Facial Recognition Is Accurate, if You’re a White Guy
By White Guy. 2018
Subfields: Computer Vision and Pattern Recognition, AI Ethics, Fairness & Society
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- John R. Smith
- Meredith Ringel Morris
- Rogério Schmidt Feris
- Ece Kamar
- Michele Merler
- Madalina Busuioc
- Jathan Sadowski
- Yaoliang Yu
In AI Ethics, Fairness & Society
Citations in the corpus
- Diversity in Faces (2019, methodology): “The gender annotations used two class labels (male and female) and the age group labeling used seven classes ([0-3],[4-12],[13-19],[20-30],[31-45],[46-60],[61-]), as well as a continuous age value to be consistent with the automatic prediction labels.”
- Towards causal benchmarking of bias in face analysis algorithms (2020, unknown): “Caucasian and African-American population [33].”
- Designing an Online Infrastructure for Collecting AI Data From People With Disabilities (2021, background): “Such studies have investigated how one’s gender [10, 34, 42, 47, 50], race [10, 34], and socioeconomic status [1] could negatively afect the performance of an AI system such as facial recognition or natural language processing systems.”
- Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training (2021, background): “In practice, such gaps are the key reason that facial recognition systems [58, 76] have a racial bias.”
- Accountable Artificial Intelligence: Holding Algorithms to Account (2020, background): “…“mother”; facial recognition algorithms have persistently been found to display much higher error rates for minorities (Buolamwini and Gebru 2018; Lohr 2018; Snow 2018; Medium 2019), potentially leading to false arrests and discrimination of already marginalized groups when used in policing…”
- When data is capital: Datafication, accumulation, and extraction (2019, background): “At other times, certain groups are missing from data sets, such as when facial recognition systems inaccurately identify people of colour because they have been trained with data composed of mostly white male faces – people who look like their programmers (Lohr, 2018).”