Algorithm aversion occurs when humans are reluctant to use algorithms despite their superior performance. Studies show that giving users outcome control by providing agency over how models' predictions are incorporated into decision-making mitigates algorithm aversion. We study whether algorithm aversion is mitigated by process control, wherein users can decide what input factors and algorithms to use in model training. We conduct a replication study of outcome control, and test novel process control study conditions on Amazon Mechanical Turk (MTurk) and Prolific. Our results partly confirm prior findings on the mitigating effects of outcome control, while also forefronting reproducibility challenges. We find that process control in the form of choosing the training algorithm mitigates algorithm aversion, but changing inputs does not. Furthermore, giving users both outcome and process control does not reduce algorithm aversion more than outcome or process control alone. This study contributes to design considerations around mitigating algorithm aversion.

翻译：算法厌恶指人类在算法表现更优时仍不愿使用算法的现象。研究表明，赋予用户结果控制权——即让用户决定如何将模型预测融入决策过程——可缓解算法厌恶。本文探究过程控制（用户可决定模型训练所用的输入特征与算法）能否同样缓解算法厌恶。我们开展了一项结果控制的复制研究，并在Amazon Mechanical Turk与Prolific平台上测试了新型过程控制实验条件。研究结果部分验证了结果控制具有缓解效应的既有结论，同时揭示了可重复性挑战。我们发现，以选择训练算法形式呈现的过程控制能缓解算法厌恶，但改变输入特征则无此效果。此外，同时赋予用户结果控制与过程控制，其缓解效果并未优于单独采用其中任一控制方式。本研究为缓解算法厌恶的设计策略提供了参考依据。