| Download | - View final version: What makes a good counter-stereotype? Evaluating strategies for automated responses to stereotypical text (PDF, 907 KiB)
|
|---|
| DOI | Resolve DOI: https://doi.org/10.18653/v1/2023.sicon-1.4 |
|---|
| Author | Search for: Fraser, Kathleen C.1; Search for: Kiritchenko, Svetlana1; Search for: Nejadgholi, Isar1; Search for: Kerkhof, Anna |
|---|
| Affiliation | - National Research Council of Canada. Digital Technologies
|
|---|
| Format | Text, Article |
|---|
| Conference | First Workshop on Social Influence in Conversations (SICon 2023), July 14, 2023, Toronto, Ontario |
|---|
| Abstract | When harmful social stereotypes are expressed on a public platform, they must be addressed in a way that educates and informs both the original poster and other readers, without causing offence or perpetuating new stereotypes. In this paper, we synthesize findings from psychology and computer science to propose a set of potential counter-stereotype strategies. We then automatically generate such counter-stereotypes using ChatGPT, and analyze their correctness and expected effectiveness at reducing stereotypical associations. We identify the strategies of denouncing stereotypes, warning of consequences, and using an empathetic tone as three promising strategies to be further tested. |
|---|
| Publication date | 2023-07-14 |
|---|
| Publisher | Association for Computational Linguistics |
|---|
| Licence | |
|---|
| In | |
|---|
| Language | English |
|---|
| Peer reviewed | Yes |
|---|
| Export citation | Export as RIS |
|---|
| Report a correction | Report a correction (opens in a new tab) |
|---|
| Record identifier | 0b1f722f-e170-4108-8347-ed43d325239c |
|---|
| Record created | 2023-07-19 |
|---|
| Record modified | 2023-11-01 |
|---|
