Written evidence submitted by Dr Ana Canhoto, Professor Ashley Braganza and Dr Asieh Tabaghdehi
Connected tech: smart or sinister?
Evidence submitted by Dr Ana Canhoto, Professor Ashley Braganza, & Dr Asieh Tabaghdehi (Centre for Artificial Intelligence: Social and Digital Innovation; Brunel University London)
Our response is a collective effort, compiled by three academics based in Brunel University London:
Ana Canhoto, Reader in Marketing. Her research focuses on the use of digital technology in interactions between firms and their customers, including obtaining customer insight from social media, and AI-enabled personalisation. She leads the "Innovation, Digitalisation and Society" research lab.
Ashley Braganza, Professor in Business Transformation. His research interests encompass big data, change management, strategy implementation, process and knowledge management, and transformation enabled information systems. He is co-director of the Centre for AI: Social and Digital Innovation.
Asieh Tabaghdehi, Lecturer in Strategy & Business Economy. Her current research expertise lies in digital economy with the focus on ethics for AI, digital footprint data and SMEs digital adoption in digital ecosystem. She leads the BSc International Business Programme.
1.1. The main feature that distinguishes smart devices from any other devices that serve the same functional purpose (e.g., smart vs non-smart car) is the collection and transmission of data, over an internet connection. Smart technology can collect and transmit data about how they are used, and/or about their context of use, with little or no interference from users. Some devices can also interact with each other, with no human interference, and learn from each other (e.g., self-driving cars).
1.2. Accordingly, the most important impact of smart technology has been the datafication of daily life - i.e., the collection of data about most human and non-human (e.g., performance of machinery) activities, sometimes without the intervention - or even awareness - by those about whom data are being collected.
1.3. At the individual level, access to a continuous flow of granular and timely data fuels a desire to optimise performance (e.g., health and fitness) and productivity. But also exposes individuals to privacy risks.
1.4. In the workplace, datafication enables evidence informed decision making (e.g., pre-emptive maintenance, or designing workers’ shifts); and creates opportunities for innovation. However, it also changes the number and nature of jobs, and is leading to professional alienation
1.5. In our towns and cities, datafication can support the achievement of SDGs, such as enhancing the environment by expending the circular economy. However, that comes with the risk of uncontested surveillance.
2.1. We start from the assumption that any citizen may, in principle, be able to benefit from smart technology, and, thus, focus our attention on those attributes that may cause vulnerability.
2.2. While some groups are at an increased risk of finding themselves in a vulnerable position (as per the list of protected characteristics already embedded in legislation), it is the context rather than personal characteristics that determines vulnerability. For instance, a pensioner’s vulnerability to online scams arises from their unfamiliarity with new technology, not their age.
2.3. In terms of smart technology, vulnerability can result from:
2.4. Those citizens that find themselves at the intersection of two or more of the factors mentioned in 2.3 are at an increased risk of vulnerability.
3.1. The typical mechanism for ensuring compliance is through the imposition of standards. An example is the European Commission’s ruling that all mobile devices on sale in the EU market will have to include a USB-C charging port, to reduce electronic waste and protect consumers. However, standards and rulings focus on the end-product and, thus, are designed and applied only after a problem is evident (e.g., fatalities caused by self-driving cars).
3.2. A better approach is to identify the value destruction potential of the technology, before it is deployed. This is done by firstly, mapping the components of the whole system, including the inputs and the process used to produce the end-product. Second, there is an analysis of how the connectivity, cognitive ability and imperceptibility of the smart system can create specific risks. For instance, connectivity means that data inputs may be corrupted, incomplete, or misleading; that processing algorithms may be chosen because of the need for compatibility rather than its performance; and that poor quality outputs spread broadly and quickly, increasing the scope and likelihood of mistakes.
3.3. Push and pull mechanisms should be used to ensure that firms conduct a thorough assessment of the value destruction potential of the technologies that they develop. Push mechanisms include the development of relevant guidelines and creation of audit and enforcement mechanisms. In turn, pull mechanisms include the investment in resources to identify and handle those risks (e.g., education; diverse workforce), as well as behavioural changes (e.g., through certification).
4.1. Technological advancement created issues around information security and various short-term cyber risk such as cyber-bullying, cyber-dating violence and hacking which, in the long-term, lead to mental health risk such as depression, anxiety, psychological distress as a key social concern in digital society.
4.2. Data from smart devices are an important and valuable asset. However, unlike other assets (e.g., people or capital), data ownership creates data network effects (DNE) for the platform owner. That is, the more user data accumulated by the platform owner, the more valuable the platform becomes to each user . Therefore, DNE may result in de facto monopolies for smart device manufacturer, as a long-term risk.
4.3. One way to develop relevant awareness and behaviour, for safer use of technology, is through the use of quasi-simulations, whereby participants are required to assess an uncertain environment, plan and execute their actions; and obtain feedback through the use of metrics.
5.1. There is no evidence to support the expectation that consumers would choose a brand because of their socio-political stance. For instance, customers’ opinions on Brexit (from either side of the debate) do not influence purchase intention towards brands that moved production to the UK. However, their perceptions of such brands improved when the onshoring decision was deemed to improve the local economy or reduce carbon footprint. That is, preference for domestic firms may be influenced by emphasising their corporate social responsibility initiatives.
5.2. Preference may also be influenced by raising the cost of non-domestic options (e.g., through taxation), or its performance risk (e.g., through sanctions). For example, President Trump’s blacklisting of Huawei, in 2019, led to a decline of 47% in revenues in the consumer electronics part of the business (though the company recorded growth in other segments).
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