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In 2015, Brian Nosek and 269 co-authors attempted to reproduce the findings from 100 prominent psychology papers. The authors were only successful in 39 of the 100 attempts. In other words, 61 per cent of the original results could not be replicated.
These alarming conclusions stimulated what soon became known as the “reproducibility crisis” in science. In many ways, it was a long overdue realisation. A decade earlier, a Professor at Stanford found that “it is more likely for a research claim to be false than true”. In 2012, a separate group of researchers evaluated 53 renowned cancer studies published in top-tier journals, finding that only six could be reproduced.
These and other reports confirmed what most in the research community already suspected and had already experienced first-hand. Even for the general public, the phenomenon of flawed research is nothing new. Many will remember the MMR vaccine controversy – a study in The Lancet claiming the MMR vaccine caused autism. While the paper was eventually retracted in light of multiple undeclared conflicts of interest as well as manipulated evidence, the damage had already been done; the results of the study had reached the minds of the general public, and a small but significant subsection of society continues to cling to it as truth.
True, this can be seen as an extreme example, but it illustrates a valuable point: the scientific ecosystem is remarkably slow to self-correct. Flawed papers can and do slip through the editorial and peer review process. By the time bad papers are disproved, they’ve already caused irreparable damage.
Once in the bloodstream of published content, inaccurate research can easily metastasise into new research, and may even continue to be cited following retraction. The world’s ten most ‘popular’ retracted papers have been cited on over 7,500 occasions, and that’s a conservative estimate. Research, including bad research, spreads like wildfire.
The problem is, in part, intrinsic to science itself. It’s fundamentally reliant on interconnectedness. New science is built upon the results of older science, and citations abound. It’s a wholly a posteriori network. That level of interdependence makes it vulnerable. If original evidence is flimsy, then corollary research, by extension, also becomes flawed.
But that interdependent nature is also an opportunity. It’s a system ripe for disruption – highly suitable for a technology that has only been around for just under a decade: blockchain.
Blockchain is, in simple terms, a decentralised database which is open to anyone. It has the potential to reshape various business models in the majority industry verticals, and is most commonly referenced in relation to financial services and the supply chain space. But it’s academic research where blockchain has some of its greatest potential, specifically as a solution to the problem of trust.
Scientific knowledge is arguably the ultimate decentralised system, particularly as we have transitioned from analogue into digital. In essence, it is not controlled by a central agent and is, by and large, independent. It demands public scrutiny and constant challenge, it is valuable for a large and fast-growing community, and it has numerous practical uses within the scientific research industry.
Right now, for example, getting a study published rests firmly on the peer-review process. A handful of experts will quickly read a study, offer advice, and recommend whether it should be published. But as poor reproducibility statistics have gone to show, it’s a highly fallible process. Reviewers are under considerable time pressure. Reliability is difficult to gauge. The problem of bias is troublesome.
With blockchain, every bit of that process could be made transparent, and more than a select few would be able to read the study to assess its validity. The issue of time-pressure would be all but negated because peer reviewers could assess research papers at their leisure. With papers side-by-side on a single database, and with the assistance of artificial intelligence, reliability becomes easier to establish. The influence of human bias would abate through the sheer volume of peer reviewers, and the cancellation of these biases in aggregation.
Blockchain also provides a useful way to validate knowledge dynamically, subsequent to publication, especially so in combination with powerful AI tools such as Iris.ai. Science is mercurial: new information constantly arises casting doubt on older items of research, but existing publishing practices lack the proper tools to accommodate these changes in literature – retractions, replications, new findings, and so on. With blockchain, the peer review process could become continuous. Rather than a select few reviewers deciding at an arbitrary moment after publication that research is ‘valid’, blockchain would allow for an ongoing process of research appraisal.
Finally, blockchain offers the potential of building entirely new economic models through issuing tokens, or digital currency, tied to the value of what the community is building: a currency tied to the most precious thing we humans possess: knowledge. With the incentives of tokenised economies, frequently found in blockchain models, accurate peer reviewing, and accurate research papers, could be rewarded.
An open, scalable, decentralised platform, backgrounded by blockchain, thus offers an optimal way to fix distortions that beget inaccuracies and fuel distrust in scientific knowledge generation and dissemination worldwide. A community-run engine capable of checking the underlying factual base of a given input text, provides us with a unique opportunity to unbias our entire knowledge base, and doing so through a new prism built with the highest transparency and accountability standards.
This is what we’re doing with Project Aiur. We plan to democratise science through blockchain-enabled disintermediation. In short, we want to use blockchain to promote reliable research and sort fact from fiction. This is, fundamentally, what science is all about.