Editing Neural network (machine learning) (section)

=== Dataset bias ===
Neural networks are dependent on the quality of the data they are trained on, thus low quality data with imbalanced representativeness can lead to the model learning and perpetuating societal biases.<ref name=":010">{{Cite journal |last1=Norori |first1=Natalia |last2=Hu |first2=Qiyang |last3=Aellen |first3=Florence Marcelle |last4=Faraci |first4=Francesca Dalia |last5=Tzovara |first5=Athina |date=October 2021 |title=Addressing bias in big data and AI for health care: A call for open science |journal=Patterns |language=en |volume=2 |issue=10 |page=100347 |doi=10.1016/j.patter.2021.100347|doi-access=free |pmid=34693373 |pmc=8515002 }}</ref><ref name=":17">{{Cite journal |last=Carina |first=Wang |date=27 October 2022 |title=Failing at Face Value: The Effect of Biased Facial Recognition Technology on Racial Discrimination in Criminal Justice |journal=Scientific and Social Research |volume=4 |issue=10 |pages=29–40 |doi=10.26689/ssr.v4i10.4402 |issn=2661-4332|doi-access=free }}</ref> These inherited biases become especially critical when the ANNs are integrated into real-world scenarios where the training data may be imbalanced due to the scarcity of data for a specific race, gender or other attribute.<ref name=":010" /> This imbalance can result in the model having inadequate representation and understanding of underrepresented groups, leading to discriminatory outcomes that exacerbate societal inequalities, especially in applications like [[Facial recognition system|facial recognition]], hiring processes, and [[law enforcement]].<ref name=":17" /><ref name=":22">{{Cite journal |last=Chang |first=Xinyu |date=13 September 2023 |title=Gender Bias in Hiring: An Analysis of the Impact of Amazon's Recruiting Algorithm |url=https://aemps.ewapublishing.org/article.html?pk=e5b93601b03d453c855d54d3153875ba |journal=Advances in Economics, Management and Political Sciences |volume=23 |issue=1 |pages=134–140 |doi=10.54254/2754-1169/23/20230367 |issn=2754-1169 |doi-access=free |access-date=9 December 2023 |archive-date=9 December 2023 |archive-url=https://web.archive.org/web/20231209135207/https://aemps.ewapublishing.org/article.html?pk=e5b93601b03d453c855d54d3153875ba |url-status=live }}</ref> For example, in 2018, [[Amazon (company)|Amazon]] had to scrap a recruiting tool because the model favored men over women for jobs in software engineering due to the higher number of male workers in the field.<ref name=":22" /> The program would penalize any resume with the word "woman" or the name of any women's college. However, the use of [[synthetic data]] can help reduce dataset bias and increase representation in datasets.<ref>{{Cite book |last1=Kortylewski |first1=Adam |last2=Egger |first2=Bernhard |last3=Schneider |first3=Andreas |last4=Gerig |first4=Thomas |last5=Morel-Forster |first5=Andreas |last6=Vetter |first6=Thomas |chapter=Analyzing and Reducing the Damage of Dataset Bias to Face Recognition with Synthetic Data |date=June 2019 |title=2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |pages=2261–2268 |publisher=IEEE |doi=10.1109/cvprw.2019.00279 |isbn=978-1-7281-2506-0 |s2cid=198183828 |url=https://edoc.unibas.ch/75257/1/20200128164027_5e3055eb775f1.pdf |access-date=30 December 2023 |archive-date=19 May 2024 |archive-url=https://web.archive.org/web/20240519082642/https://edoc.unibas.ch/75257/1/20200128164027_5e3055eb775f1.pdf |url-status=live }}</ref>