On Detecting Bias in Smart Contracts Using Supervisor Synthesis

Paper i proceeding, 2026

Smart contracts are computer programs executing in a blockchain environment, that can enforce agreements among mutually distrusting users without the involvement of any trusted third party. However, smart contracts may implement bias towards or against certain users, either intentionally or mistakenly by improper coding. Such bias may also manifest through multiple users colluding to put other users at a disadvantage. This study proposes a methodology to use supervisor synthesis to detect such biases in smart contracts. Given an extended finite-state machine model of a smart contract, by selecting which events are considered to be controllable, the perspective from which bias is considered can be decided, and by marking states, positive as well as negative bias can be captured. The existence of a supervisor may then reveal the existence of bias. The method is demonstrated on a multi-player game-based smart contract. The analysis shows that there does not exist positive bias towards any single player, that is, there is no guaranteed winning strategy for any single player. However, colluding players may enact negative bias against the other players, thus, there is negative bias against other players so that they may lose the game irrespective of their actions.