Previous research on expert advice-taking shows that humans exhibit two
contradictory behaviors: on the one hand, people tend to overvalue their own
opinions undervaluing the expert opinion, and on the other, people often defer
to other people's advice even if the advice itself is rather obviously wrong.
In our study, we conduct an exploratory evaluation of users' AI-advice
accepting behavior when evaluating the truthfulness of a health-related
statement in different "advice quality" settings. We find that even feedback
that is confined to just stating that "the AI thinks that the statement is
false/true" results in more than half of people moving their statement veracity
assessment towards the AI suggestion. The different types of advice given
influence the acceptance rates, but the sheer effect of getting a suggestion is
often bigger than the suggestion-type effect.

我们研究了用户在评估健康相关陈述的真实性时，对人工智能建议接受行为的探索性评估。我们发现，即使仅仅是 “AI 认为该陈述为真实 / 虚假” 的反馈，也会导致超过一半的用户将其陈述真实性评估移向 AI 建议。给出不同类型的建议会影响接受率，但仅仅得到建议本身的影响通常比建议类型的影响更大。

无论原因，对健康相关询问的 AI 事实核查与 AI 权威

Right, No Matter Why: AI Fact-checking and AI Authority in  Health-related Inquiry Settings

This article describes an approach to incorporate expert opinion on
observable quantities through the use of a loss function which updates a prior
belief as opposed to specifying parameters on the priors. Eliciting information
on observable quantities allows experts to provide meaningful information on a
quantity familiar to them, in contrast to elicitation on model parameters,
which may be subject to interactions with other parameters or non-linear
transformations before obtaining an observable quantity. The approach to
incorporating expert opinion described in this paper is distinctive in that we
do not specify a prior to match an expert's opinion on observed quantity,
rather we obtain a posterior by updating the model parameters through a loss
function. This loss function contains the observable quantity, expressed a
function of the parameters, and is related to the expert's opinion which is
typically operationalized as a statistical distribution. Parameters which
generate observable quantities which are further from the expert's opinion
incur a higher loss, allowing for the model parameters to be estimated based on
their fidelity to both the data and expert opinion, with the relative strength
determined by the number of observations and precision of the elicited belief.
Including expert opinion in this fashion allows for a flexible specification of
the opinion and in many situations is straightforward to implement with
commonly used probabilistic programming software. We highlight this using three
worked examples of varying model complexity including survival models, a
multivariate normal distribution and a regression problem.

本文章提出了一种采用损失函数更新引入专家意见的先验概率而非指定先验概率参数可观测量的方法，该方法不仅能够简便地将专家的意见纳入，同时也对专家提供的信息进行了更具体的处理，使得模型参数不但更好地拟合数据，也更符合实际情况。

将专家意见纳入统计模型的可观察量中 -- 通用框架

Incorporating Expert Opinion on Observable Quantities into Statistical Models -- A General Framework

We study the experimentation dynamics of a decision maker (DM) in a two-armed
bandit setup (Bolton and Harris (1999)), where the agent holds ambiguous
beliefs regarding the distribution of the return process of one arm and is
certain about the other one. The DM entertains Multiplier preferences a la
Hansen and Sargent (2001), thus we frame the decision making environment as a
two-player differential game against nature in continuous time. We characterize
the DM value function and her optimal experimentation strategy that turns out
to follow a cut-off rule with respect to her belief process. The belief
threshold for exploring the ambiguous arm is found in closed form and is shown
to be increasing with respect to the ambiguity aversion index. We then study
the effect of provision of an unambiguous information source about the
ambiguous arm. Interestingly, we show that the exploration threshold rises
unambiguously as a result of this new information source, thereby leading to
more conservatism. This analysis also sheds light on the efficient time to
reach for an expert opinion.

本研究通过对 DM 在双臂赌博环境中的实验动态进行研究，明确了决策者面临的实验决策最优策略，同时通过提供信息源，揭示了对于游走在不确定性中的一臂的阈值会上升，导致更多的保守性，并且为专家意见的获取提供了便利。