Over-reliance on Trust and Lack of Objectivity

The present system is insufficiently objective as at each stage it relies on certain assumptions, trust and good faith.
Over-reliance of trust on authors:  In the current system, authors wishing to publish are unable to be fully objective with their own research, and may overestimate the validity of their data in order to craft a story that is deemed suitable or novel enough for publication. Since most of the data that are obtained by the researchers/authors are in the centralized system such as their local servers, computers, data can be massaged, manipulated or mutated to sell the best narrative. Authors are also trusted that they remain objective in the face of hypothesis-defying results and interpret the date carefully and objectively. While crowdsourced wisdom is becoming thing of importance in science, it is more of an exception than norm and as a result, the trust is placed in a centralized manner on authors that they perform and publish science objectively. However, some scientists do take advantage of the system to manipulate data to varying degrees and thus corrupt the system. Scientists feel the pressure to omit ‘inconvenient truths’ that interfere with their developing storyline as the system demands stories, rather than science to be published.This also leads to irreproducibility. Estimates are that around ⅔ of the published work are not reproducible and that some of the work are results of fraudulence (citation: NYTimes, Economist)
Over-reliance on the peer review system: Peer review is fundamental to scientific research and publishing, distinguishing it from virtually all other forms of output. It forms the basis for quality control of academic research, by having scientists in the same field review each other's work and provide feedback to help decide if the research should be published or not. However, at the present it is unclear whether it meets these simple functions, and there is a large potential scope for disruptive innovation in this area (e.g., Tennant et al., 2017).