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Algorithmic systems are increasingly deployed to make decisions in many areas of peoples lives. The shift from human to algorithmic decision-making has been accompanied by concern about potentially opaque decisions that are not aligned with social values, as well as proposed remedies such as explainability. We present results of a qualitative study of algorithmic decision-making, comprised of five workshops conducted with a total of 60 participants in Finland, Germany, the United Kingdom, and the United States. We invited participants to reason about decision-making qualities such as explainability and accuracy in a variety of domains. Participants viewed AI as a decision-maker that follows rigid criteria and performs mechanical tasks well, but is largely incapable of subjective or morally complex judgments. We discuss participants consideration of humanity in decision-making, and introduce the concept of negotiability, the ability to go beyond formal criteria and work flexibly around the system.
The recent enthusiasm for artificial intelligence (AI) is due principally to advances in deep learning. Deep learning methods are remarkably accurate, but also opaque, which limits their potential use in safety-critical applications. To achieve trust
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How to attribute responsibility for autonomous artificial intelligence (AI) systems actions has been widely debated across the humanities and social science disciplines. This work presents two experiments ($N$=200 each) that measure peoples perceptio
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