Name
Abstract | ||
|---|---|---|
ABSTRACTRoot cause analysis is a common data analysis task. While question-answering systems enable people to easily articulate a why question (e.g., why students in Massachusetts have high ACT Math scores on average) and obtain an answer, these systems often produce questionable causal claims. To investigate how such claims might mislead users, we conducted two crowdsourced experiments to study the impact of showing different information on user perceptions of a question-answering system. We found that in a system that occasionally provided unreasonable responses, showing a scatterplot increased the plausibility of unreasonable causal claims. Also, simply warning participants that correlation is not causation seemed to lead participants to accept reasonable causal claims more cautiously. We observed a strong tendency among participants to associate correlation with causation. Yet, the warning appeared to reduce the tendency. Grounded in the findings, we propose ways to reduce the illusion of causality when using question-answering systems. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3411764.3445444 | Conference on Human Factors in Computing Systems |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Po-Ming Law | 1 | 11 | 3.13 |
| Leo Yu-Ho Lo | 2 | 1 | 0.35 |
| Alex Endert | 3 | 974 | 52.18 |
| John Stasko | 4 | 5655 | 494.01 |
| Huamin Qu | 5 | 2033 | 115.33 |