Affective computing provides new frontier in user manipulation

By 2016, a logical direction for data-driven personalisation efforts to go was toward the "Internet of Emotions": equipping devices with facial, vocal, and biometric sensors that use affective computing to analyse and influence the feelings of device owners. Of particular concern is the potential for using subtle cues to manipulate people in a more nuanced way than is presently discussed. The beginnings of this are already visible in the example of an Amazon Echo that displayed the items a partner was browsing in answer to a casual spoken across-the-room query. How to build trust, security, or privacy into cognitive systems where users do not control their own data is a difficult question. One proposal is the idea of an "algorithmic angel" that would act as an individually programmable combined personal assistant, proxy avatar, and blocker for bad data. Numerous companies are beginning to develop prototypes.

Writer: John C. Havens
Publication: Mashable UK

What is Privacy International calling for?

People must know

People must be able to know what data is being generated by devices, the networks and platforms we use, and the infrastructure within which devices become embedded.  People should be able to know and ultimately determine the manner of processing.

Limit data analysis by design

As nearly every human interaction now generates some form of data, systems should be designed to limit the invasiveness of data analysis by all parties in the transaction and networking.

Control over intelligence

Individuals should have control over the data generated about their activities, conduct, devices, and interactions, and be able to determine who is gaining this intelligence and how it is to be used.

Identities under our control

Individuals must be able to selectively disclose their identity, generate new identities, pseudonyms, and/or remain anonymous. 

We should know all our data and profiles

Individuals need to have full insight into their profiles. This includes full access to derived, inferred and predicted data about them.