Related papers: The Public Algorithms Survey in Allegheny County
Algorithmic fairness is a new interdisciplinary field of study focused on how to measure whether a process, or algorithm, may unintentionally produce unfair outcomes, as well as whether or how the potential unfairness of such processes can…
As machine learning becomes a more mainstream technology, the objective for governments and public sectors is to harness the power of machine learning to advance their mission by revolutionizing public services. Motivational government use…
Genetic algorithms have been developed for decades by researchers in academia and perform well in engineering applications, yet their uptake in industry remains limited. In order to understand why this is the case, the opinions of users of…
Recent advances in generative Artificial Intelligence have raised public awareness, shaping expectations and concerns about their societal implications. Central to these debates is the question of AI alignment -- how well AI systems meet…
Smart recommendation algorithms have revolutionized content delivery and improved efficiency across various domains. However, concerns about user agency arise from the algorithms' inherent opacity (information asymmetry) and one-way output…
Should we be concerned by the massive use of devices and algorithms which automatically handle an increasing number of everyday activities within our societies? The paper makes a short overview of the scientific investigation around this…
The field of algorithmic fairness has rapidly emerged over the past 15 years as algorithms have become ubiquitous in everyday lives. Algorithmic fairness traditionally considers statistical notions of fairness algorithms might satisfy in…
The rapid adoption of generative artificial intelligence (GenAI) technologies has led many organizations to integrate AI into their products and services, often without considering user preferences. Yet, public attitudes toward AI use,…
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…
Data-driven predictive models are increasingly used in education to support students, instructors, and administrators. However, there are concerns about the fairness of the predictions and uses of these algorithmic systems. In this…
Social influence plays an important role in human behavior and decisions. The sources of influence can be generally divided into external, which are independent of social context, or as originating from peers, such as family and friends. An…
In the light of modern technological advances, Artificial Intelligence (AI) is relied upon to enhance performance, increase efficiency, and maximize gains. For Law Enforcement Agencies (LEAs), it can prove valuable in optimizing evidence…
Participatory budgeting, as a paradigm for democratic innovations, engages citizens in the distribution of a public budget to projects, which they propose and vote for implementation. So far, voting algorithms have been proposed and studied…
This paper explores the growing presence of emotionally responsive artificial intelligence through a critical and interdisciplinary lens. Bringing together the voices of early-career researchers from multiple fields, it explores how AI…
Autonomous multi-agent AI systems are poised to transform various industries, particularly software development and knowledge work. Understanding current perceptions among professionals is crucial for anticipating adoption challenges,…
Affective computing is an emerging interdisciplinary field where computational systems are developed to analyze, recognize, and influence the affective states of a human. It can generally be divided into two subproblems: affective…
In recent years, we have witnessed a marked development and growth in Artificial Intelligence. The growth of the data volume generated by sensors and machines, combined with the information flow resulting from the user actions on the…
Given the stated preferences of several people over a number of proposals regarding public policy initiatives, some of those proposals might be judged to be more ``divisive'' than others. When designing online participatory platforms to…
Automated decision systems (ADS) have become ubiquitous in many high-stakes domains. Those systems typically involve sophisticated yet opaque artificial intelligence (AI) techniques that seldom allow for full comprehension of their inner…
The use of machine learning to guide clinical decision making has the potential to worsen existing health disparities. Several recent works frame the problem as that of algorithmic fairness, a framework that has attracted considerable…