Argumentation is a formalism allowing to reason with contradictory information by modeling arguments and their interactions. There are now an increasing number of gradual semantics and impact measures that have emerged to facilitate the interpretation of their outcomes. An impact measure assesses, for each argument, the impact of other arguments on its score. In this paper, we refine an existing impact measure from Delobelle and Villata and introduce a new impact measure rooted in Shapley values. We introduce several principles to evaluate those two impact measures w.r.t. some well-known gradual semantics. This comprehensive analysis provides deeper insights into their functionality and desirability.

The problem of artificial intelligence safety can be seen as can be seen as ensuring an agent with the power of causing harm chooses to not do so. In the limit, the agent can be powerful enough that causing existential catastrophe is within its limit, and it has incentives to doing so [6], so our task is to guarantee that it chooses not to. A possible approach is penalize changes in the world caused by agent, leading to the agent not causing catastrophe because that leads to large changes in the world[24]. The hope is that this is a relatively easy objective to align the agent with, as opposed to aligning it with the full range of human values. So, our desideratum is that the AI achieves something while doing as little in the world as possible .