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My Experience (so far) as a Woman in CS

December 16, 2022

Women in STEM! Women in STEM! Women in STEM!

It seems as though every conversation I have had with a woman I just met in my major of CS never fails to include the line “women in STEM! That’s us! :p” The term “women in STEM” is actually multi-faceted; the term generally refers to the organization called “Women in STEM” that is concerned with empowering and supporting, well, WOMEN IN STEM! Sorry for the repetition, but it is catchy, isn’t it? To be completely honest, I wasn’t even aware that the phrase referred to an actual organization, I always just thought it was the name for the literal group of women that work or study in STEM subjects. Now, you’re probably thinking, why do the women in STEM always say this phrase to each other? Frankly, it serves as a bit of a joke because of the stereotypical perception of women in CS- surrounded by men all the time with few female counterparts present in professional settings. Is this just a stereotype, or does it hold some merit?

One example out of many: LMC 3403

I wanted to reflect on this subject with regards to my experience in one of my classes this semester, that being LMC 3403: Technical Communication. Although this class is from the school of LMC, it is required for some CS students depending on their threads. Because of that, all of the computer science students in the course were put into one section. My class was super small compared to the norm for CS; there were only about 25 students compared to the usual 350 in a CS class. Even with this dramatic decrease in size, the same pattern still emerged, but somehow even more prominent. It has never been odd that most of the giant CS classes I have been in are probably 75% men, but it was heightened in this class for a couple of reasons. The actual number of women in my class was five out of twenty-five, so 20% women. This is already smaller than the usual, but further, a lot of people would skip lecture so it was basically three women with about ten or more men in the class at any given time. I didn’t notice that glaring difference in demographic until about halfway through the semester when I realized that every group I had been put in during the class had been all guys. The thing about LMC 3403 is that almost every single lecture, the professor would break the class up into four or five small groups so it was much easier to see the breakdown of people. For some reason I never did end up with another woman in my group, which saddened me a little bit.

So, what was it like?

When I was a freshman, a lot of the guys in my classes were a little immature in a sense. They would ignore the women in the class and only speak to their guy friends. As time went on though, I started noticing this less with the guys in my upper-level classes. By the time I was a senior, I was used to working with men as my major was stock full of them. I found that working with men could be really difficult at times as the only women- sometimes, I couldn’t get a single word in over their talking, or they would do all the work before I could contribute anything. I know that every group, regardless of gender, has these problems, but I couldn’t help but feel like this behavior was stemming from the gender divide. Let’s just say they weren’t dying to hear my opinions on any part of the work.

A different experience

After working in these groups full of men talking over me enough times, I had gotten used to this behavior and was prepared for it. To my surprise, LMC 3403 was completely different. When I would be put into small groups with the men in this class, it was the complete opposite of what I had encountered before. The guys would hardly talk at all, only speaking to discuss the requirements of the assignments. This time, no one was fighting for attention or talking room- it was wide open. I felt like I could actually talk freely and share my opinions without being judged. This class was one of the few where I contributed all my thoughts without fear of being wrong or being ignored by the other group members. Not only was I able to speak, but my group members would actively listen! I know, the bar is on the ground, but this was really shocking to me, a woman in STEM who had learned how to tiptoe around the men in STEM. LMC 3403 was one of my favorite courses because of this, and it helped me produce some of my best writing and group work of college.

Conclusion

The stereotype of women being heavily outnumbered in the computer science field is true, but I know that the percentage of WOMEN IN STEM! is going up every single day, thank goodness! Thanks to the contributions and hard work from organizations like Women in STEM, little girls everywhere are learning about computer science and picturing themselves as future coders. This article is just my experience, and I hope to see more women in CS at Georgia Tech every year.

 

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About Author

Ivana is currently an undergraduate at Georgia Tech studying computer science. On a day off, you may find her binge-watching her new favorite TV show, playing with her two dogs, or playing solitaire/doing a crossword.

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  Timothy Durham was convicted of rape when a test showed his DNA genotype matched the DNA recovered from the crime scene. Eye witnesses testified that Durham was in a different state at the time of the incident, but he wasn’t exonerated from his 3000 year sentence until follow up DNA analysis showed that the preliminary forensic analysis used in the trial was misinterpreted (Thomas et al.). DNA testing serves an invaluable part in our criminal justice system, but it comes with complications and bias that can lead to wrongful convictions and misinformed juries.  

