Explaining how your AI system is fair
To implement fair machine learning in a sustainable way, choosing the right fairness objective is key. Since fairness is a concept of justice which comes in various, sometimes conflicting definitions, this is not a trivial task though. The most appropriate fairness definition for an artificial intelligence (AI) system is a matter of ethical standards and legal requirements, and the right choice depends on the particular use case and its context. In this position paper, we propose to use a decision tree as means to explain and justify the implemented kind of fairness to the end users. Such a structure would first of all support AI practitioners in mapping ethical principles to fairness definitions for a concrete application and therefore make the selection a straightforward and transparent process. However, this approach would also help document the reasoning behind the decision making. Due to the general complexity of the topic of fairness in AI, we argue that specifying "fairness" for a given use case is the best way forward to maintain confidence in AI systems. In this case, this could be achieved by sharing the reasons and principles expressed during the decision making process with the broader audience.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingFairnessSimilar Papers 제목 키워드 기반
Everything is Relative: Understanding Fairness with Optimal Transport
To study discrimination in automated decision-making systems, scholars have proposed several definitions of fairness, each expressing a different fair ideal. These definitions require practitioners to make complex decisi…
Decision MakingFairnessAppropriate Fairness Perceptions? On the Effectiveness of Explanations in Enabling People to Assess the Fairness of Automated Decision Systems
It is often argued that one goal of explaining automated decision systems (ADS) is to facilitate positive perceptions (e.g., fairness or trustworthiness) of users towards such systems. This viewpoint, however, makes the …
FairnessExplainability for fair machine learning
As the decisions made or influenced by machine learning models increasingly impact our lives, it is crucial to detect, understand, and mitigate unfairness. But even simply determining what "unfairness" should mean in a g…
AttributeBIG-bench Machine LearningFairnessYour fairness may vary: Pretrained language model fairness in toxic text classification
Warning: This paper contains samples of offensive text. The popularity of pretrained language models in natural language processing systems calls for a careful evaluation of such models in down-stream tasks, which have …
FairnessLanguage ModelingLanguage Modellingtext-classification+1Clip Your Sequences Fairly: Enforcing Length Fairness for Sequence-Level RL
We propose FSPO (Fair Sequence Policy Optimization), a sequence-level reinforcement learning method for LLMs that enforces length-fair clipping on the importance-sampling (IS) weight. We study RL methods with sequence-le…
Reinforcement Learning