| Category | Details |
|---|---|
| Description | The AI Risks and Benefits Scale (Kerstan, Bienefeld, & Grote, 2024) was developed to assess perceptions of risks and benefits associated with artificial intelligence (AI)-based technologies in healthcare, without reference to specific AI applications. This questionnaire was designed for a study examining the preference for human doctors versus AI-based treatment recommendations in an online sample of adults recruited via Prolific. The scale consists of 19 items derived from themes identified in prior research (e.g., Blease et al., 2019) and a pretest. Factor analysis and reliability assessments were conducted. |
| Test Type | Original |
| Instrument Type | Inventory/Questionnaire |
| Construct | Attitudes toward Artificial Intelligence in Healthcare |
| Purpose | To measure the likelihood of risks or benefits related to artificial intelligence occurring in healthcare practice. |
| Test Year | 2024 |
| Author | Kerstan, Sophie; Bienefeld, Nadine; Grote, Gudela |
| Affiliation | ETH Zurich, Department of Management, Technology, and Economics |
| Author Identifier | Sophie Kerstan: ORCID; Nadine Bienefeld: ORCID; Gudela Grote: ORCID |
| Sophie Kerstan: [email protected] | |
| Correspondence Address | Sophie Kerstan, ETH Zurich, Department of Management, Technology, and Economics, Work and Organizational Psychology, Weinbergstrasse 56/58, Zurich, Switzerland, 8092, [email protected] |
| Web Site | Creative Commons License |
| Format | Participants rate items on a 7-point scale (1 = Very Unlikely to 7 = Very Likely). |
| Administration Method | Electronic |
| Number of Items | 19 items |
| Factors and Subscales | Subscales: Risk Perceptions; Benefit Perceptions |
| Reliability | Internal Consistency: Cronbach’s alpha = 0.87 (risk perceptions), 0.84 (benefit perceptions). |
| Validity | No validity indicated. |
| Factor Analysis | Exploratory Factor Analysis (EFA): A 2-factor solution was identified for risk perception items, while a 1-factor solution was found for benefit perception items. One benefit perception item was excluded due to loading on a separate factor. Confirmatory Factor Analysis (CFA): A five-factor model showed acceptable fit (χ² = 1008.46, df = 547, p < 0.001, χ²/df = 1.84, CFI = 0.91, TLI = 0.90, RMSEA = 0.04, SRMR = 0.06). A four-factor model combining risk and benefit perceptions into one factor showed significantly poorer fit. |
| Test Methodology | Test Reliability; Internal Consistency; Factor Analysis; Confirmatory Factor Analysis; Exploratory Factor Analysis |
| Classification | Human-Computer Interaction; Treatment, Rehabilitation, and Therapeutic Processes |
| Age Group | Adulthood (18 yrs & older) |
| Population Group | Human; Male; Female |
| Population Details | Location: United States; Respondents: Adult Participants |
| Keywords | Artificial Intelligence; Benefit Perceptions; Healthcare Practice; Risk Perceptions; Treatment |
| Index Terms | Artificial Intelligence; Client Attitudes; Health Care Delivery; Risk Perception; Telemedicine; Therapeutic Processes; Treatment Process and Outcome Measures; Health Attitude Measures; Human-Computer Interaction Measures |
| Files | No file available for download. |
| Reference | Kerstan, S., Bienefeld, N., & Grote, G. (2024). AI Risks and Benefits Scale: Measuring risk-benefit perceptions of AI-based technologies in healthcare. ETH Zurich, Department of Management, Technology, and Economics. |
Al Risks and Benefits Scale
Variable: Risk-benefit perceptions (self-developed)
Items/Stimuli: In your opinion, how likely are the following risks or benefits to occur? Al in healthcare might …
Risks:
| Item Number | Item Description |
| R1 | … fail to recognize the uniqueness of each patient’s condition |
| R2 | … result in a loss of jobs for healthcare professionals |
| R3 | … increase patient data security breaches |
| R4 | … lead to ethical problems |
| R5 | … introduce new bugs and equipment failures |
| R6 | … result in overreliance on technology |
| R7 | … dehumanize care |
| R8 | … increase medical errors |
| R9 | … provide unreliable information |
| R10 | … measure patient parameters inaccurately |
Benefits:
| Item Number | Item Description |
| B1 | … reduce medical errors |
| B2 | … enable a more personalized care |
| B3 | … facilitate the prediction of negative health events |
| B4 | … improve information sharing between patients and healthcare providers |
| B5 | … improve the accessibility of care |
| B6 | … provide more accurate health data measurements |
| B7 | … help in monitoring treatment efficiency |
| B8 | … help in diagnosing health problems |
| B9 | … help in choosing adequate treatments |
Response Options
| Response | Numerical Value |
| Very unlikely | 1 |
| Unlikely | 2 |
| Somewhat unlikely | 3 |
| Neutral | 4 |
| Somewhat likely | 5 |
| Likely | 6 |
| Very likely | 7 |
Cite this article
Mohammed looti (2026). AI Risks and Benefits Scale. PSYCHOLOGICAL SCALES. Retrieved from https://scales.arabpsychology.com/s/ai-risks-and-benefits-scale-2/
Mohammed looti. "AI Risks and Benefits Scale." PSYCHOLOGICAL SCALES, 4 Apr. 2026, https://scales.arabpsychology.com/s/ai-risks-and-benefits-scale-2/.
Mohammed looti. "AI Risks and Benefits Scale." PSYCHOLOGICAL SCALES, 2026. https://scales.arabpsychology.com/s/ai-risks-and-benefits-scale-2/.
Mohammed looti (2026) 'AI Risks and Benefits Scale', PSYCHOLOGICAL SCALES. Available at: https://scales.arabpsychology.com/s/ai-risks-and-benefits-scale-2/.
[1] Mohammed looti, "AI Risks and Benefits Scale," PSYCHOLOGICAL SCALES, vol. X, no. Y, ص Z-Z, April, 2026.
Mohammed looti. AI Risks and Benefits Scale. PSYCHOLOGICAL SCALES. 2026;vol(issue):pages.