DOI: 10.5281/zenodo.11448395
title: XAI-FUNGI: Dataset from the user study on comprehensibility of XAI algorithms

description: XAI-FUNGI: Dataset from the user study on comprehensibility of XAI algorithms

We present the dataset which was created during a user study on evaluation of explainability of artificial intelligence (AI) at the Jagielloninan University as a collaborative work of computer science (GEIST team) and information sciences research groups. The main goal of the research was to explore effective explanations of AI model patterns to diverse audiences.

The dataset contains material collected from 39 participants during the interviews conducted by the Information Sciences research group.  The participants were recruited from 149 candidates to form three groups that represented domain experts in the field of mycology (DE), students with data science and visualization background (IT) and students from social sciences and humanities (SSH). Each group was given an explanation of a machine learning model trained to predict edible and non-edible mushrooms and asked to interpret the explanations and answer various questions during the interview. The machine learning model and explanations for its decision were prepared by the computer science research team.

The resulting dataset was constructed from the surveys obtained from the candidates, anonymized transcripts of the interviews, the results from thematic analysis, and original explanations with modifications suggested by the participants. The dataset is complemented with the source code allowing one to reproduce the initial machine leaning model and explanations.

The general structure of the dataset is described in the following table. The files that contain in their names [RR]_[SS]_[NN] contain the individual results obtained from particular participant. The meaning of the prefix is as follows:



RR - initials of the researcher conducting the interview,

SS - type of the participant (DE for domain expert, SSH  for social sciences and humanities students, or IT for computer science students),

NN - number of the participant


 




File
Description


SURVEY.csv
The results from a survey that was filled by 149 participants out of which 39 were selected to form a final group of particiapnts.


CODEBOOK.csv
The codebook used in thematic analysis and MAXQDA coding


QUESTIONS.csv
List of questions that the participants were asked during interviews. 


SLIDES.csv
List of slides used in the study with their interpretation and reference to MAXQDA themes and VISUAL_MODIFICATIONS tables.


MAXQDA_SUMMARY.csv
Summary of thematic analysis performed with codes used in CODEBOOK for each participant


PROBLEMS.csv
List of problems that participants were asked to solve during interviews. They correspond to three instances from the dataset that the participants had to classify using knowledge gained from explanations.


PROBLEMS_RESPONSES.csv
The responses to the problems for each participant to the problems listed in PROBLEMS.csv


VISUALIZATION_MODIFICATIONS.csv
Information on how the order of the slides was modified by the participant, which slides (explanations) were removed, and what kind of additional explanation was suggested.


ORIGINAL_VISUZALIZATIONS.pdf
The PDF file containing the visualization of explanations presented to the participants during the interviews


VISUALIZATION_MODIFICATIONS.zip
The PDF file containing the original slides from ORIGINAL_VISUZALIZATIONS.pdf with the modifications suggested by the participant. Each file is a PDF file named with the participant ID, i.e. [RR]_[SS]_[NN].pdf


TRANSCRIPTS.zip
The anonymized transcripts of interviews for each given participant, zipped into one archive. Each transcript is named after the particiapnt ID, i.e. [RR]_[SS]_[NN].csv and contains text tagged with slide number that it related to, question number from QUESTIONS.csv, and problem number from PROBLEMS.csv.




The detailed structure of the files presented in the previous Table is given in the Technical info section.

The source code used to train ML model and to generate explanations is available on  Gitlab

 
description: Technical Info

The following sections contain descriptions of the records in the particual files. We provide description only to the CSV files, which has a structure. The PDF files content is self explanatory, and does not need additional description.

SURVEY




Column name
Column description


candidate_id
Unique ID of the participant of a survey. Note that this ID is not used further in other files. Instead participant_id should be used to join records from the CSV files.


