Toward response-able AI: A decolonial perspective to AI-enabled accounting systems in Africa
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research draws from decolonial, and feminist Science and Technologies Studies approaches to explore the power dynamics of accounting knowledge systems in African contexts. It investigates traditional African indigenous accounting systems, then focuses on the current accounting systems used on the continent and future accounting possibilities presented by AI. We argue that while current accounting systems used in Africa are dominantly Western-centric, AI may reproduce and amplify this structural and systemic power dominance, which has further socio-material consequences on the continent. In trying to mitigate these effects, we propose response-ability in the conceptualization, design, and adoption of AI accounting systems. Fundamentally, we aim to open a discussion for rethinking how these systems can address social issues in alternative worlds and consider alternative and indigenous knowledge systems in African contexts. Toward this end, we seek to open conversations on how accounting AI applications can be designed and adopted in ways that reflect and promote the fundamental principles of objectivity, transparency, accountability, and trustworthiness as embedded locally in African community life and values.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 it