IoT data monetization contributes to surveillance capitalism

What is surveillance capitalism?

Surveillance capitalism refers to a new economic logic of generating revenues by surveilling our digital lives in order to predict and control human behavior for commercial gain. It is centered around the commodification of personal information, powered by mechanisms, companies and business models, which are virtually invisible to people.

The term was coined in 2014 by Shoshana Zuboff, Harvard Business School professor emerita, but has gained attention concurrent with revelations of widespread data malpractice. First pioneered at Google and later Facebook, these organizations unilaterally collect human experience as free raw material. Data as raw material is fed into proprietary behavioral analysis engines, then "fabricated into prediction products anticipating what you will do now, soon and later," Zuboff wrote in her book The Age of Surveillance Capitalism. Organizations that use this model for IoT data monetization drive users toward organizations' desired outcomes for better or worse.

How does IoT fit into surveillance capitalism?

Studies show organizations deploy IoT technology to achieve a wide range of business benefits, such as boosting productivity of employees or improving the quality of products. But studies don't typically mention behavioral analysis or personal data collection as a motivation for adopting IoT. Three factors tie IoT to surveillance capitalism and emphasize IoT providers' role in responsible IoT data monetization.

1. Consumer IoT is synonymous with personal data collection today. Any device marketed as smart contains sensors, software and personalization mechanisms designed for IoT data monetization. Organizations encourage consumers to welcome data collection in their homes and on their bodies through many devices, such as smart door locks, digital assistants, wearables and washing machines. Consumer IoT also serves as ground zero for numerous biometric and emotion sensors coming online, many of which are designed to infer stress, fatigue, illness or intoxication.

Devices don't necessarily have to be "supply-chain interfaces for the unobstructed flow" of intimate user data, as Zuboff said. Consumer devices can deliver value without commoditizing user data. But the data capture-package-sell model is the predominant IoT data monetization model today. The model is driven by the logic that behaviors, needs and weaknesses can be predicted to serve commercial gains. Asymmetrical knowledge about consumers engenders asymmetrical power over them.

Designers of IoT devices can ensure user value aligns with awareness and trust. They should ask questions during the development process such as how can technology enlighten users while delivering the intended use case, and how can design maximize desired outcomes and trust while minimizing risk and obfuscation?

2. Commercial IoT powers the instruments for data profiling. The internet sector has pioneered the business models of behavioral targeting, but surveillance capitalism is by no means restricted to direct-to-consumer devices. Insurance organizations now commonly use behavioral underwriting -- the decision of whether applicants are eligible for insurance, loans or credit based on behaviors -- powered by sensors in the home, car and body. Telcos increasingly deal in location data and financial firms in transaction data. Organizations across retail, education, consumer packaged goods, healthcare and transportation all have introduced behavioral models into their business plans.

It's up to IoT leaders to design the next digital era -- one that aligns foremost with people's rights, dignity, safety and well-being.

In reality, years of invisible, unobstructed personal data sharing and sales have fostered a multibillion dollar data brokerage industry, according to CBS video report "The Data Brokers." The diverse cast of intermediaries mine, fuse, categorize, package and resell profiles, "psychographics" and other propensities to any paying buyer, including those on the dark web.

The forces behind surveillance capitalism are greater than any single IoT provider. Leaders must consider whose interests their technology serves and who takes responsibility for downstream effects of data malpractice. They must ask how information can support diversified IoT data monetization models, not only for commercial resilience, but to address more urgent issues than behavioral targeting, such as carbon-free energy, education, disease cures and hunger.

3. IoT's convergence with other technologies will influence the fate of surveillance capitalism. No technology is inherently an instrument of surveillance capitalism; it is a logic, not a technology. IoT devices and connected infrastructure are only part of the proverbial technology stack. AI, computer vision, augmented reality, social media, and emerging techniques and interfaces converging with IoT power and scale the surveillance capitalism logic. For example, computer vision endows connected cameras with facial and emotion recognition, which use AI to derive categorizations, such as happy or sick.

IoT's convergence with other technologies does not necessarily equate to more surveillance capitalism. Organizations employ tools to support the logic, but tools can also be employed to divert it. Differential privacy, federated learning, various de-identification and encryption techniques are new configurations designed to thwart unfettered flow of sensitive data.

IoT leaders must think beyond IoT, because multiple parallel technologies powering and converging with IoT can create new designs, power new capabilities and unlock new market opportunities. Surveillance capitalism itself was simply unimaginable prior to the digital milieu. It's up to IoT leaders to design the next digital era -- one that aligns foremost with people's rights, dignity, safety and well-being.