
## Opening the Analogical Portal to Explainability: Can Analogies Help Laypeople in AI-assisted Decision Making?
Gaole He, Agathe Balayn, Stefan Buijsman, Jie Yang, Ujwal Gadiraju
Keywords: 
JAIR/2024/Proceedings/15118 - Opening the Analogical Portal to Explainability: Can Analogies Help Laypeople in AI-assisted Decision Making?.pdf
Project URL: 

### Implementation
_Given the documentation given by the authors on the method, how much time investment would it be to re-implement the method from scratch?_

[1]

The authors conduct a human study, implementation less applicable. The code for their analysis can be found in footnote. (https://github.com/delftcrowd/HCOMP2022_ARCHIE/tree/main)

### Data
_Given the data description in the documentation, how much effort take to either: Find the same dataset the authors used, or similar datasets and defend the comparability, or acquire one from scratch?_

[1]

(1/1)

The authors collect data from humans, and provide the annotation manual online. The procedure is documented extensively, and a rich data analysis is done. The data set is available in the same link as the annotation manual.

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for their results, and compare against their budgetary constraints?_

[1]

N/A

### Experimental Procedure
_Given the experimental set-up of the work, how difficult is it to set up a new experiment, similar to those presented in the original work, with the same procedure?_

[1]

The authors analyse the data with standard statistical procedures.

### Expertise
_How much effort would it take to acquire the expertise required to reproduce the work independently relying on the available documentation?_

[2]

Requires expertise on XAI.
