Your parents have a lot of influence over you and your choices, from passing on their genes to giving advice and setting an example as you grow up. But how much of your chosen job depends on your parents? Researchers at Facebook used data from 5.6 million people to reveal that certain jobs parents have, including being a nurse, scientist or lawyer, increase the chances of their children following in their footsteps. Researchers at Facebook used data from 5.6 million people to reveal that certain jobs parents have, including being a nurse, scientist or lawyer, increases the chances of their children following in their footsteps The researchers analysed two sets of data to determine whether children ended up in similar jobs as their parents The researchers analysed two sets of data to determine whether children ended up in the same type of jobs as their parents, and whether siblings were more likely to choose the same job. The results showed that, compared to the rest of the population, offspring and siblings are statistically more likely to choose the jobs their parents or siblings have done.
Case studies, such as Kay et al., 2015 have shown that in image summarization, such as with Google Image Search, the people in the results presented for occupations are more imbalanced with respect to sensitive attributes such as gender and ethnicity than the ground truth. Most of the existing approaches to correct for this problem in image summarization assume that the images are labelled and use the labels for training the model and correcting for biases. However, these labels may not always be present. Furthermore, it is often not possible (nor even desirable) to automatically classify images by sensitive attributes such as gender or race. Moreover, balancing according to the labels does not guarantee that the diversity will be visibly apparent - arguably the only metric that matters when selecting diverse images. We develop a novel approach that takes as input a visibly diverse control set of images and uses this set to produce images in response to a query which is similarly visibly diverse. We implement this approach using pre-trained and modified Convolutional Neural Networks like VGG-16, and evaluate our approach empirically on the Image dataset compiled and used by Kay et al., 2015. We compare our results with the Google Image Search results from Kay et al., 2015 and natural baselines and observe that our algorithm produces images that are accurate with respect to their similarity to the query images (on par with that of the Google Image Search results), but significantly outperforms with respect to visible diversity as measured by their similarity to our diverse control set.
Employers added 292,000 jobs last month underscoring that the U.S. economy remains strong despite uncertainty in China. How will Wall Street react? SAN FRANCISCO -- If you're an American who wants to make more money, learn a technology occupation. If you're already a tech worker and want to live even more like the rich, move to Seattle or Texas. Those are two of the main takeaways from the latest annual salary and occupational data on Americans, released last week by the U.S. Bureau of Labor Statistics.
The projections are especially useful if you're interested in advising others about careers or if you'd like to know what to expect in terms of employment growth, required education for entry, and wages for certain occupations. This article presents the 2016–26 occupational employment projections in 14 charts. Charts 1 and 2 highlight occupations that are projected to have the fastest growth or the most new jobs over the 2016–26 decade. Chart 3 shows the occupations that are projected to have the largest number of openings in each year of the decade, on average, for workers who are entering the occupation. Chart 4 shows the occupations that are expected to have the most job losses.
We present a large-scale study of gender bias in occupation classification, a task where the use of machine learning may lead to negative outcomes on peoples' lives. We analyze the potential allocation harms that can result from semantic representation bias. To do so, we study the impact on occupation classification of including explicit gender indicators---such as first names and pronouns---in different semantic representations of online biographies. Additionally, we quantify the bias that remains when these indicators are "scrubbed," and describe proxy behavior that occurs in the absence of explicit gender indicators. As we demonstrate, differences in true positive rates between genders are correlated with existing gender imbalances in occupations, which may compound these imbalances.