In the practice of multi-criteria decision analysis, biased responses to the preference elicitation questions may impact the outcome of the process. In particular, there is a risk that the effects of biases accumulate in favor of a single alternative or a subset of alternatives. In this paper, we develop new bias mitigation techniques for multi-criteria decision analysis, which are based on the idea that the effects of biases can cancel out each other in the preference elicitation process. The benefits of the techniques include that the decision maker does not need to try to change her behavior to avoid biases, and there are no numerical adjustments of her judgements. The new techniques that we propose are: (1). Introducing a virtual reference alternative in the decision problem. (2). Introducing an auxiliary measuring stick attribute. (3). Rotating the reference point. (4). Restarting the decision process at an intermediate step with a reduced set of alternatives. We simulate computationally how these techniques help mitigate biases in the Even Swaps process when the decision maker exhibits the loss aversion bias, the measuring stick bias, and makes random response errors. The techniques can also be applied in weight elicitation using the SWING and trade-off methods to reduce the aforementioned biases.