The role of explainable AI in the research field of AI ethics
H Vainio-Pekka, MOO Agbese, M Jantunen… - ACM Transactions on …, 2023 - dl.acm.org
Ethics of Artificial Intelligence (AI) is a growing research field that has emerged in response
to the challenges related to AI. Transparency poses a key challenge for implementing AI …
to the challenges related to AI. Transparency poses a key challenge for implementing AI …
Mapping the Potential of Explainable Artificial Intelligence (XAI) for Fairness Along the AI Lifecycle
The widespread use of artificial intelligence (AI) systems across various domains is
increasingly highlighting issues related to algorithmic fairness, especially in high-stakes …
increasingly highlighting issues related to algorithmic fairness, especially in high-stakes …
Understanding Decision Subjects' Fairness Perceptions and Retention in Repeated Interactions with AI-Based Decision Systems
MA Gemalmaz, M Yin - Proceedings of the 2022 AAAI/ACM Conference …, 2022 - dl.acm.org
The wide application of AI-based decision systems in many high-stake domains has raised
concerns regarding fairness of these systems. As these systems will lead to real-life …
concerns regarding fairness of these systems. As these systems will lead to real-life …
Local justice and machine learning: Modeling and inferring dynamic ethical preferences toward allocations
We consider a setting in which a social planner has to make a sequence of decisions to
allocate scarce resources in a high-stakes domain. Our goal is to understand stakeholders' …
allocate scarce resources in a high-stakes domain. Our goal is to understand stakeholders' …
HAMLET: A framework for Human-centered AutoML via Structured Argumentation
Abstract Machine Learning (ML) plays a crucial role in data analysis and data platforms (ie,
integrated sets of technologies that collectively meet end-to-end data needs). In the last …
integrated sets of technologies that collectively meet end-to-end data needs). In the last …
Expressiveness, Cost, and Collectivism: How the Design of Preference Languages Shapes Participation in Algorithmic Decision-Making
Emerging methods for participatory algorithm design have proposed collecting and
aggregating individual stakeholders' preferences to create algorithmic systems that account …
aggregating individual stakeholders' preferences to create algorithmic systems that account …
InterFair: Debiasing with natural language feedback for fair interpretable predictions
Debiasing methods in NLP models traditionally focus on isolating information related to a
sensitive attribute (like gender or race). We instead argue that a favorable debiasing method …
sensitive attribute (like gender or race). We instead argue that a favorable debiasing method …
Causal Perception
JM Alvarez, S Ruggieri - arXiv preprint arXiv:2401.13408, 2024 - arxiv.org
Perception occurs when two individuals interpret the same information differently. Despite
being a known phenomenon with implications for bias in decision-making, as individuals' …
being a known phenomenon with implications for bias in decision-making, as individuals' …
Understanding Decision Subjects' Engagement with and Perceived Fairness of AI Models When Opportunities of Qualification Improvement Exist
MA Gemalmaz, M Yin - arXiv preprint arXiv:2410.03126, 2024 - arxiv.org
We explore how an AI model's decision fairness affects people's engagement with and
perceived fairness of the model if they are subject to its decisions, but could repeatedly and …
perceived fairness of the model if they are subject to its decisions, but could repeatedly and …
Fair Clustering: Critique, Caveats, and Future Directions
Clustering is a fundamental problem in machine learning and operations research.
Therefore, given the fact that fairness considerations have become of paramount importance …
Therefore, given the fact that fairness considerations have become of paramount importance …