Cultural Challenges And Artificial Intelligence (AI)
Bibliographic record
Abstract
Artificial intelligence (AI) attracts considerable interest in personal and organizational contexts because it can potentially have many applications, from creating personalized content to redesigning business processes and operations. AI could be the second technological revolution following the advent of mobile phones and could even boost the economic growth of a country (Barker, 2023). That is why we are observing rising momentum in launching new AI strategies across Sub-Saharan African countries that have traditionally lagged in AI governance (Oxford Insights, 2024). Many reasons, including human dimensions (Deloitte, 20222) and cultural factors, influence AI's diffusion, adoption, and success. In the literature on the acceptance of information technology (IT), a few researchers have examined the cultural influence on the acceptance of IT, and the conclusions obtained are not consistent (Yamaguchi, 2023; Ahmed et al., 2023). Furthermore, AI differs from traditional technologies given its unique characteristics, which are reflected in intractability and data-driven, illimitable change capability (Zöll, Eitle, & Hendriks, 2024). AI algorithms developed on a database of a specific culture, then applied in a different culture, can lead to cultural incompatibilities due to ethical preferences, and prior cultural studies have led to the identification of these cultural disparities and nuances (Ellenrieder et al., 2023). Indeed, AIs cannot be transferred from one context to another without prior readjustment. There is a gap in our understanding of the cultural influence on the diffusion and acceptance of such an emerging innovation. Research to address this is essential (Ahmed et al., 2023) as there are challenges regarding using and adopting emerging technologies across cultures (Ellenrieder et al., 2023). This study aims to provide a descriptive analysis of the cultural challenges associated with adopting technological innovations in Sub-Saharan Africa, such as artificial intelligence (AI). Specifically, we will focus on the cultural characteristics that may hinder the successful acceptance and use of AI in this region, which is among the least researched areas of the world. This timely study will make significant contributions by identifying the inhibiting and facilitating factors that influence the successful introduction of AI in Africa.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".