 2, pp. 51-63, 2024.
G. Figà Talamanca and S. Arfini, "Through the Newsfeed Glass: Rethinking Filter Bubbles and Echo Chambers," Philosophy & Technology, vol. 35, no. 1, p. 20, 2022.
S. C. Rhodes, "Filter Bubbles, Echo Chambers, and Fake News: How Social Media Conditions Individuals to Be Less Critical of Political Misinformation," Political Communication, vol. 39, no. 1, pp. 1-22, 2022.
S. Nikou and M. Aavakare, "An Assessment of the Interplay between Literacy and Digital Technology in Higher Education," Education and Information Technologies, vol. 26, no. 4, pp. 3893-3915, 2021.
S. Nikou, M. De Reuver, and M. Mahboob Kanafi, "Workplace Literacy Skills—How Information and Digital Literacy Affect Adoption of Digital Technology," Journal of Documentation, vol. 78, no. 7, pp. 371-391, 2022.
H. Tinmaz, Y.-T. Lee, M. Fanea-Ivanovici, and H. Baber, "A Systematic Review on Digital Literacy," Smart Learning Environments, vol. 9, no. 1, p. 21, 2022.
D. Cetindamar, B. Abedin, and K. Shirahada, "The Role of Employees in Digital Transformation: a Preliminary Study on How Employees’ Digital Literacy Impacts Use of Digital Technologies," IEEE Transactions on Engineering Management, 2021.
M. Ruckenstein, The Feel of Algorithms. Univ of California Press, 2023.
T. Fouquaert and P. Mechant, "Making Curation Algorithms Apparent: a Case Study of ‘Instawareness’ as a Means to Heighten Awareness and Understanding of Instagram's Algorithm," Information, Communication & Society, vol. 25, no. 12, pp. 1769-1789, 2022.
S. Rahmani, H. Aghalar, S. Jebreili, and A. Goli, "Optimization and Computing Using Intelligent Data-Driven Approaches for Decision-Making," in Optimization and Computing using Intelligent Data-Driven Approaches for Decision-Making: CRC Press, pp. 90-176.
S. Fazelpour and D. Danks, "Algorithmic Bias: Senses, Sources, Solutions," Philosophy Compass, vol. 16, no. 8, p. e12760, 2021.
S. Akter et al., "Algorithmic Bias in Data-Driven Innovation in the Age of AI," vol. 60, ed: Elsevier, 2021, p. 102387.
N. Kordzadeh and M. Ghasemaghaei, "Algorithmic Bias: Review, Synthesis, and Future Research Directions," European Journal of Information Systems, vol. 31, no. 3, pp. 388-409, 2022.
D. Shin, "The Effects of Explainability and Causability on Perception, Trust, and Acceptance: Implications for Explainable AI," International journal of human-computer studies, vol. 146, p. 102551, 2021.
F. Cabiddu, L. Moi, G. Patriotta, and D. G. Allen, "Why do Users Trust Algorithms? A Review and Conceptualization of Initial Trust and Trust Over Time," European management journal, vol. 40, no. 5, pp. 685-706, 2022.
A. Tsamados et al., "The Ethics of Algorithms: Key Problems and Solutions," Ethics, governance, and policies in artificial intelligence, pp. 97-123, 2021.
H. Mahmud, A. N. Islam, S. I. Ahmed, and K. Smolander, "What Influences Algorithmic Decision-Making? A Systematic Literature Review on Algorithm Aversion," Technological Forecasting and Social Change, vol. 175, p. 121390, 2022.
Q. M. Areeb et al., "Filter Bubbles in Recommender Systems: Fact or Fallacy—A Systematic Review," Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 13, no. 6, p. e1512, 2023.
E. Rodilosso, "Filter Bubbles and the Unfeeling: How AI for Social Media Can Foster Extremism and Polarization," Philosophy & Technology, vol. 37, no. 2, p. 71, 2024.
R. Interian, R. G. Marzo, I. Mendoza, and C. C. Ribeiro, "Network Polarization, Filter Bubbles, and Echo Chambers: An Annotated Review of Measures and Reduction Methods," International Transactions in Operational Research, vol. 30, no. 6, pp. 3122-3158, 2023.
E. Perez Vallejos et al., "The Impact of Algorithmic Decision-Making Processes on Young People’s Well-Being," Health Informatics Journal, vol. 27, no. 1, p. 1460458220972750, 2021.
M. H. Jarrahi, G. Newlands, M. K. Lee, C. T. Wolf, E. Kinder, and W. Sutherland, "Algorithmic Management in a Work Context," Big Data & Society, vol. 8, no. 2, p. 20539517211020332, 2021.
D. Shin, K. F. Kee, and E. Y. Shin, "Algorithm Awareness: Why User Awareness is Critical for Personal Privacy in the Adoption of Algorithmic Platforms?," International Journal of Information Management, vol. 65, p. 102494, 2022.
A.-B. Gran, P. Booth, and T. Bucher, "To Be or Not to Be Algorithm Aware: a Question of a New Digital Divide?," Information, Communication & Society, vol. 24, no. 12, pp. 1779-1796, 2021.
C. Starke, J. Baleis, B. Keller, and F. Marcinkowski, "Fairness Perceptions of Algorithmic Decision-Making: A Systematic Review of the Empirical Literature," Big Data & Society, vol. 9, no. 2, p. 20539517221115189, 2022.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.