Collaborating Authors

gender difference

Stochastic encoding of graphs in deep learning allows for complex analysis of gender classification in resting-state and task functional brain networks from the UK Biobank Machine Learning

Classification of whole-brain functional connectivity MRI data with convolutional neural networks (CNNs) has shown promise, but the complexity of these models impedes understanding of which aspects of brain activity contribute to classification. While visualization techniques have been developed to interpret CNNs, bias inherent in the method of encoding abstract input data, as well as the natural variance of deep learning models, detract from the accuracy of these techniques. We introduce a stochastic encoding method in an ensemble of CNNs to classify functional connectomes by gender. We applied our method to resting-state and task data from the UK BioBank, using two visualization techniques to measure the salience of three brain networks involved in task- and resting-states, and their interaction. To regress confounding factors such as head motion, age, and intracranial volume, we introduced a multivariate balancing algorithm to ensure equal distributions of such covariates between classes in our data. We achieved a final AUROC of 0.8459. We found that resting-state data classifies more accurately than task data, with the inner salience network playing the most important role of the three networks overall in classification of resting-state data and connections to the central executive network in task data.

Maybe The Apple And Goldman Sachs Credit Card Isn't Gender Biased


The Apple credit card has come under scrutiny for offering lower credit limits to women (AP ... [ ] Photo/Tony Avelar, File) The Apple-branded credit card is under scrutiny, because women are receiving less credit than their spouses who share their income and credit score. In launching the card, Apple partnered with Goldman Sachs, and Goldman is the issuing bank for the card. Now, Goldman's credit review process is being labeled sexist by Elizabeth Warren and several high-power tech execs. A tech entrepreneur, David Heinemeier Hansson, first raised the issue when he tweeted that the Apple Card's algorithms discriminated against his wife, giving him 20 times the credit limit it had given to her. Apple cofounder Steve Wozniak weighed in asserting that he can borrow ten times as much as his wife on their Apple Cards.

Men are equally capable of multi-tasking, study finds

Daily Mail - Science & tech

Women are not inherently better at multi-tasking - and that's according to scientists. A study examining the long-asserted myth has proved that men are just as capable of juggling numerous jobs simultaneously. In fact, despite years of claims to the contrary, it transpires that both genders are equally able, or unable, to do more than one task concurrently. A team of researchers led by Dr Patricia Hirsch of Germany's Aachen University reached the conclusion after analysing 48 men and 48 women, with an average age of 24, in letter or number identification tasks. Some participants were asked to pay attention to two tasks at once, known as concurrent multitasking.

Exploring difference in public perceptions on HPV vaccine between gender groups from Twitter using deep learning Machine Learning

In this study, we proposed a convolutional neural network model for gender prediction using English Twitter text as input. Ensemble of proposed model achieved an accuracy at 0.8237 on gender prediction and compared favorably with the state-of-the-art performance in a recent author profiling task. We further leveraged the trained models to predict the gender labels from an HPV vaccine related corpus and identified gender difference in public perceptions regarding HPV vaccine. The findings are largely consistent with previous survey-based studies.

Gender specific and Age dependent classification model for improved diagnosis in Parkinson's disease Machine Learning

Accurate diagnosis is crucial for preventing the progression of Parkinson's, as well as improving the quality of life with individuals with Parkinson's disease. In this paper, we develop a gender specific and age dependent classification method to diagnose the Parkinson's disease using the handwriting based measurements. The gender specific and age dependent classifier was observed significantly outperforming the generalized classifier. An improved accuracy of 83.75% (SD=1.63) with the female specific classifier, and 79.55% (SD=1.58) with the old age dependent classifier was observed in comparison to 75.76% (SD=1.17) accuracy with the generalized classifier. Finally, combining the age and gender information proved to be encouraging in classification. We performed a rigorous analysis to observe the dominance of gender specific and age dependent features for Parkinson's detection and ranked them using the support vector machine(SVM) ranking method. Distinct set of features were observed to be dominating for higher classification accuracy in different category of classification.

Turkish women coming to rule world of artificial intelligence


The World Economic Forum (WEF) publishes its report entitled, "Business Life and Gender Differences," every year. The report got a new name for the first time this year; as you can imagine, this title relates to artificial intelligence (AI). The WEF researched the rates of female AI experts in the workforce in 146 countries. Some important points in the report include caring for children at home, robots taking the place of workers at factories and offices being the most significant factors in women losing their jobs. The WEF Center for the New Economy and Society President Saadia Zahidi says that robotic and artificial intelligence technologies take place in fields where women traditionally work, such as management, customer relations and telemarketing.

Relationship of gender differences in preferences to economic development and gender equality


The relationships are predicted from local polynomial regressions. Shaded areas indicate 95% confidence intervals. Preferences concerning time, risk, and social interactions systematically shape human behavior and contribute to differential economic and social outcomes between women and men. We present a global investigation of gender differences in six fundamental preferences. Our data consist of measures of willingness to take risks, patience, altruism, positive and negative reciprocity, and trust for 80,000 individuals in 76 representative country samples. Gender differences in preferences were positively related to economic development and gender equality. This finding suggests that greater availability of and gender-equal access to material and social resources favor the manifestation of gender-differentiated preferences across countries. Fundamental preferences such as altruism, risk-taking, reciprocity, patience, or trust constitute the foundation of choice theories and govern human behavior.

Could AI Be the Cure for Workplace Gender Inequality?


Artificial intelligence is beginning to replace many of the workplace roles that men dominate. The parts of those jobs that will have staying power are those that rely more heavily on emotional intelligence -- skills in which women typically excel.

Men Are Better At Maps Until Women Take This Course - Issue 54: The Unspoken


Sheryl Sorby, a professor of engineering education at Ohio State University, was used to getting A's. For as long as she could remember, she found academics a breeze. She excelled in math and science in particular, but "I never thought there was a subject I couldn't do," she says matter-of-factly.

Why Do Men Get More Attention? Exploring Factors Behind Success in An Online Design Community

AAAI Conferences

Online platforms are an increasingly popular tool for people to produce, promote or sell their work. However recent studies indicate that social disparities and biases present in the real world might transfer to online platforms and could be exacerbated by seemingly harmless design choices on the site (for example: recommendation systems or publicly visible success measures). In this paper we analyze an exclusive online community of teams of design professionals called Dribbble and investigate apparent differences in outcomes by gender. Overall, we find that men produce more work, and are able to show it to a larger audience thus receiving more likes. Some of this effect can be explained by the fact that women have different skills and design different images. Most importantly however, women and men position themselves differently in the Dribbble community. Our investigation of users' position in the social network shows that women have more clustered and gender homophilous following relations, which leads them to have smaller and more closely knit social networks. Overall, our study demonstrates that looking behind the apparent patterns of gender inequalities in online markets with the help of social networks and product differentiation helps us to better understand gender differences in success and failure.