There is a blind spot in AI research

By Kate Crawford, Ryan Calo. 2016

460 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Health Informatics, Safety Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Health Informatics

In Safety Research

Citations in the corpus

Artificial Intelligence and the ‘Good Society’: the US, EU, and UK approach (2017, unknown): “42 Crawford and Calo (2016).”
Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management (2018, unknown): “A recent article written by Crawford and Calo (2016) oﬀers a critical perspective on current trends in algorithms and artiﬁcial intelligence in industry.”
From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices (2019, background): “ML algorithms are powerful (Ananny & Crawford, 2018) socio-technical constructs that raise concerns that are as much (if not more) about people as they are about code (Crawford & Calo, 2016).”
Ethics of artificial intelligence and robotics (2020, background): “On general AI policy, see (Calo 2018) as well as (Crawford and Calo 2016; Stahl, Timmermans, and Mittelstadt 2016; Johnson and Verdicchio 2017; Giubilini and Savulescu 2018).”
Key challenges for delivering clinical impact with artificial intelligence (2019, background): “Blind spots in machine learning can reflect the worst societal biases, with a risk of unintended or unknown accuracies in minority subgroups, and there is fear over the potential for amplifying biases present in the historical data [78].”
Artificial intelligence versus Maya Angelou: Experimental evidence that people cannot differentiate AI-generated from human-written poetry (2020, background): “Understanding humans ’ behavioral reactions to such algorithms helps shape policies to ensure artificial intelligence remains beneficial (Crawford & Calo, 2016).”
Governing artificial intelligence: ethical, legal and technical opportunities and challenges (2018, unknown): “Various academics expertly questioned the imaginaries underlying data-driven technologies like AI [21] in current debates and highlighted the risks of the use of AI systems [22– 24].”