Bias detectives: the researchers striving to make algorithms fair
By R. Courtland. 2018
175 citations recorded by the source corpus.
Subfields: Cognitive Neuroscience, 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 Cognitive Neuroscience
- Ava P. Soleimany
- Daniela Rus
- Ali Sunyaev
- Sebastian Lins
- Ben Hutchinson
- Alexander Amini
- Timnit Gebru
- Scott Thiebes
In Health Informatics
- Ava P. Soleimany
- Daniela Rus
- Ali Sunyaev
- Sebastian Lins
- Alexander Amini
- Scott Thiebes
- David Rozado
- Sangeeta N. Bhatia
In Safety Research
- Ben Hutchinson
- Inioluwa Deborah Raji
- Margaret Mitchell
- Timnit Gebru
- Andrew Smart
- Ava P. Soleimany
- Dan Suciu
- Jamila Smith-Loud
Citations in the corpus
- Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure (2019, methodology): “Negative examples were taken from the ImageNet dataset (Deng et al. 2009), from a wide variety of non-human categories.”
- Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing (2020, background): “dit framework on two hypothetical client projects. The first (hypothetical) client wishes to develop a child abuse screening tool similar to that of the real cases extensively studied and reported on [11, 14, 15, 21, 25, 36]. This complex case intersects heavily with applications in high-risk scenar”
- Interventional Fairness: Causal Database Repair for Algorithmic Fairness (2019, background): “Fairness is increasingly recognized as a critical component of machine learning (ML) systems, which make daily decisions that affect people’s lives [11].”
- Trustworthy artificial intelligence (2020, methodology): “Extant research shows that AI can be employed for purposes of predictive policing (i.e., using mathematical models to forecast what crimes will happen when and where) and therefore benefit society by allowing for a better allocation of police staff and reducing crime rates (Courtland 2018).”
- Wide range screening of algorithmic bias in word embedding models using large sentiment lexicons reveals underreported bias types (2020, background): “Popular machine learning artifacts have been used to illustrate the creeping of purported societal biases and prejudices into models used for computer vision [1], recidivism prediction [2] or language modeling [3].”