Search Results for author: Nyalleng Moorosi

Found 6 papers, 2 papers with code

Length of Stay prediction for Hospital Management using Domain Adaptation

no code implementations29 Jun 2023 Lyse Naomi Wamba Momo, Nyalleng Moorosi, Elaine O. Nsoesie, Frank Rademakers, Bart De Moor

In this study, we predict early hospital LoS at the granular level of admission units by applying domain adaptation to leverage information learned from a potential source domain.

Domain Adaptation Length-of-Stay prediction +1

Healthsheet: Development of a Transparency Artifact for Health Datasets

1 code implementation26 Feb 2022 Negar Rostamzadeh, Diana Mincu, Subhrajit Roy, Andrew Smart, Lauren Wilcox, Mahima Pushkarna, Jessica Schrouff, Razvan Amironesei, Nyalleng Moorosi, Katherine Heller

Our findings from the interviewee study and case studies show 1) that datasheets should be contextualized for healthcare, 2) that despite incentives to adopt accountability practices such as datasheets, there is a lack of consistency in the broader use of these practices 3) how the ML for health community views datasheets and particularly \textit{Healthsheets} as diagnostic tool to surface the limitations and strength of datasets and 4) the relative importance of different fields in the datasheet to healthcare concerns.

Fair Wrapping for Black-box Predictions

1 code implementation31 Jan 2022 Alexander Soen, Ibrahim Alabdulmohsin, Sanmi Koyejo, Yishay Mansour, Nyalleng Moorosi, Richard Nock, Ke Sun, Lexing Xie

We introduce a new family of techniques to post-process ("wrap") a black-box classifier in order to reduce its bias.


Characterising Bias in Compressed Models

no code implementations6 Oct 2020 Sara Hooker, Nyalleng Moorosi, Gregory Clark, Samy Bengio, Emily Denton

However, overall accuracy hides disproportionately high errors on a small subset of examples; we call this subset Compression Identified Exemplars (CIE).

Fairness Quantization

Diversity and Inclusion Metrics in Subset Selection

no code implementations9 Feb 2020 Margaret Mitchell, Dylan Baker, Nyalleng Moorosi, Emily Denton, Ben Hutchinson, Alex Hanna, Timnit Gebru, Jamie Morgenstern

The ethical concept of fairness has recently been applied in machine learning (ML) settings to describe a wide range of constraints and objectives.


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