Precautionary Principle Revisited
Seasoned data scientists apply the "precautionary principle" - defined as considering the potential for causing harm when experience and knowledge is lacking. Yet taken to extremes the precautionary principle can disrupt life and society and cause significant economic, health, social cohesion and human rights damage. For example, during COVID19 the policy of draconian lockdowns caused massive damage for little, if any, benefits.
Moreover, innovation and life improvement depends on experimentation to find what works and what does not work. Incentives to innovate matter at both system and individual human levels. Caution and small scale experiments are usually prudent to avoid massive mistakes. Some things that work at small scales will work at larger scales - and others will not scale in size much, if at all. Experiments and experience applying the scientific method helps innovation to massively improve life for all.
Centralized versus decentralized decision making processes apply the precautionary principle in different ways that may affect innovation, quality of life and ability to experiment with fast self-correcting mechanisms to mitigate risk and potential harm. Taken to extremes the precautionary principle misapplied can itself cause both direct damage and massive invisible long-term harm. Incentives to innovate matter and big bang changes at the system level without small scale experimentation is usually a very bad idea.
Getting the right balance to prudently apply the precautionary principle is tricky. Accurate and dispassionate analysis is needed to avoid overestimation and underestimation of potential real-world risk and thus miscalculation that overly values safety yet retards innovation. At the system level, fear causes leaders and policy makers to concentrate on potential worst-case scenarios thus magnifying the power of potential risks at the expense of innovation, human rights and civil liberties. Significant disruption of normal life and economic activity is almost always a disaster at both group system and individual levels.
Professional data scientists apply the precautionary principle in a prudent, balanced way to both properly assess potential risk and harms and yet protect customary life and economic activity as well as incentives to innovate and safeguard human rights and civil liberties.