REFLECTing SPERET: Measuring and Promoting Ethics and Privacy Reflexivity in Eye-Tracking Research

TL;DR

The paper tackles the need for ethical and privacy reflexivity in eye-tracking research by introducing two complementary tools: REFLECT, a qualitative questionnaire, and SPERET, a psychometric scale. Through workshops and input from over $N>70$ researchers, the authors ground REFLECT in open-ended reflections and derive SPERET items from those insights, ensuring relevance to eye-tracking contexts. The REFLECT analysis yields three themes—privacy primacy, generalizability concerns (WEIRD bias), and misuse risks—while SPERET demonstrates acceptable internal consistency ($ ext{Cronbach’s }\alpha=0.936$ after refinement; validation sample: $ ext{α}=0.772$) and a structured subscale setup (Privacy, WEIRD Burden, Misuse). The work offers practical guidance for individuals and communities to foster responsible, privacy-aware eye-tracking practice and highlights both the potential and risks of reflexivity tools, including the threat of ethics-washing if misused. Overall, REFLECT and SPERET provide a formative framework to promote ethical growth, inform training, and enable governance structures that support responsible innovation in eye-tracking technologies.

Abstract

The proliferation of eye tracking in high-stakes domains - such as healthcare, marketing and surveillance - underscores the need for researchers to be ethically aware when employing this technology. Although privacy and ethical guidelines have emerged in recent years, empirical research on how scholars reflect on their own work remains scarce. To address this gap, we present two complementary instruments developed with input from more than 70 researchers: REFLECT, a qualitative questionnaire, and SPERET (Latin for "hope"), a quantitative psychometric scale that measures privacy and ethics reflexivity in eye tracking. Our findings reveal a research community that is concerned about user privacy, cognisant of methodological constraints, such as sample bias, and that possesses a nuanced sense of ethical responsibility evolving with project maturity. Together, these tools and our analyses offer a systematic examination and a hopeful outlook on reflexivity in eye-tracking research, promoting more privacy and ethics-conscious practice.

Figures (3)

• Figure 1: There exists a marked imbalance between the overall volume of publications in the field of eye tracking (Left) and the comparatively small number of studies that address the privacy and ethical dimensions of this technology (Right). The infographic is generated by dimensions.ai showing data from the past 10 years.
• Figure 2: The development and linkage of our complementary tools REFLECT and SPERET. The thematic findings of the qualitative analysis are the basis for the item statements. $N$ denotes the number of participants. The icons are generated by deepai.org.
• Figure 3: A recognition and documentation of researchers who have made significant contributions to the field of privacy-preserving eye tracking. Although they currently represent a minority within the broader community, our objective is to acknowledge their efforts and encourage continued advancement in this important area. The infographic is generated based on dimensions.ai's content.