Reinforcement learning (RL) has shown promise as a tool for engineering safe,
ethical, or legal behaviour in autonomous agents. Its use typically relies on
assigning punishments to state-action pairs that constitute unsafe or unethical
choices. Despite this assignment being a crucial step in this approach,
however, there has been limited discussion on generalizing the process of
selecting punishments and deciding where to apply them. In this paper, we adopt
an approach that leverages an existing framework -- the normative supervisor of
(Neufeld et al., 2021) -- during training. This normative supervisor is used to
dynamically translate states and the applicable normative system into
defeasible deontic logic theories, feed these theories to a theorem prover, and
use the conclusions derived to decide whether or not to assign a punishment to
the agent. We use multi-objective RL (MORL) to balance the ethical objective of
avoiding violations with a non-ethical objective; we will demonstrate that our
approach works for a multiplicity of MORL techniques, and show that it is
effective regardless of the magnitude of the punishment we assign.

本文展示了一种使用利用现有的 normative supervisor 框架的方法，通过 MORL 技术以平衡非道德目标和避免违规的道德目标，在自主代理中实现安全、道德或合法行为，且不受惩罚强度大小的影响。

可证明符合性引导的强化学习

Reinforcement Learning Guided by Provable Normative Compliance

This paper stresses the importance of biases in the field of artificial
intelligence (AI) in two regards. First, in order to foster efficient
algorithmic decision-making in complex, unstable, and uncertain real-world
environments, we argue for the structurewise implementation of human cognitive
biases in learning algorithms. Secondly, we argue that in order to achieve
ethical machine behavior, filter mechanisms have to be applied for selecting
biased training stimuli that represent social or behavioral traits that are
ethically desirable. We use insights from cognitive science as well as ethics
and apply them to the AI field, combining theoretical considerations with seven
case studies depicting tangible bias implementation scenarios. Ultimately, this
paper is the first tentative step to explicitly pursue the idea of a
re-evaluation of the ethical significance of machine biases, as well as putting
the idea forth to implement cognitive biases into machines.

本文阐述了人类认知偏差在人工智能领域中的重要性，并提出了在学习算法中结构上实施认知偏差以及为选择具有道德价值的社会或行为特征的偏向训练刺激应用过滤机制，结合七个具体的偏见实现情景案例，将来自认知科学和伦理学的启示应用于人工智能领域，是对重新评估机器偏见道德意义的探索和实施认知偏差到机器中的首次尝试。

为何需要有偏倚的人工智能 —— 如何通过引入认知和伦理机器偏差来增强人工智能系统

Why we need biased AI -- How including cognitive and ethical machine biases can enhance AI systems

A new wave of decision-support systems are being built today using AI
services that draw insights from data (like text and video) and incorporate
them in human-in-the-loop assistance. However, just as we expect humans to be
ethical, the same expectation needs to be met by automated systems that
increasingly get delegated to act on their behalf. A very important aspect of
an ethical behavior is to avoid (intended, perceived, or accidental) bias. Bias
occurs when the data distribution is not representative enough of the natural
phenomenon one wants to model and reason about. The possibly biased behavior of
a service is hard to detect and handle if the AI service is merely being used
and not developed from scratch, since the training data set is not available.
In this situation, we envisage a 3rd party rating agency that is independent of
the API producer or consumer and has its own set of biased and unbiased data,
with customizable distributions. We propose a 2-step rating approach that
generates bias ratings signifying whether the AI service is unbiased
compensating, data-sensitive biased, or biased. The approach also works on
composite services. We implement it in the context of text translation and
report interesting results.

研究新一波的决策支持系统，通过 AI 服务从数据获取信息并将其融入人机协作，提出采用第三方评级机构来检测并处理服务的可能偏倚行为，以保证其客观性和可靠性。