Background:

Forensic genetics aims to identify the origin of a biological sample. It can be traced back to Karl Landsteiner who discovered human ABO blood groups. With blood groups, suspects could be eliminated based on their blood type. For example, if a suspect had type A blood and there was type B blood left at the scene by the perpetrator, the investigators could rule out the suspect. Unfortunately, the reverse didn’t work. If the suspect and perpetrator both had the same blood type, it can’t be concluded that the suspect was guilty. Blood identifiers evolved into 8 distinct categories which improved the ability to eliminate suspects, but could only conclude that (if the system matched) it might be the perpetrator or this person might be related to the perpetrator (Jobling and Gill). The field evolved in the 1980s with the discovery of Minisatellites, lines of repetitive DNA that don’t encode proteins. Minisatellite patterns in DNA create what we know today as the DNA fingerprint, and it has a much higher accuracy rate than previous methods (Augustyn et al.). Around the same time, a breakthrough for victims of sexual assault was discovered. In the case of assault, bodily fluids of both the victim and the assaulter can get mixed. A process called differential lysis was created which could selectively increase the concentration of sperm in a fluid sample thereby allowing for accurate DNA testing (Jobling and Gill). Forensics evolved to use single-locus probe testing after several years because of its ease of interpretation. It looked at specific minisatellites that had high variability between subjects. This was the first DNA-based method to be used in a criminal investigation. Two murder homicides from several years apart were connected using DNA evidence. The evidence was then used to exonerate an innocent man, and the guilty party was found when he tried to evade mandatory DNA testing for all men in the small town. His guilt was then reinforced when the DNA profiles matched, and he gave a confession (Jobling and Gill). Finding DNA at a crime scene doesn’t guarantee that you’ll find a murderer. There are issues with non-pure samples and limited samples that make DNA work difficult, and accreditation of facilities and new techniques and take time (Jobling and Gill).  

Subjectivity of DNA Interpretation:

In a 2009 publication, the National Academy of Science discusses the possibility for subjective influence in lab based forensics. The interpretation bias happens in complex cases where, for example, there could be a mixture of DNA. A study published in 2011 looked specifically at this issue. A gang rape happened in Georgia, and the mixture in DNA from the case was used to prosecute one suspect after another suspect took a plea bargain to testify against him. With DNA mixtures, there are three possible conclusions: suspect excluded, suspect cannot be excluded, and inconclusive. If the suspect is excluded, it means that there’s little to no possibility that his DNA is part of the sample. If the suspect cannot be excluded, it means that there’s a possibility that his DNA is part of the sample. In the trial, the DNA examiners who knew the context of the case concluded that the suspect being prosecuted could not be excluded. The 2011 study gave the DNA evidence from this case and the DNA profiles of the suspects to 17 forensic examiners. These examiners had no context of the case, and they all worked independently. Of the 17, only 1 came to the same conclusion (suspect cannot be excluded) as the examiners who knew the context of the case. Twelve of the 17 concluded that the suspect who was prosecuted and convicted had no relation to the DNA found in the sample. Not only does this show that subjectivity occurs due to the context of the case, but the 17 examiners came up with different findings based on their past experiences meaning that not all DNA analysis is objective. Some organizations like the Scientific Working Group on DNA Analysis Methods say that quantitative statistical evidence should be presented with every categorical conclusion, but the US doesn’t require this (Dror and Hampikian).

False Positives:

There are two categories that cause wrongful identification in a DNA report: coincidental matches and false positives. Coincidental matches are DNA matches between two unrelated people. The probability of these matches is about 1 to 500,000 for white individuals and about 1 to 350,000 for black individuals. This small probability of coincidental matches makes people feel confident in DNA evidence. False positives, on the other hand, are matches between DNA and test DNA that result from issues in the forensics lab. There have been multiple cases of forensics labs accidentally switching victim and suspected rapist DNA which lead to convictions that were later overturned when the lab realized the error. There are also cases of misreading or misunderstanding a sample. In a case from 1993, a man was convicted of rape because someone interpreted a mixed sample of victim and perpetrator DNA as an individual sample. The conviction was again overturned. To prevent false convictions, retesting DNA is allowed, but it’s not an efficient solution. Many people are pushing for statistical information on the probability of false positives, so that the jurors can make accurate judgements based on DNA evidence (Thomas et al.).  

Conclusion:

DNA analysis is a useful tool for the criminal justice system, but subjectivity can be an issue in forensic testing. To combat wrongful convictions, there are organizations like The Innocence Project who use DNA testing to free the innocent, but reformed forensic testing standards should be put in place to prevent subjectivity and to ensure juries are correctly informed during trials. https://www.innocenceproject.org/    

Citation:

Augustyn , Adam, et al. “DNA Finerprinting.” Encyclopedia Britannica , 26 Apr. 2018, www.britannica.com/science/DNA-fingerprinting#ref153186. Dror, Itiel E., and Greg Hampikian. “Subjectivity and Bias in Forensic DNA Mixture Interpretation.” Science & Justice, vol. 51, no. 4, 2011, pp. 204–208., doi:10.1016/j.scijus.2011.08.004. Jobling, Mark A., and Peter Gill. “Encoded Evidence: DNA in Forensic Analysis.” Nature Reviews: Genetics, vol. 5, no. 10, Oct. 2004, pp. 739–751., doi:10.1038/nrg1455. Thompson, William C, et al. “How the Probability of a False Positive Affects the Value of DNA Evidence.” Journal of Forensic Sciences, vol. 48, no. 1, Jan. 2003, pp. 1–8., doi:10.1520/jfs2001171.