Implications of big data for individuals (5)

 


Bias and Discrimination

Big data systems, which appear to be objective, are in reality capable of perpetuating bias and even extending it. Algorithms that are trained on old data can become like society prejudices and, consequently, unfairness can be the result in areas like hiring, lending, policing, and healthcare. For instance, a hiring algorithm that uses we can past hiring that reflect racial or gender biases may discriminate against certain groups without being aware of it.

On the other hand, one huge issue with the present situation is the lack of openness in the operations of algorithms. People do not understand why they were refused a loan or that they were not chosen for a job and because of this, they cannot find and challenge biased decisions. This goal of accountability is jeopardized due to this opacity, along with the difficulties in detecting and correcting discrimination.

Similar to these points is the fact that biased algorithms may impact marginalized groups in a disproportionate manner, and thus impact the social inequalities more than before. Even if the aims of the data applications are good, still they may fail if they are not designed carefully and then tested for fairness. In this context of big data positioning at the core of decision-making, the issue of algorithmic bias should be tackled seriously because it is the way for technological progress and sharing it with every community.

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