Inverse Reinforcement Learning (IRL) algorithms are closely related to Reinforcement Learning (RL) but instead try to model the reward function from a given set of expert demonstrations. In IRL, many algorithms have been proposed, but most assume consistent demonstrations. Consistency is the assumption that all demonstrations follow the same underlying reward function and near-optimal policy, without any contradictions. This, however, is not always the case. This study investigates the effect of conflicting demonstrations on IRL algorithms. For our experiments, the Lunar Lander environment and a grid-world environment are used in combination with a state-of-the-art IRL algorithm. To obtain the expert demonstrations, agents were trained using RL algorithms with explicit differences in the reward functions to achieve optimal policy. Then these demonstrations were used in training IRL in a variety of different configurations of hyperparameters. Our results show that IRL algorithms can be trained using demonstrations with varying levels of conflict. In conclusion, we demonstrate that IRL can learn even when provided with a set of conflicting demonstrations.