AI Risks and Benefits Scale
Measures perceptions of risks and benefits associated with artificial intelligence (AI)-based technologies in healthcare.
Scale Development & Technical Details
Scale Overview
The AI Risks and Benefits Scale was developed by Sophie Kerstan, Nadine Bienefeld, & Gudela Grote (2024). It is designed to measure Attitudes toward Artificial Intelligence in Healthcare. The scale is intended for use with Adults.
Scale Structure
This instrument consists of 19 items organized into 2 factors/subscales: Risk Perceptions, Benefit Perceptions.
| Factor / Subscale | Items | N |
|---|---|---|
| Risk Perceptions | 1,2,3,4,5,6,7,8,9,10 | 10 |
| Benefit Perceptions | 11,12,13,14,15,16,17,18,19 | 9 |
Response Format
Respondents rate each item using a custom response format.
Response anchors: 1 = Very unlikely, 2 = Unlikely, 3 = Somewhat unlikely, 4 = Neutral, 5 = Somewhat likely, 6 = Likely, 7 = Very likely.
Scoring
Items are scored by subscale, with each subscale sum representing a distinct dimension.
Total scores range from 19 to 133. Interpretation guidelines:
- Low perceived likelihood of AI risks/benefits: 19 – 57
- Moderate perceived likelihood of AI risks/benefits: 58 – 95
- High perceived likelihood of AI risks/benefits: 96 – 133
Psychometric Properties
Internal Consistency: The scale has demonstrated good to excellent internal consistency with reported Cronbach’s alpha values of Cronbach's alpha = 0.87 (risk perceptions), 0.84 (benefit perceptions).
Administration
The scale is self-administered and typically takes approximately 5 minutes to complete. It can be administered individually or in group settings. No special training is required for administration.