Why is it More Common for Bias to be Called on Negative Rather than Positive Articles?

Executive Summary

  • The term bias is often used as a critique against an article or argument — however, it is normally used against arguments where the conclusion is negative.


This comment was taken from a LinkedIn discussion in response to our article Is Blockchain All that Innovative or is it Overhyped?

I have seen many good applications of the technology in real life, and it is worth to consider in scenarios where you don’t trust other parties involved. I agree with that was a hype and blockchain partner companies are able to propose a solution using it for most of the problems you can think on. For me the you should have to investigated better the companies that you are judging and provide some more arguments and back up facts, right now read as a bit biased for me.

Our References for This Article

If you want to see our references for this article and related Brightwork articles, see this link.

Here is my response to this comment.

Proving Bias

So let us review your specific critiques.

a.) You have seen many good applications of blockchain in real life, and it is worth considering in scenarios where you don’t trust other parties involved.

This is difficult to respond to because it is a personal experience that is unspecific and not documented.

b.) You agree there is hype in blockchain and that it is overapplied.

So that means that you agree with the primary conclusion of the article.

Hype is one term, but lies is another term. The article provides good evidence that major IT entities are lying about blockchain.

c.) You would have liked it if I investigated the companies making the claims.

I just added the Brightwork Consulting Article Library link to the article.

The article provides evidence (the quotes from related articles) that blockchain has a poor implementation history. The things being said about blockchain don’t make sense. This is one of the primary methods used in the article. Not making sense, claims — like those made by SAP that describes an application that is not blockchain, as doing “blockchain” things is one example of this.

d.) The article reads as biased to you.

To establish bias, a good strategy would be able to contradict the arguments made in the article. So if, for example, it could be shown that SAP TM is blockchain, or that blockchain has revolutionized banking — but I did not acknowledge these facts, or that the WiPro rep was not making false claims about Bermuda has being a “high regulatory” environment. However, if the observations are true, and are not contested, then the argument that I have a built-in bias against blockchain is more difficult to establish.


Brightwork receives no money from vendors and has no financial incentive to promote or contradict blockchain. If we compare Brightwork’s bias to the bias of the entities listed in this article — that are actively trying to sell blockchain projects and software — it would seem that the place to observe bias is not with me but with these major IT entities.

However, in many promotional articles about blockchain, I have almost never seen the author accused of bias — even when they have a quota to sell blockchain software or services.

This fits into a pattern I have observed where bias tends to be called out if the article contradicts (i.e., is a negative conclusion). If the article is promotional (i.e., a positive conclusion), then bias is far less likely to be asserted in the comments. And it is not only accusations of bias.

It is also the interest level in the article. If I publish a thoroughly researched article, which calls into question AI/ML claims, it will get far less interest.

But I am increasingly coming to the view that a small fraction of the overall population cares what is true. Most people that are working are just working to make money and to attain status in their field. That sounds like a strange thing to say — however, what I mean is that they interpret every statement or assertion not based upon whether it is true or false, or has evidence or does not have proof, but on whether it makes them more money or less money. I recently shared an article How Common is Research/IP theft in IT, that covers the high degree of research theft and lack of references/attribution in private research. It was one of my worst performing shares.


Well, it does not translate to $$$ but goes to the integrity of private research. If I publish an article on how much opportunity there is in AI/ML, I will get large numbers of likes, even with putting little effort into the article. People are searching for ways they can make money — not ways they can find out what is true. Work is the place where you “make money,” it is not, too many people, a place where you look for what is true.