How can philanthropy promote ethical, inclusive, and responsible AI development?
Bibliographic record
Abstract
The advent of artificial intelligence (AI) marks a crucial transformation in technology and human interaction, transcending traditional boundaries and redefining our relationship with non-human entities. This chapter showcases the quest of impactIA, a philanthropic organization, to identify and promote ethical principles that can guide the evolution of AI, moving beyond its role as a mere technological tool toward becoming an integral part of the human experience, reshaping societal dynamics and ethical considerations. In particular, impactIA endorses the principles of the Montreal Declaration for the Responsible Development of AI and presents them as crucial guiding principles for philanthropic efforts. These principles emphasize the alignment of AI with fundamental rights, urging philanthropy to support humanistic AI initiatives. The chapter calls for philanthropic leadership in the AI era, emphasizing the need for committed and responsible management to steer AI toward equitable and inclusive societal progress that includes open-source movements and advocates for transparency. This approach marks a decisive turning point in our coexistence with technology, where philanthropy plays a transformative role in shaping a future where AI serves the common good.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".