AI for All: Identifying AI incidents Related to Diversity and Inclusion
Authors: Rifat Ara Shams, Didar Zowghi, Muneera Bano
JAIR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This study addresses these challenges by identifying and understanding D&I issues within AI systems through a manual analysis of two AI incident databases, AI Incident Database (AIID) and AI, Algorithmic, and Automation Incidents and Controversies (AIAAIC). The research develops a decision tree to investigate D&I issues tied to AI incidents and populate a public repository of D&I-related AI incidents. The decision tree was validated through a card sorting exercise and focus group discussions. The research demonstrates that almost half of the analyzed AI incidents are related to D&I, with a notable predominance of racial, gender, and age discrimination. |
| Researcher Affiliation | Academia | RIFAT ARA SHAMS , CSIRO s Data61, Australia DIDAR ZOWGHI, CSIRO s Data61, Australia MUNEERA BANO, CSIRO s Data61, Australia |
| Pseudocode | Yes | Figure 3 shows the results of RQ1. To identify Diversity and Inclusion (D&I) related AI incidents, we developed a decision tree after a rigorous analysis. Our proposed decision tree has four conditions. The first condition checks if any human is directly or indirectly impacted by the AI incident. ... Similarly, Figure 4 (b) shows an example of an AI incident that is not related to D&I issues. |
| Open Source Code | No | The paper does not provide explicit source code for its methodology (e.g., the implementation of the decision tree or the analysis scripts). It does mention a public repository of D&I-related AI incidents, but this is a data resource, not source code for their method: "Finally, we have designed, populated, and made publicly available repository of D&I-related AI incidents, intended for use for future research." and "Rifat Ara Shams, Didar Zowghi, and Muneera Bano. 2024. Diversity and Inclusion (DI)Related AI Incidents Repository. https://doi.org/10.5281/zenodo.11639709" |
| Open Datasets | Yes | This study addresses these challenges by identifying and understanding D&I issues within AI systems through a manual analysis of two AI incident databases, AI Incident Database (AIID) and AI, Algorithmic, and Automation Incidents and Controversies (AIAAIC). ... 1https://incidentdatabase.ai/ 2https://www.aiaaic.org/aiaaic-repository ... Finally, we created a public repository of D&I-related AI incidents for both AIID and AIAAIC. This open-source repository will serve as a valuable resource for other researchers in this field. ... Rifat Ara Shams, Didar Zowghi, and Muneera Bano. 2024. Diversity and Inclusion (DI)Related AI Incidents Repository. https://doi.org/10.5281/zenodo.11639709 |
| Dataset Splits | No | The paper describes a qualitative and mixed-methods study involving manual analysis of incidents, card sorting, and focus group discussions. It does not perform machine learning experiments that would typically involve training, validation, and test dataset splits. |
| Hardware Specification | No | The paper describes a qualitative study involving manual analysis, card sorting exercises, and focus group discussions. It does not mention any computational experiments requiring specific hardware specifications. |
| Software Dependencies | No | The paper describes a qualitative and mixed-methods study (manual analysis, card sorting, focus groups). It does not mention specific software dependencies with version numbers for computational experiments. |
| Experiment Setup | No | The paper describes a qualitative study with a focus on identifying and categorizing AI incidents, developing a decision tree, and validating it through participatory activities. It does not involve computational experiments with hyperparameters or system-level training settings. |