Should we Trust the Wisdom of Crowd: Crowd-Sourcing?

Should we Trust the Wisdom of Crowd: Crowd-Sourcing?






The Wisdom of Crowds

In my own opinion, I tend to think that crowds should never be trusted given the fact that there is an assumption that crowd means everyone in any place in the community. It can prove a great disaster when you wish to crowd-source without first determining who actually should be brought to book and get involved in the matter otherwise the crowd will be a disappointment. It is important to highlight that crowd sourcing is something more than a suggestion box. It is important that all the details and the basic ideas be selected intelligently from a technocratic point of view before the wisdom of the crowd is asked for. The crowd is prone to manipulation, persuasion and lies which compromises the knowledge of an expert scrutiny (Quinn& Bederson, 2011).

One of the major ways through which the internet has influenced the power and pitfalls of crowd sourcing is over-inflation. It is a fact that presently, the internet has been contaminated by grade inflation. The biases in the internet have become prominent with the fact that the opinions and views of independent individualsand everyone are not listened to(Quinn& Bederson, 2011). Reasoning of the crowd does not allow any descending opinion to be put across irrespective of how logical it may be. It is therefore very clear that internet out-sourcing has a lot of pitfalls some of which include lack of confidentiality, lack of direction establishment, some of the good talents are missed out, and misleads that arises from popularity battles among other disadvantages. It is therefore important to conclude that we should never trust the wisdom of the crowd because its demerits outweigh its merits (Skene, 2009).


Quinn, A. & Bederson, B. (2011). “Human computation: a survey and taxonomy of a growing field,” inProceedings of the 2011 Annual Conference on Human Factors in Computing Systems (CHI), pp.1403–1412.

Skene, M. (2009).Maximum likelihood estimation of observer error-rates using the emalgorithm. Applied Statistics, 28(1): 20–28,


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