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Chapter 7 Multi-Criteria Decision-Making: Reference-Type Methods
Zhiyuan Wang, Gade Pandu Rangaiah
https://arxiv.org/abs/2508.16087 https://arxiv.org/pdf/2…

Chapter 7 Multi-Criteria Decision-Making: Reference-Type Methods
This chapter describes selected reference-type multi-criteria decision-making (MCDM) methods that rank alternatives by comparing them with one or more reference solutions derived from an alternatives-criteria matrix (ACM). After explaining the idea of constructing positive ideal, negative ideal and/or average reference solutions, the chapter details the algorithmic steps of each method, illustrating them with a common ACM example. The 9 methods covered are: Technique for Order of Preference by …

Unequal Uncertainty: Rethinking Algorithmic Interventions for Mitigating Discrimination from AI
Holli Sargeant, Mackenzie Jorgensen, Arina Shah, Adrian Weller, Umang Bhatt
https://arxiv.org/abs/2508.07872

Unequal Uncertainty: Rethinking Algorithmic Interventions for Mitigating Discrimination from AI
Uncertainty in artificial intelligence (AI) predictions poses urgent legal and ethical challenges for AI-assisted decision-making. We examine two algorithmic interventions that act as guardrails for human-AI collaboration: selective abstention, which withholds high-uncertainty predictions from human decision-makers, and selective friction, which delivers those predictions together with salient warnings or disclosures that slow the decision process. Research has shown that selective abstention b…

A Methodological Framework and Questionnaire for Investigating Perceived Algorithmic Fairness
Ahmed Abdal Shafi Rasel, Ahmed Mustafa Amlan, Tasmim Shajahan Mim, Tanvir Hasan
https://arxiv.org/abs/2508.05281

A Methodological Framework and Questionnaire for Investigating Perceived Algorithmic Fairness
This study explores perceptions of fairness in algorithmic decision-making among users in Bangladesh through a comprehensive mixed-methods approach. By integrating quantitative survey data with qualitative interview insights, we examine how cultural, social, and contextual factors influence users' understanding of fairness, transparency, and accountability in AI systems. Our findings reveal nuanced attitudes toward human oversight, explanation mechanisms, and contestability, highlighting the im…

Designing Algorithmic Delegates: The Role of Indistinguishability in Human-AI Handoff
Sophie Greenwood, Karen Levy, Solon Barocas, Hoda Heidari, Jon Kleinberg
https://arxiv.org/abs/2506.03102

Designing Algorithmic Delegates: The Role of Indistinguishability in Human-AI Handoff
As AI technologies improve, people are increasingly willing to delegate tasks to AI agents. In many cases, the human decision-maker chooses whether to delegate to an AI agent based on properties of the specific instance of the decision-making problem they are facing. Since humans typically lack full awareness of all the factors relevant to this choice for a given decision-making instance, they perform a kind of categorization by treating indistinguishable instances -- those that have the same o…

The Market Effects of Algorithms
Lindsey Raymond
https://arxiv.org/abs/2508.09513 https://arxiv.org/pdf/2508.09513

The Market Effects of Algorithms
While there is excitement about the potential for algorithms to optimize individual decision-making, changes in individual behavior will, almost inevitably, impact markets. Yet little is known about such effects. In this paper, I study how the availability of algorithmic prediction changes entry, allocation, and prices in the US single-family housing market, a key driver of household wealth. I identify a market-level natural experiment that generates variation in the cost of using algorithms to…

A Comprehensive Review of Human Error in Risk-Informed Decision Making: Integrating Human Reliability Assessment, Artificial Intelligence, and Human Performance Models
Xingyu Xiao, Hongxu Zhu, Jingang Liang, Jiejuan Tong, Haitao Wang
https://arxiv.org/abs/2507.01017

A Comprehensive Review of Human Error in Risk-Informed Decision Making: Integrating Human Reliability Assessment, Artificial Intelligence, and Human Performance Models
Human error remains a dominant risk driver in safety-critical sectors such as nuclear power, aviation, and healthcare, where seemingly minor mistakes can cascade into catastrophic outcomes. Although decades of research have produced a rich repertoire of mitigation techniques, persistent limitations: scarce high-quality data, algorithmic opacity, and residual reliance on expert judgment, continue to constrain progress. This review synthesizes recent advances at the intersection of risk-informed …

Secondary Bounded Rationality: A Theory of How Algorithms Reproduce Structural Inequality in AI Hiring
Jia Xiao
https://arxiv.org/abs/2507.09233 https://…

Secondary Bounded Rationality: A Theory of How Algorithms Reproduce Structural Inequality in AI Hiring
AI-driven recruitment systems, while promising efficiency and objectivity, often perpetuate systemic inequalities by encoding cultural and social capital disparities into algorithmic decision making. This article develops and defends a novel theory of secondary bounded rationality, arguing that AI systems, despite their computational power, inherit and amplify human cognitive and structural biases through technical and sociopolitical constraints. Analyzing multimodal recruitment frameworks, we …

Agency Among Agents: Designing with Hypertextual Friction in the Algorithmic Web
Sophia Liu, Shm Garanganao Almeda
https://arxiv.org/abs/2507.23585 https://

Agency Among Agents: Designing with Hypertextual Friction in the Algorithmic Web
Today's algorithm-driven interfaces, from recommendation feeds to GenAI tools, often prioritize engagement and efficiency at the expense of user agency. As systems take on more decision-making, users have less control over what they see and how meaning or relationships between content are constructed. This paper introduces "Hypertextual Friction," a conceptual design stance that repositions classical hypertext principles--friction, traceability, and structure--as actionable values for reclaimin…