Gender bias in high stakes pitching: an NLP approach

Questions from a Contraceptive Pill Junkie: Applying Human Psychometrics to Investigate Gender Bias in Machine Learning

10.26686/wgtn.17151377 ◽

2021 ◽

Author(s):

◽

Hazel Darney

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Gender Bias ◽

Relevant Literature ◽

Interdisciplinary Approach ◽

High Stakes ◽

Daily Lives ◽

Science Field ◽

Gender Biases ◽

Free Machine

<p>With the rapid uptake of machine learning artificial intelligence in our daily lives, we are beginning to realise the risks involved in implementing this technology in high-stakes decision making. This risk is due to machine learning decisions being based in human-curated datasets, meaning these decisions are not bias-free. Machine learning datasets put women at a disadvantage due to factors including (but not limited to) historical exclusion of women in data collection, research, and design; as well as the low participation of women in artificial intelligence fields. These factors mean that applications of machine learning may fail to treat the needs and experiences of women as equal to those of men. Research into understanding gender biases in machine learning frequently occurs within the computer science field. This has frequently resulted in research where bias is inconsistently defined, and proposed techniques do not engage with relevant literature outside of the artificial intelligence field. This research proposes a novel, interdisciplinary approach to the measurement and validation of gender biases in machine learning. This approach translates methods of human-based gender bias measurement in psychology, forming a gender bias questionnaire for use on a machine rather than a human. The final output system of this research as a proof of concept demonstrates the potential for a new approach to gender bias investigation. This system takes advantage of the qualitative nature of language to provide a new way of understanding gender data biases by outputting both quantitative and qualitative results. These results can then be meaningfully translated into their real-world implications.</p>

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Questions from a Contraceptive Pill Junkie: Applying Human Psychometrics to Investigate Gender Bias in Machine Learning

10.26686/wgtn.17151377.v1 ◽

2021 ◽

Author(s):

◽

Hazel Darney

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Gender Bias ◽

Relevant Literature ◽

Interdisciplinary Approach ◽

High Stakes ◽

Daily Lives ◽

Science Field ◽

Gender Biases ◽

Free Machine

<p>With the rapid uptake of machine learning artificial intelligence in our daily lives, we are beginning to realise the risks involved in implementing this technology in high-stakes decision making. This risk is due to machine learning decisions being based in human-curated datasets, meaning these decisions are not bias-free. Machine learning datasets put women at a disadvantage due to factors including (but not limited to) historical exclusion of women in data collection, research, and design; as well as the low participation of women in artificial intelligence fields. These factors mean that applications of machine learning may fail to treat the needs and experiences of women as equal to those of men. Research into understanding gender biases in machine learning frequently occurs within the computer science field. This has frequently resulted in research where bias is inconsistently defined, and proposed techniques do not engage with relevant literature outside of the artificial intelligence field. This research proposes a novel, interdisciplinary approach to the measurement and validation of gender biases in machine learning. This approach translates methods of human-based gender bias measurement in psychology, forming a gender bias questionnaire for use on a machine rather than a human. The final output system of this research as a proof of concept demonstrates the potential for a new approach to gender bias investigation. This system takes advantage of the qualitative nature of language to provide a new way of understanding gender data biases by outputting both quantitative and qualitative results. These results can then be meaningfully translated into their real-world implications.</p>

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Democrazy—Dem Go Craze, O: Monitoring the 1999 Nigerian Elections

Issue A Journal of Opinion ◽

10.1017/s1548450500005862 ◽

1999 ◽

Vol 27 (1) ◽

pp. 29-33

Author(s):

Darren Kew

Keyword(s):

Transition Program ◽

The Political ◽

High Stakes ◽

Political Class ◽

Soccer Match ◽

The Public ◽

The Military

In many respects, the least important part of the 1999 elections were the elections themselves. From the beginning of General Abdusalam Abubakar’s transition program in mid-1998, most Nigerians who were not part of the wealthy “political class” of elites—which is to say, most Nigerians— adopted their usual politically savvy perspective of siddon look (sit and look). They waited with cautious optimism to see what sort of new arrangement the military would allow the civilian politicians to struggle over, and what in turn the civilians would offer the public. No one had any illusions that anything but high-stakes bargaining within the military and the political class would determine the structures of power in the civilian government. Elections would influence this process to the extent that the crowd influences a soccer match.

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School Success and School Engagement of Immigrant Children and Adolescents

European Psychologist ◽

10.1027/1016-9040/a000139 ◽

2013 ◽

Vol 18 (2) ◽

pp. 126-135 ◽

Cited By ~ 42

Author(s):

Frosso Motti-Stefanidi ◽

Ann S. Masten

Keyword(s):

Academic Achievement ◽

Academic Success ◽

Children And Adolescents ◽

School Engagement ◽

School Success ◽

Immigrant Children ◽

Integrative Model ◽

High Stakes ◽

The Individual

Academic achievement in immigrant children and adolescents is an indicator of current and future adaptive success. Since the future of immigrant youths is inextricably linked to that of the receiving society, the success of their trajectory through school becomes a high stakes issue both for the individual and society. The present article focuses on school success in immigrant children and adolescents, and the role of school engagement in accounting for individual and group differences in academic achievement from the perspective of a multilevel integrative model of immigrant youths’ adaptation ( Motti-Stefanidi, Berry, Chryssochoou, Sam, & Phinney, 2012 ). Drawing on this conceptual framework, school success is examined in developmental and acculturative context, taking into account multiple levels of analysis. Findings suggest that for both immigrant and nonimmigrant youths the relationship between school engagement and school success is bidirectional, each influencing over time the other. Evidence regarding potential moderating and mediating roles of school engagement for the academic success of immigrant youths also is evaluated.

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