Denial of justice in international arbitration is a violation that investors are increasingly resorting to. Given the increase in this type of claims, the application of neural networks and data analysis to study the consequences of denial of justice in international arbitration is relevant. It is notable that States prevail in cases of denial of justice, but by a minimal margin. Moreover, in studying the most relevant arbitral awards arising from Lion v. Mexico, the discussion surrounding denial of justice does not produce a uniform view among arbitral tribunals. Consistently, arbitrators rely on a variety of authors and precedents on denial of justice that do not necessarily reflect by themselves a favorable picture in terms of diversity and inclusion. Nevertheless, data analysis allows for a scholarly tracing of authors with diverse legal perspectives on denial of justice, which may assist in resolving some of the unknowns raised by this article.