The implementation of automation technologies in the workplace is affected by the saturation of fake news and exaggerated promises, creating a technological imagination disconnected from labor reality. In contrast, in the scientific field, there is the notion of 'convergent evidence,' which involves multiple studies and disciplines pointing toward the same conclusion. This more rigorous and ethical approach seeks to structure the debate based on cumulative facts rather than close it.
In the context of advancements in artificial intelligence, it is crucial for society to distinguish between convergent evidence and convincing rhetoric. Often, claims about the benefits of automation are supported by non-replicable internal studies or weak correlations. This lack of rigor is exploited in technology marketing, creating a perception of inevitability around such technologies.
'Convergent evidence' is key to rigorous scientific thinking, but misunderstood concepts linger around popular and business discourse, replacing this notion with expressions like 'convergent evidence.' This confusion is reflected in the presentation of 'convergence' as synonymous with success, regardless of the strength of the claims behind it.
In a scenario where artificial intelligence permeates decision-making processes in society, it is essential to differentiate between convenient narratives and solid evidence. A critical approach, based on the convergence of data and openness to revision, is fundamental for AI to contribute to progress without generating labor alienation. Longitudinal studies reveal complex and disparate effects of automation, with reductions in workforce in many sectors without a clear redistribution of the generated value.
The lack of scientific scrutiny has generated a 'pseudo-consensus' that influences political, labor, and investment decisions, based on an alleged unassailable authority. It is crucial to adopt a technology narrative backed by convergent evidence to build a discourse grounded and resistant to misinformation. The need to demand precision, replicability, and transparency in scientific claims is vital in a context where 'convergent evidence' has become more of a rhetorical tool than an epistemic one.
