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In the current economic scenario characterized by turbulence, innovation is a requisite for company's growth. The innovation activities are implemented through the realization of innovative project. This paper aims to prospect the promising opportunities coming from the application of Machine Learning (ML) algorithms to project risk management for organizational innovation, where a large amount of data supports the decision-making process within the companies and the organizations.

Moving from a structured literature review (SLR), a final sample of 42 papers has been analyzed through a descriptive, content and bibliographic analysis. Moreover, metrics for measuring the impact of the citation index approach and the CPY (Citations per year) have been defined. The descriptive and cluster analysis has been realized with VOSviewer, a tool for constructing and visualizing bibliometric networks and clusters.

Prospective future developments and forthcoming challenges of ML applications for managing risks in projects have been identified in the following research context: software development projects; construction industry projects; climate and environmental issues and Health and Safety projects. Insights about the impact of ML for improving organizational innovation through the project risks management are defined.

The study have some limitations regarding the choice of keywords and as well the database chosen for selecting the final sample. Another limitation regards the number of the analyzed papers.

The analysis demonstrated how much the use of ML techniques for project risk management is still new and has many unexplored areas, given the increasing trend in annual scientific publications. This evidence represents an opportunities for supporting the organizational innovation in companies engaged into complex projects whose risk management become strategic.

The study aims to highlight and reflect on resistance to Brazil's illiberal accelerationist politics highlighting alternative possibilities based on affects and forms of relatedness.

Drawing on the case of public universities and the arts in today's Brazil, the authors point out a tragedy of resistance (when opposing change fuels its acceleration) and explore a strategy of lines of flight and becomings in the light of Deleuze and Guattari's perspective on acceleration.

Alongside an oppositional and reactive resistance, that is caught in a tragedy of resistance, the authors explore an alternative strategy that protects a plurality of life forms and forces and their becoming. This strategy differs from most critiques of accelerationism.

This strategy of resistance seems more faithful to Deleuze than the accelerationist strategies that claim to be inspired by him. The authors suggest another reading of the often quoted passage by Deleuze and Guattari. While Deleuze and Guattari favor continuous deterritorializations of the flows of desire, accelerationism reterritorializes these flows towards a (often) undesirable future.