Evidence map›Paper›PMID 37113649›Full record

ArticleFrontiers in artificial intelligence2023

AI for Anglophone Africa: Unlocking its adoption for responsible solutions in academia-private sector.

Ramadhani Sinde, Salim Diwani, Judith Leo, Tabu Kondo, Noe Elisa, Jabhera Matogoro

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
1.4field-weighted citation impact, top 16% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 8 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Ramadhani SindeSchool of Computational and Communication Science and Engineering, Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha, Tanzania.
Salim DiwaniDepartment of Computer Science and Engineering at the College of Informatics and Virtual Education, The University of Dodoma, Dodoma, Tanzania.
Judith LeoSchool of Computational and Communication Science and Engineering, Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha, Tanzania.
Tabu KondoDepartment of Computer Science and Engineering at the College of Informatics and Virtual Education, The University of Dodoma, Dodoma, Tanzania.
Noe ElisaDepartment of Computer Science and Engineering at the College of Informatics and Virtual Education, The University of Dodoma, Dodoma, Tanzania.
Jabhera MatogoroDepartment of Computer Science and Engineering at the College of Informatics and Virtual Education, The University of Dodoma, Dodoma, Tanzania.
The University of Dodoma · TZNelson Mandela African Institution of Science and Technology · TZ

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, AI technologies have become indispensable in social and industrial development, yielding revolutionary results in improving labor efficiency, lowering labor costs, optimizing human resource structure, and creating new job demands. To reap the full benefits of responsible AI solutions in Africa, it is critical to investigate existing challenges and propose strategies, policies, and frameworks for overcoming and eliminating them. As a result, this study investigated the challenges of adopting responsible AI solutions in the Academia-Private sectors for Anglophone Africa through literature reviews, expert interviews, and then proposes solutions and framework for the sustainable and successful adoption of responsible AI.

Indexed as

academia-private sectorAI stakeholdersAnglophone Africa educationartificial intelligenceresponsible solution

Identifiers

PMID37113649
PMCPMC10126471
OpenAlexW4363672182

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.