Battling algorithmic bias

By K. Kirkpatrick. 2016

88 citations recorded by the source corpus.

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Citations in the corpus

Assessing gender bias in machine translation: a case study with Google Translate (2018, methodology): “the wave of algorithmic bias, some have argued for the creation of some kind of agency in the likes of the Food and Drug Administration, with the sole purpose of regulating algorithmic discrimination [23]. With this in mind, we propose a quantitative analysis of the phenomenon of gender bias in mach”
Algorithmic Bias in Autonomous Systems (2017, background): “Moreover, there is a rapidly growing scholarly literature about algorithmic biases, including technological techniques to try to mitigate it (e.g., [Barocas and Selbst, 2016; Garcia, 2016; Kirkpatrick, 2016; Pedreschi, et al., 2008]).”
Integrating Ethics and Career Futures with Technical Learning to Promote AI Literacy for Middle School Students: An Exploratory Study (2022, background): “For instance, algorithmic bias in AI applications such as facial recognition, predictive policing, and credit score assignment has had negative impacts on communities of color (Buolamwini & Gebru, 2018; Kirkpatrick, 2016; Selbst, 2017; Van Brakel, 2016).”
Augmenting Medical Diagnosis Decisions? An Investigation into Physicians' Decision-Making Process with Artificial Intelligence (2021, background): “…commonly associated with intelligent beings, i.e., intellectual processes that are characteristic for humans such as reasoning, generalizing, or learning from experience (Russell and Norvig 2010). display racist and sexist decision schemes because of distortions in training data (Kirkpatrick 2016).”
The Ethical Implications of Using Artificial Intelligence in Auditing (2020, background): “Kirkpatrick (2016) conveys the sentiments of an AI developer who concluded that “I am afraid that our technical understanding is still so limited that regulation at this point could easily do more harm than good.””
Arming the public with artificial intelligence to counter social bots (2019, background): “As AI systems employ more sophisticated learning methodologies, model complexity increases and systems become vulnerable to overlooked biases (Kirkpatrick, 2016).”