This article tackles the challenge of blending incentives for selfish behavior with complex computational requirements. Traditional methods for designing fair systems don't mesh well with efficient algorithms. By shifting focus to a Bayesian system where telling the truth is the best strategy, the researchers found a way to create systems that work well in real-life scenarios. They developed a method that transforms any approximation algorithm into one that encourages honesty among users, ensuring a close match to the best possible outcome. This innovative approach improves the efficiency and fairness of mechanisms in situations involving self-interested individuals.