04/15/2020 – Palakh Mignonne Jude – What’s at Stake: Characterizing Risk Perceptions of Emerging Technologies

SUMMARY

The authors of this paper adapt a survey instrument from existing risk perception literature to analyze the perception of risk surrounding newer emerging data-driven technologies. The authors surveyed 175 participants (26 experts and 149 non-experts). They categorize an ‘expert’ to be anyone working in a technical role or earning a degree in a computing field. Inspired by the original 1980’s paper ‘Facts and Fears: Understanding Perceived Risk’, the authors consider 18 risks (15 new risks and 3 from the original paper). These 15 new risks include ‘biased algorithms for filtering job candidates’, ‘filter bubbles’, and ‘job loss from automation’. The authors also consider 6 psychological factors while conducting this study. The non-experts (as well as a few who were later on considered to be ‘experts’) were recruited using MTurk. The authors borrowed quantitative measures that were used in the original paper and added two new open-response questions – describing the worst-case scenario for the top three risks (as indicated by the participant) and adding new serious risks to society (if any). The authors also propose a risk-sensitive design based on the results of their survey.  

REFLECTION

I found this study to be very interesting and liked that the authors adapted the survey from existing risk perception literature. The motivation the paper reminded me about a New York Times article titled ‘Twelve Million Phones, One Dataset, Zero Privacy’ and the long-term implications of such data collection and its impact on user privacy.

 I found it interesting to learn that the survey results indicated that both experts and non-experts rated nearly all risks related to emerging technologies as characteristically involuntary. It was also interesting to learn that despite consent processes built into software and web services; the corresponding risks were not perceived to voluntary.  I thought that it was good that the authors included the open-resource question on what the user’s perceived as the worst case scenario for the top three riskiest technologies. I liked that they provided some amount of explanation for their survey results.

The authors mention that technologists should attempt to allow more discussion around data practices and be willing to hold-off rolling out new features that raise more concerns than excitement. However, this made me wonder if any of the technological companies would be willing to perform such a task. It would probably cause external overhead and the results may not be perceived by the company to be worth the amount of time and effort that such evaluations may entail.

QUESTIONS

1. In addition to the 15 new risks added by the authors for the survey, are there any more risks that should have been included? Are there any that needed to be removed or modified from the list? Are there any new psychological factors that should have been added?
2. As indicated by the authors, there are gaps in the understanding of the general public. The authors suggest that educating the public would enable this gap to be reduced more easily as compared to making the technology less risky. What is the best way to educate the public in such scenarios? What design principles should be kept in mind for the same?
3. Have any follow-up studies been conducted to identify ‘where’ the acceptable marginal perceived risk line should be drawn on the ‘Risk Perception Curve’ introduced in the paper?