discriminationinstem-blog

[discriminationinstem-blog]

Discrimination in STEM

       Developments advancing in science, technology, engineering, and math (STEM) fields have directly impacted almost every part of human life. Scientific developments don’t emerge alone; each is delivered through the diligent work and resourcefulness of scientists. The nature of this science monumentally affects the lives of people, networks, countries and the world. In the tech industry, gender bias and racial bias are fundamental issues affecting minorities. This discrimination violates the Universal Declaration of Human Rights, which argues that “Everyone has the right to work, to free choice of employment, to just and favorable conditions of work and to protection against unemployment. Everyone, without any discrimination, has the right to equal pay for equal work.” Sexism, racism, and algorithmic bias are instances of the injustices that people face in STEM fields.

Works Cited:

UN General Assembly. “Universal Declaration of Human Rights.” United Nations, 217 (III) A, 1948, Paris, art. 13, http://www.un.org/en/universal-declaration-human-rights/  Accessed 24 November 2018

#diversity #stem #discrimination

[discriminationinstem-blog]

Gender Bias in STEM

(Herrera-Perhamus 2017) A portrait of Radia Perlman, Grace Hopper, and Annie Easley. Perlam is the software designer and engineer responsible for some of the most fundamental technologies we have today. Hopper is one of the earliest computer programmers recorded. Easley dedicated tremendous research to the first NASA missions.

        Many women have contributed tremendously to the advancements in science. From venturing in amid World War II to program computing while men fought in the war, to growing new coding languages to progressive programming, many fearless women have made real commitments throughout the years that formed the manner in which we appreciate innovation today. With the discoveries by various women, the technology industry has only continued to further advance such scientific ameliorations. Despite such impressive discoveries, women are found to be victims of unfair discrimination in STEM fields.

       Women are a significant part of the development of technology. They were among the primary developers in the mid-twentieth century and contributed significantly to scientific advances. Advances including the creation of the computer algorithm, the dishwasher, and wireless transmission technology. As innovation and practices adjusted, the job of ladies as software engineers has changed, and the written history of the industry has minimized their accomplishments. In a field that was created by women, men eventually began realizing the challenges involved and wrote it off as too prestigious for women. Ada Lovelace is a remarkable leader in the progression of computer science as she built the first computer program. Her research partner, Charles Babbage, soon realized the difficulty in the discipline and discontinued allowing Lovelace to participate in the research. With circumstances such as this, women have been discriminated upon in the tech industry. In fact, there has been a decline in the amount of STEM college graduated women in North America and Europe, which has fallen to less than 50% (Schmuck 2016). Blatant discrimination is often the reason for discontinuation of STEM majors for women. Furthermore, white, cisgendered, males have dominated the industry for the past centuries.

       Along with major tech companies displaying a preference for males, the salary disparities between men and women are also substantial to the injustices women face. A recent report by Hired features contrasts in genuine pay among ladies and men in the business and in addition gaps in salaries. 63% of the time, men were offered higher pay rates than women for a similar job at a similar organization. According to this data, organizations were putting forth women among 4% and an incredible 45% less beginning pay for a similar activity. Women in tech likewise would underestimate their fairly estimated salary, requesting less pay 66% of the time, and would frequently request 6% less compensation than their male partners (Hired 2018). This discrepancy infringes the value of the lives of women. To be doing the same exact job, yet get paid less just because of the gender one identifies with is an injustice and violation of human rights. To battle this sexism in technology, scientists have recommended that organizations assume liability and change their hierarchical structure issues as opposed to anticipating that women should adjust to the current condition of the workplace.  

Works Cited:

Herrera-Perhamus , Adriana. “The Trailblazing Women In Tech That History Overlooked.” NEW INC, www.newinc.org/news-posts/women-art-tech-list-2017.

