AI-Powered Digital Identity Systems and the New Digital Divide: The Case of World
Bibliographic record
Abstract
Artificial intelligence (AI) continues to feature prominently in global discourses on sustainable development as a potential solution to long-standing social and economic issues across the global South. In recent years, there has been increased interest by public and private actors to develop and deploy AI-powered digital solutions positioned to help close the digital divide—a phenomenon that has been traditionally framed as the gap between the connected and unconnected. Framed against a backdrop of “tech for good,” developments in AI and other emerging technologies have led to new challenges, including algorithmic awareness, a new dimension of the digital divide that attends to data and data-related inequalities. This colloquium paper uses World, a digital identity project co-founded by American tech company Open AI that combines AI, biometrics and blockchain-based technologies, as a case study to explore the ethical implications of private sector-led digital initiatives in the global South. Despite claims that digital identity projects help promote social and economic inclusion, we show that projects such as World can intensity existing inequalities through data extraction methods. We argue that the company's activities in countries such as Kenya are possible because of the digital divide and gaps in regulatory frameworks on AI in the global South.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".