Search Results for author:
Found 6 papers, 0 papers with code
Blaming Humans and Machines: What Shapes People's Reactions to Algorithmic Harm
Building upon research suggesting that people blame AI systems, we investigated how several factors influence people's reactive attitudes towards machines, designers, and users.
The Conflict Between Explainable and Accountable Decision-Making Algorithms
Furthermore, we argue that XAI could result in incorrect attributions of responsibility to vulnerable stakeholders, such as those who are subjected to algorithmic decisions (i. e., patients), due to a misguided perception that they have control over explainable algorithms.
Human Perceptions on Moral Responsibility of AI: A Case Study in AI-Assisted Bail Decision-Making
How to attribute responsibility for autonomous artificial intelligence (AI) systems' actions has been widely debated across the humanities and social science disciplines.
Dimensions of Diversity in Human Perceptions of Algorithmic Fairness
A growing number of oversight boards and regulatory bodies seek to monitor and govern algorithms that make decisions about people's lives.
Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction
As algorithms are increasingly used to make important decisions that affect human lives, ranging from social benefit assignment to predicting risk of criminal recidivism, concerns have been raised about the fairness of algorithmic decision making.
On Fairness, Diversity and Randomness in Algorithmic Decision Making
Consider a binary decision making process where a single machine learning classifier replaces a multitude of humans.