Hired. “2018 State of Wage Inequality in the Workplace Report.” Hired, hired.com/wage-inequality-report. Accessed 24 November 2018

Schmuck, Claudine. Women in Stem Disciplines: the Yfactor 2016 Global Report on Gender in Science, Technology, Engineering and Mathematics. Springer, 2016.

#stem #gender bias

[discriminationinstem-blog]

(Magistad 2017) A podcast by the Public Radio International Co. interviewing students of color currently studying STEM subjects in college and discussing their experiences.

       In a nation with a population becoming more diverse each day, the U.S. technology industry is monochromatic––a bastion of white, male prosperity. Discussions concerning diversity in STEM fields have been in place recently due to the influx of white and Asian men in the industry. In an industry that continues to succeed, the quantity of innovation-related jobs will also increase and Silicon Valley will not have the capacity to pull from only a part of the populace to take care of that demand. New voices are important for proceeding with successful innovation, and with differing points of view come the additional possibility to interest a more extensive market.

       Beyond this, by hiring people of all backgrounds, companies gain revenue and global competitiveness. By simply hiring people that come from backgrounds typically not represented in tech, $470 billion and $570 billion in increased in new value to the industry (Intel 2016). Diverse perspectives are essential to the development of future scientific advancements and solutions. The ability to see a problem differently is crucial in understanding how one can solve it. When gatherings of different people are attempting to take care of challenging issues, the assorted variety of the issue solvers matters more than their individual capacity. In this way, diversity is necessary because of the various backgrounds and experiences people bring. These differences are what allow for a prosperous work environment.

Works Cited:

“Decoding Diversity: The Financial and Economic Returns in Tech.” Intel, www.intel.com/content/www/us/en/diversity/decoding-diversity-report.html. Accessed 24 November. 2018.

Magistad, Mary Kay. “Why Having More Black Leaders in Science and Tech Will Boost America’s Future.” Public Radio International, PRI, www.pri.org/stories/2016-12-02/why-having-more-black-leaders-science-and-tech-will-boost-americas-future. Accessed 24 November. 2018.

#diversity #racial bias #stem

[discriminationinstem-blog]

Algorithmic Bias

A TED talk by that commences a discussion on an algorithimic bias and the importance in fighting bias in machine learning,

 In numerous parts of the world, especially in low-income communities, the ramifications of utilizing machine learning to settle on choices that influence individuals' lives are most likely going to have sweeping, durable, and conceivably irreversible results. Individuals of color are left out in the progression of scientific advancement. Innovation is excessively critical and excessively implanted in our lives—from classrooms to medicine—our future will be immensely innovation driven.

       One instance in which this advancement is implemented with injustices is in a new scientific algorithm that law enforcement has developed. The utilization of "predictive policing”––which is the use of factual or machine learning models to police information––anticipates where or by whom crimes will be committed later on. The objective of each machine learning algorithm interprets the designs in the information collected. At the point when given police information, the algorithm will learn patterns in the police information—which allows the police to use the information to target specific groups in a community. A study by Lum determined that since the calculation has discovered that the overrepresented areas to have the most crimes committed, more police will be dispatched to those regions and significantly more wrongdoing will be seen in those areas (Lum 2018). The algorithm then predicts which areas in a specific community might be targeted towards violence and crime.

        Although this innovation is significant to the advancement of our future technology, this is constructed to discriminate upon minorities. This makes a brutal repetitive sequence where police are sent to specific areas that the algorithm determined as the areas with the most crimes. These areas are typically populated of minorities living in low-income communities. These assumptions that these neighborhoods are associated with high crime rates allow these people to remain oppressed and have their rights infringed upon. This creation ultimately progresses the racial bias embedded in law enforcement and targets minorities––simply for living their lives. (1211)

Works Cited:

Talk, TED. YouTube, YouTube, 29 Mar. 2017, www.youtube.com/watch?v=UG_X_7g63rY&t=62s.

#algorithm #algorithmic bias #stem #discrimination