Blaming Humans and Machines: What Shapes People's Reactions to Algorithmic Harm
April 05, 2023 Β· Declared Dead Β· π International Conference on Human Factors in Computing Systems
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Gabriel Lima, Nina GrgiΔ-HlaΔa, Meeyoung Cha
arXiv ID
2304.02176
Category
cs.CY: Computers & Society
Cross-listed
cs.AI,
cs.HC
Citations
34
Venue
International Conference on Human Factors in Computing Systems
Last Checked
4 months ago
Abstract
Artificial intelligence (AI) systems can cause harm to people. This research examines how individuals react to such harm through the lens of blame. 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 results of three studies (N = 1,153) indicate differences in how blame is attributed to these actors. Whether AI systems were explainable did not impact blame directed at them, their developers, and their users. Considerations about fairness and harmfulness increased blame towards designers and users but had little to no effect on judgments of AI systems. Instead, what determined people's reactive attitudes towards machines was whether people thought blaming them would be a suitable response to algorithmic harm. We discuss implications, such as how future decisions about including AI systems in the social and moral spheres will shape laypeople's reactions to AI-caused harm.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Computers & Society
π
π
The Cartographer
R.I.P.
π»
Ghosted
Artificial Intelligence: the global landscape of ethics guidelines
R.I.P.
π»
Ghosted
The role of artificial intelligence in achieving the Sustainable Development Goals
R.I.P.
π»
Ghosted
Green AI
R.I.P.
π»
Ghosted
Principles alone cannot guarantee ethical AI
R.I.P.
π»
Ghosted
Tackling Climate Change with Machine Learning
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted