Mitigating Gender Bias in Machine Learning Data Sets

Artificial Intelligence has the capacity to amplify and perpetuate societal biases and presents profound ethical implications for society. Gender bias has been identified in the context of employment advertising and recruitment tools, due to their reliance on underlying language processing and recommendation algorithms. Attempts to address such issues have involved testing learned associations, integrating concepts of fairness to machine learning and performing more rigorous analysis of training data. Mitigating bias when algorithms are trained on textual data is particularly challenging given the complex way gender ideology is embedded in language. This paper proposes a framework for the identification of gender bias in training data for machine learning.The work draws upon gender theory and sociolinguistics to systematically indicate levels of bias in textual training data and associated neural word embedding models, thus highlighting pathways for both removing bias from training data and critically assessing its impact.Comment: 10 pages, 5 figures, 5 Tables, Presented as Bias2020 workshop (as part of the ECIR Conference) - http://bias.disim.univaq.i

‘Reading and Interpreting Brecht’ – Q&A with Director Dr Wang Xiaoying

Invited panelis

A guide to the NMC emergency standards for nurse education during the current deployment of student nurses

The Nursing and Midwifery Council (NMC) recognises the important contribution that nursing students are making to the national response to the COVID-19 pandemic. This article reports on the Greater Manchester Supervision and Delegation Framework, providing practical guidance for students and practice staff (practice supervisor/practice assessor and registered nurse) on how to support student nurses who have opted into a paid (deployed) healthcare role. The framework operationalises NMC emergency standards for Nursing and Midwifery education, enabling students to complete their pre-registration undergraduate or postgraduate nursing programme while also supporting the healthcare workforce (NMC, 2020)

Chasing happiness : the role of marriage in the aspiration of success among China’s middle-class women

The first only-child generation born in the 1980s has experienced the great transformations in Chinese society under marketisation. Simultaneously, the state has re-emphasised ‘traditional family values’ to counteract increasing individualism and compensate the lack of public welfare provision. Based on interviews with 31 middle-class women and 11 men born in this cohort, this chapter investigates these women’s attempt to fulfil the ideal of a happy complete family. I apply the analytical tools offered by Illouz (Why Love Hurts: A Sociological Explanation. Cambridge: Polity Press, 2012) about love’s great transformation in Western societies to examine the conditions within which romantic choices are made, shedding light on why women make certain choices to find love and why love hurts for these women in the Chinese context