[set of columns corresponding to survey content]
This columsn are self explanatory, as the title of the colum contain a question the participant was asked and the content the answer given. It also contains some metadata connected with the survey as time spent to fill in the survey by the participant, dates when the survey was filled etc.


participant_id
The participant ID that is an unique identifier of an individual that participated in the interviews. This column should be used to join records form other tables.


comment
Column containing additional comments for participants. In current version this comments are limited to indicators which of the candidates were used as pilot study participants that were nnot included in the final dataset.




CODEBOOK




Column name
Column description


code
The name of a code used in thematic analysis that has the hierarchy encoede within. The > divides hierarchy levels. For instance Aesthetics > layout represent a code that is on the second level of the hierarchy, havind Aesthetics as the parent.


memo
The meaning of a given code.




SLIDES




Column name
Column description


slide_id
Unique ID of a slide that allows to join other tables that uses slide ID with TRANSCRIPTS


maxqda_theme
The name of the explanatation type used in thematic analysis and present in columns of MAXQDA_SUMMARY.csv


slide_name
Slide name used in VISUALIZATION_MODIFICATIONS.csv


comment
The explanation of a content of the slide




MAXQDA_SUMMARY




Columna name
Column description


code
Name of the code, that can be mached with CODEBOOK.csv


[list of columns corresponding to particual type of explanation, e.g., LIME, descriptive statistics, etc.]
Number of occurences of a given code in a given type of explanation


participant_id
ID of the participant for which the summary was prepared




PROBLEMS




Column name
Column description


problem_id
Unique ID of the perticipant that can be usede to join the table with other tables


[set of features for a given mushroom used in particual problem]
Features values of a mushroom to be classified


model_class
Class returned by the machine learnign model


model_probability
Probability assigned by the machine learnign model to the prediction




PROBLEMS_RESPONSES




Column name
Column description


problem_id
Unique ID of the problem that can be used to join the responses with PROBLEMS table and other tables.


participant_id
Unique ID representing given participant that can be sued as a key to join with other tables


prediction_decision
The class assigned by the participand to the given problem


prediction_certainty
The certainty of the particiapnt decision




VISUALIZATION_MODIFICATIONS




Column name
Column description


participant_id
Unique ID of the problem that can be used to join the table with other tables.


slide_id
ID of a slide used in other tables 


original_order
The original order of a given slide in the presentation. In case of custom slides, thet were added to the presentation by the participant, this field is empty.


new_order
The order (place in a presentation) of a slide assigned by the participant. 


slide_name
symbolic name of the slide


modification
The type of modification suggested by the participant, where 0 means no modification, 1 removal and 2 addition of custom slide


details
Details o a custom slide added to the presentation. In case of the other slides this field is empty




QUESTIONS




Column name
Column description


question_id
Unique ID of each of the question that allows to match the actual question text with transcripts


realted_slide_id
The slide ID that the question was oroginally assigned to


question
The actual text of the question 




TRANSCRIPTS




Column name
Column description


speaker_id
ID of a person who is the autorship of the text in the following column. This is basically the distinction between investigator and participant.


slide_id
ID of a slide that matches the ID in SLIDES.csv that the following text is related to. The row in which the slide ID appears is marked as the starting point there this slide is on the participant screen. There are three special slides numbers that identifis the stage of the interview : __S00__ indicates the begining of the core part of the interview with slide analysis, before that the description of the study is introducet to the participant; __S99__ represent the begining of the section where the participants analyze visualization order, the slides ids are not assigned in this section, due to dynamic slide switching by participants; __S15__ represents the end of the slide analysis section,  __S88__ represents the begining of the problem solving section .


question_id
ID of a question that can be matched with QUESTIONS.csv. It represent a place, where the question was asked, or where the participant started giving answer to the question. Not all of the questions from QUESTIONS can be matched with transcripts, as some participants did not answer some of the questions, or the tagging of a question answer was to vague.


problem_id
ID of a problem that participants were asked to solve that can be matched with PROBLEMS.csv. The apperance of the ID in a row indicate that from this point in time the participant tried to solve the problem.


text
The anonymized transript of the participant words obtained with MS Teams.