Unlocking the Black Box of Wearable Intelligence: Ethical Considerations and Social Impact

Computational intelligence is making its way into a variety of popular consumer products, including wearable physiological monitors such as activity trackers and sleep trackers. Such products are very convenient for the user, but this convenience is the result of a trade-off that has ethical implications, since in almost all cases it denies the user access to their own raw data underlying the easy-to-understand analyses that the products generate for them. One problem with this is that the user is not made aware of the uncertainty of the conclusions or analyses drawn from the data; another is that it is difficult for the user to reuse his or her data in other contexts, such as to combine data from multiple sources. Even if the user did have full control of the data, this would only solve part of the problem, because most people do not have the special skills required to analyze such data. This overall problem could be solved through collaboration between the data owner and a data analysis expert, though this again introduces further problems, notably that of preserving the data owner’s privacy. In this paper we analyze the aforementioned issues pertaining to the ethics of wearable intelligence, propose possible approaches to handling them, and discuss the potential social impact of the technology if the issues can be successfully overcome.

[1]  Michael Zimmer,et al.  ‘There’s nothing really they can do with this information’: unpacking how users manage privacy boundaries for personal fitness information , 2018, Information, Communication & Society.

[2]  Eser Kandogan,et al.  LabBook: Metadata-driven social collaborative data analysis , 2015, 2015 IEEE International Conference on Big Data (Big Data).

[3]  P. Shah,et al.  Review of Graph Comprehension Research: Implications for Instruction , 2002 .

[4]  Alan F. Smeaton,et al.  Challenges and Opportunities of Lifelog Technologies: A Literature Review and Critical Analysis , 2014, Sci. Eng. Ethics.

[5]  Mitja Lustrek,et al.  Estimating Energy Expenditure With Multiple Models Using Different Wearable Sensors , 2016, IEEE Journal of Biomedical and Health Informatics.

[6]  Paul Humphreys,et al.  The philosophical novelty of computer simulation methods , 2009, Synthese.

[7]  N. Johnston,et al.  Consumer physical activity tracking device ownership and use among a population-based sample of adults , 2018, PloS one.

[8]  Michael Goldsmith,et al.  Unwinding Ariadne's Identity Thread: Privacy Risks with Fitness Trackers and Online Social Networks , 2017, MPS@CCS.

[9]  Ilaria Torre,et al.  Supporting users to take informed decisions on privacy settings of personal devices , 2018, Personal and Ubiquitous Computing.

[10]  Tamara Heck,et al.  Rewarding Fitness Tracking - The Communication and Promotion of Health Insurers' Bonus Programs and the Use of Self-tracking Data , 2018, HCI.

[11]  Lorenzo Scalise,et al.  Wearables for health and fitness: Measurement characteristics and accuracy , 2018, 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).

[12]  Munni Begum,et al.  Comparison of linear and non-linear models for predicting energy expenditure from raw accelerometer data , 2017, Physiological measurement.

[13]  Alan F. Smeaton,et al.  Ontology-based negotiation and enforcement of privacy constraints in collaborative knowledge discovery , 2018 .

[14]  Daniele Senzani.  CHARTER OF FUNDAMENTAL RIGHTS OF THE EUROPEAN UNION , 2016 .

[15]  Mukund Deshpande.  ShareInsights: An Unified Approach to Full-stack Data Processing , 2016, COMAD.

[16]  Bart Jacobs,et al.  Increased security through open source , 2007, Commun. ACM.

[17]  Bran Knowles,et al.  Uncertainty in current and future health wearables , 2018, Commun. ACM.

[18]  Alejandro Baldominos Gómez,et al.  A Comparison Study of Classifier Algorithms for Cross-Person Physical Activity Recognition , 2016, Sensors.

[19]  Philipp Brauner,et al.  A Step in the Right Direction - Understanding Privacy Concerns and Perceived Sensitivity of Fitness Trackers , 2017, AHFE.

[20]  Clayon B Hamilton,et al.  Accuracy of Fitbit Devices: Systematic Review and Narrative Syntheses of Quantitative Data , 2018, JMIR mHealth and uHealth.

[21]  James Irvine,et al.  Privacy Implications of Wearable Health Devices , 2014, SIN.

[22]  Alan F. Smeaton,et al.  A Review of Contemporary Work on the Ethics of Ambient Assisted Living Technologies for People with Dementia , 2014, Science and Engineering Ethics.

[23]  A. Smeaton,et al.  Behavioral Periodicity Detection from 24 h Wrist Accelerometry and Associations with Cardiometabolic Risk and Health-Related Quality of Life , 2016, BioMed research international.

[24]  Tim Berners-Lee,et al.  A Demonstration of the Solid Platform for Social Web Applications , 2016, WWW.

[25]  Klaas R. Westerterp,et al.  Physical activity and physical activity induced energy expenditure in humans: measurement, determinants, and effects , 2013, Front. Physiol..