Privacy policies are expected to inform data subjects about their data
protection rights. They should explain the data controller's data management
practices, and make facts such as retention periods or data transfers to third
parties transparent. Privacy policies only fulfill their purpose, if they are
correctly perceived, interpreted, understood, and trusted by the data subject.
Amongst others, this requires that a privacy policy is written in a fair way,
e.g., it does not use polarizing terms, does not require a certain education,
or does not assume a particular social background. In this work-in-progress
paper, we outline our approach to assessing fairness in privacy policies. To
this end, we identify from fundamental legal sources and fairness research, how
the dimensions informational fairness, representational fairness and
ethics/morality are related to privacy policies. We propose options to
automatically assess policies in these fairness dimensions, based on text
statistics, linguistic methods and artificial intelligence. Finally, we conduct
initial experiments with German privacy policies to provide evidence that our
approach is applicable. Our experiments indicate that there are indeed issues
in all three dimensions of fairness. For example, our approach finds out if a
policy discriminates against individuals with impaired reading skills or
certain demographics, and identifies questionable ethics. This is important, as
future privacy policies may be used in a corpus for legal artificial
intelligence models.

本研究论文介绍了一种评估隐私政策公平性的方法，基于文本统计、语言学方法和人工智能，从信息公平性、表征公平性和伦理 / 道德等方面自动评估隐私政策。初步实验结果发现隐私政策存在信息公平性、表征公平性和伦理等方面的问题。

法律约束力但不公平吗？评估隐私政策的公平性

Legally Binding but Unfair? Towards Assessing Fairness of Privacy  Policies

Counterfactual explanations (CFEs) exemplify how to minimally modify a
feature vector to achieve a different prediction for an instance. CFEs can
enhance informational fairness and trustworthiness, and provide suggestions for
users who receive adverse predictions. However, recent research has shown that
multiple CFEs can be offered for the same instance or instances with slight
differences. Multiple CFEs provide flexible choices and cover diverse
desiderata for user selection. However, individual fairness and model
reliability will be damaged if unstable CFEs with different costs are returned.
Existing methods fail to exploit flexibility and address the concerns of
non-robustness simultaneously. To address these issues, we propose a
conceptually simple yet effective solution named Counterfactual Explanations
with Minimal Satisfiable Perturbations (CEMSP). Specifically, CEMSP constrains
changing values of abnormal features with the help of their semantically
meaningful normal ranges. For efficiency, we model the problem as a Boolean
satisfiability problem to modify as few features as possible. Additionally,
CEMSP is a general framework and can easily accommodate more practical
requirements, e.g., casualty and actionability. Compared to existing methods,
we conduct comprehensive experiments on both synthetic and real-world datasets
to demonstrate that our method provides more robust explanations while
preserving flexibility.

通过 Counterfactual Explanations with Minimal Satisfiable Perturbations (CEMSP) 提供更健壮的解释，同时保留灵活性，以应对多个 Counterfactual explanations 可能导致的不稳定性问题。

灵活稳健的反事实解释与最小满足扰动

Flexible and Robust Counterfactual Explanations with Minimal Satisfiable  Perturbations

Automated decision systems (ADS) are increasingly used for consequential
decision-making. These systems often rely on sophisticated yet opaque machine
learning models, which do not allow for understanding how a given decision was
arrived at. In this work, we conduct a human subject study to assess people's
perceptions of informational fairness (i.e., whether people think they are
given adequate information on and explanation of the process and its outcomes)
and trustworthiness of an underlying ADS when provided with varying types of
information about the system. More specifically, we instantiate an ADS in the
area of automated loan approval and generate different explanations that are
commonly used in the literature. We randomize the amount of information that
study participants get to see by providing certain groups of people with the
same explanations as others plus additional explanations. From our quantitative
analyses, we observe that different amounts of information as well as people's
(self-assessed) AI literacy significantly influence the perceived informational
fairness, which, in turn, positively relates to perceived trustworthiness of
the ADS. A comprehensive analysis of qualitative feedback sheds light on
people's desiderata for explanations, among which are (i) consistency (both
with people's expectations and across different explanations), (ii) disclosure
of monotonic relationships between features and outcome, and (iii)
actionability of recommendations.

本研究通过人体实验来评估不同的信息类型和信息量对自动贷款批准领域的 ADS（Automated Decision System）的信息公正性和值得信赖度的影响，发现信息量和人的人工智能素养显著影响信息公正性，从而影响 ADS 的可信度。同时，对资料的详尽性、一致性、披露单调关系以及建议的可操作性等方面提出明确的要求。

信息不足”：解释对自动化决策中信息公平与可信度知觉的影响

"There Is Not Enough Information": On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Making