Data · dataset · 2026
Investigations in fair and responsible AI
Listed in ZivaHub and Deakin Research Online and DMU Figshare — shown once because both records carry DOI 10.17034/32826128.v1
Artificial Intelligence (AI) has shown much promise in automating tasks that require human expertise.
Description
Indeed, AI is being embedded into multiple decision-making processes, often leading to lower costs while retaining or even exceeding the performance of human decision-makers. However, the indiscriminate use of AI within decision-making processes, with little or no regard to the ramifications, poses a significant problem.
The lack of human oversight only further aggravates this problem. This is more serious when AI is used for decision-making within the public sector, which has very different ethos than those of the corporate sector, as the latter largely drives the advancement in AI technologies. Furthermore, it is often the vulnerable sections of the society that bear the brunt.
Read the rest (2 more)
Evidently, there is a need to study the use of AI within the public sector, and its potential for discriminating and disadvantaging people. This thesis consists of a broad range of investigations that study the negative impact of AI if used without due diligence. These investigations include the potential issues of AI use within multiple important public sector domains (public health, policing, immigration, elections), and recommendations and alternative directions towards responsible development and usage of AI for the public sector.
Furthermore, AI’s tendency to unfairly and unjustifiably discriminate between sections of society is looked at, with clustering – a popular machine learning problem – as our focus. New notions, metrics, and algorithms are developed and studied towards advancing research on fair AI.
Links
Where it is published
- DOI doi.org/10.17034/32826128.v1 ↗
DOI / persistent id · from zivahub uct ac za
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from zivahub uct ac za
Topics
- From keywords
- Computer Science & AI · Computer Science & AI · Computer Science & AI · Earth & Environmental Science · Earth & Environmental Science · Earth & Environmental Science
- Inferred from text
- Artificial intelligence 72%
Provenance · 3 source records, 11 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ZivaHub | oai:figshare.com:article/32826128 | 13 d ago | JSON v1 |
| Deakin Research Online | oai:figshare.com:article/32826128 | 13 d ago | JSON v1 |
| DMU Figshare | oai:figshare.com:article/32826128 | 13 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:group:4602 | enrichment · zivahub uct ac za | taxonomy-embedding@1.1.0 | title+keywords+description (72%) |
| concepts[field].local:field:computer-science-ai | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
| description | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/description |
| license_text | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| publication_date | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| title | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/title |