  As AI takes on a greater role in the modern world, it is essential to ensure
that AI models can overcome decision uncertainty and remain aligned with human
morality and interests. This research paper proposes a method for improving the
decision-making of language models (LMs) via Automated Parliaments (APs) -
constructs made of AI delegates each representing a certain perspective.
Delegates themselves consist of three AI models: generators, modifiers, and
evaluators. We specify two mechanisms for producing optimal solutions: the
Simultaneous Modification mechanism for response creation and an evaluation
mechanism for fairly assessing solutions. The overall process begins when each
generator creates a response aligned with its delegate's theory. The modifiers
alter all other responses to make them more self-aligned. The evaluators
collectively assess the best end response. Finally, the modifiers and
generators learn from feedback from the evaluators. In our research, we tested
the evaluation mechanism, comparing the use of single-value zero-shot prompting
and AP few-shot prompting in evaluating morally contentious scenarios. We found
that the AP architecture saw a 57.3% reduction in its loss value compared to
the baseline. We conclude by discussing some potential applications of APs and
specifically their potential impact when implemented as Automated Moral
Parliaments.
