Internet of behaviours

Author: Xabier Lareo

In 2012, Professor Gote Nyman coined the term Internet of Behaviours (IoB) to describe a network in which behavioural patterns would have an IoB address in the same way that each device has an IP address in the Internet of Things (IoT).

However, the term IoB is most often used to describe an extension of the Internet of Things (IoT). A network of interconnected physical and digital objects that collect and exchange information over the Internet, linking this data to specific human measured or inferred behaviours. Gartner Consulting highlighted the Internet of Behaviours as one of the Top Strategic Technology Trends for 2021.

The aim of IoB is to address how data collected can be interpreted from a human psychological and sociological perspective and how to use this understanding to influence or change human behaviour for various purposes, ranging from commercial interests to public policies.

Overall, IoB is not a completely new concept: behavioural-targeted advertising tracks human behaviour to show personalised ads, or Bluetooth and Wi-Fi technologies are used in malls to infer prospective shoppers’ behaviours with a view to better marketing. IoB somehow integrates extensively all these technologies in a holistic approach, and are able to follow people’s lives and behaviours whenever it is possible to measure their interaction with the digital or physical objects surrounding or interacting with them.

An example of an application of IoB could be the use of patients’ and employees’ location data in hospitals during the COVID-19 pandemic to identify the behaviours that spread or mitigate the virus, in order to be able to influence future ones for people’s benefit (e.g. RFID tags at handwashing stations to identify if employees are following hygiene protocols). Information from RFID readers could be used to track when and how often healthcare workers or patients are washing their hands and place reminding messages in relevant spots.

Computer vision could detect non-compliance with preventive policies, such as the obligation of wearing masks, and trigger reminders on the closest screen.

Negative impacts foreseen on data protection:

  • Increased processing of personal data and profiling: General IoB relies on the collection and processing of data from different IoT devices, such as wearables, smart cameras or Bluetooth and Wi-Fi sensors. These devices include identifiers (e.g. IP, MAC or email addresses) that make it possible to cross-link, profile and identify individuals. This increased processing of personal data -   possibly by different actors and for different purposes - might easily conflict with the principles of data minimisation and purpose limitation.
  • Lack of transparency and control: IoT devices suffer from transparency and control issues because they often lack appropriate means to inform their users (e.g. tiny screens or absence of it or of any other form of notice), their data collection is seamless (e.g. surveillance cameras) and the means to exert control over the processing are limited. The General IoB inherits these transparency issues and could make them even greater if IoB users are not properly informed of the way their behavioural data are processed.
  • Potential Inaccuracy: The General IoB works on the assumption that human behaviour can be accurately inferred by tracking individuals. However, this might not be the case in many contexts, due to weaknesses inherited from technologies used for inferences, such as Machine Learning (e.g. bias), and the complexity of the link between human behaviours and the rationale behind them. Data controllers will hardly be able to ensure data accuracy unless they clearly inform the individuals subject to the IoB and provide them with the means to rectify erroneous inferences.

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