R0024/2026-03-25/Q003/SRC01
CHI 2025 study on dark addiction patterns in AI chatbot interfaces
Source
| Field |
Value |
| Title |
The Dark Addiction Patterns of Current AI Chatbot Interfaces |
| Publisher |
ACM (CHI Conference on Human Factors in Computing Systems) |
| Author(s) |
M. Karen Shen, Dongwook Yoon |
| Date |
April 2025 |
| URL |
https://dl.acm.org/doi/10.1145/3706599.3720003 |
| Type |
Research paper (peer-reviewed conference) |
Summary
| Dimension |
Rating |
| Reliability |
High |
| Relevance |
High |
| Bias: Missing data |
Low risk |
| Bias: Measurement |
Some concerns |
| Bias: Selective reporting |
Low risk |
| Bias: Randomization |
N/A — not an RCT |
| Bias: Protocol deviation |
N/A — not an RCT |
| Bias: COI/Funding |
Low risk |
Rationale
| Dimension |
Rationale |
| Reliability |
Published at CHI, the premier HCI conference. Peer-reviewed. Authors from established institution. |
| Relevance |
Directly identifies four addictive design patterns in AI chatbots, including empathetic/agreeable responses and non-deterministic outputs as reward mechanisms. |
| Bias flags |
Some measurement concerns: the dopamine framework is inferred from behavioral addiction theory rather than directly measured. This is acknowledged by the authors and is standard for HCI research. |
| Evidence ID |
Summary |
| SRC01-E01 |
Four dark addiction patterns identified including sycophantic responses as dopamine-activating mechanism |