Devices are planted in our houses; we are monitored, experiences turned into behavioural data (Farooq et al., 2015). However, this is no longer enough; corporations, world governments are using machine learning algorithms to create predictive models for ‘nudge-economy’, and herd mass manipulation. (Hrnjic & Tomczak, 2019)

Machined Surveillance is an investigative series into the Machine Learning augmented surveillance capabilities of governments & cooperations, their capabilities to gain insight and generate action-points. This series also covers the creation of deepfakes and their underlying datasets. To address the question, “How are the general populous able to utilise Machine-learning algorithms to aid in counter-surveillance techniques?”. However, in order to answer this, the following needs to be addressed – “What does the machine know about me from my data?”, “What do machines see versus what humans see?”; “Do you believe what you see?”.

 

References:

U Farooq, M. et al., 2015. A Review on Internet of Things (IoT). International Journal of Computer Applications, 113(1), pp.1—7.

Hrnjic, E. & Tomczak, N., 2019. Machine learning and behavioral economics for personalized choice architecture. arXiv.org, econ.GN.