Machine Learning

Everyday activities are more and more shifting to a digital environment. Digital gadgets such as smartphones and werable devices are becoming inseparable part of our lives promising mostly convenience. New digital technologies have been mainly seen as empowering technologies for the users. FitBit, for example, is claimed to be a motivating device to lead a healthy and active life enabling users to achieve their goals analysing their data [1]. The data collected by this kind of devices include sleeping patterns, the number of steps, the amount of time they are engaged in physical activities and so forth. However, these data are not available just to the users but also to companies that can use them for multiple purposes. Health insurance companies, such as Vitality [2], already exploit their customers’ data in an exchange of rewards such as free tickets to the cinema or hot beverages. The potential implications of the collection and manipulation of personal data on a personal and societal level though have been downgraded. Just imagine a National Health insurance business model that operates on the basis of the classification of citizens as high- or low- risk based on their data [3]. Citizens profiled as low-risk will be granted with lower health contributions, while high-risk profiled citizens will be paying expensive and unaffordable plans.

Imagine a society where decisions on public well-being, education and so forth will be dependent on algorithmic predictions. Cathy O’Neil’s book Weapon of Math Destruction; How Big Data Increases Inequality and Threatens Democracy explores exactly these societal consequences emerging from the abuse of big data predictions.

O’Neil gives insights of how algorithms can be misused in the sake of convenience and cost efficiency resulting in practices of discrimination and bias, amplifying inequality and threatening ultimately Democracy. Her book is written for the lay public drawing though upon her academic expertise and her working experience in the financial sector. O’Neil after earning a PhD in Mathematics at Harvard, worked for the D. E. Shaw hedge fund when she initially felt a sense of disillusionment towards mathematics for their part in the financial crisis in the U.S. in 2008. The financial sector was relying on algorithmic models based on mathematical formulas that, using her words, “were more to impress than clarify”. It is when similar incomprehensible models got adopted into other sectors that she started investigating on the matter. 

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