Bibliographic record
Abstract
UNESCO recently published a report on the ethics of Artificial Intelligence (AI).Its member states have commissioned a recommendation to be written on the ethics of AI, to be adopted in November 2021 during the UNESCO General Conference.The consultations are ongoing, and were preceded by the report of the UNESCO World Commission on the ethics of scientific knowledge and technology (COMEST) on AI and ethics.Africa has had little part so far in designing the new algorithms for AI or drawing up ethical guidelines for its application.The companies and researchers involved are mainly in the West or China and ethical guidelines have been issued mainly in North America, Canada, the EU, Council of Europe and OECD.We have already seen that AI can lead to biases, as machine learning is based on collecting examples of the past.It is often better suited for men than women and also may have biases against people of color and thus invisibly perpetuates discrimination.Recently there is more attention for these problems, amongst others within UNESCO.This issue however runs much deeper when seen from a post-colonial, counter hegemonic, perspective where decolonization of the mindset is still in its infancy when it comes to debates of development, sustainability and human rights.The question is whether different value systems would also lead to different choices in programming and application of AI.Ubuntu (I am a person through other persons) is one such ethic in Africa, that starts from collective morals rather than individual ethics.What are the implications for AI when seen from a collective ontology?When confronted with issues of privacy, Ubuntu emphasizes transparency to group members, rather than individual privacy.When confronted with economic choices, Ubuntu favors sharing above competition.In democratic terms it promotes consensus decision making over representative democracy.What are the implications for designing a worldwide guideline on ethics of AI?And are African philosophers involved in this discussion, or simply (Western-trained) AI experts from Africa?Certain applications of AI may be more controversial in Africa than in other parts of the world, for example in care for the elderly, that deserve the utmost respect and attention, but at the same time AI may be helpful, as care from the home and community is encouraged from an Ubuntu perspective.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".