Climbing the Ladder: How Agents Reach Counterfactual Thinking

Abstract

We increasingly rely on automated decision-making systems to search for information and make everyday choices. While concerns regarding bias and fairness in machine learning algorithms have high resonance, less addressed is the equally important question of to what extent we are handing our own role of agents over to artificial information-retrieval systems. This paper aims at drawing attention to this issue by considering what agency in decision-making processes amounts to. The main argument that will be proposed is that a system needs to be capable of reasoning in counterfactual terms in order for it to be attributed agency. To reach this step, automated system necessarily need to develop a stable and modular model of their environment.