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One TA is Not Like the Others

March 4, 2022

If your teacher was AI, would you notice? Would you feel duped that you weren’t being taught by a real human? Or would you be awestruck by advanced technology’s capabilities? Perhaps a mix of both.

In spring of 2016, Georgia Tech professor Ashok Goel’s course about Artificial Intelligence had nine teaching assistants. One of them was not like the others; unknown to students, one of them was not human. 

Jill Watson, an artificial intelligence program implemented on IBM Watson’s platform, was fed 40 thousand forum posts from prior semesters. While students change, the questions they ask often repeat semester after semester, and Jill wouldn’t get tired of answering them as many times as needed. 

At first, Jill wasn’t very good at her job; she would use the keywords in the question but assumed wrongly what the context of the question was. Her answers weren’t released to the students until she began to answer with 97% accuracy, due to modifications of her algorithm. 

At the end of the semester, Goel finally spilt the beans, much to the awe of students who had been interacting with an AI TA for most of the semester. Only one student had guessed at the secret. Over following semesters, Goel renamed Jill with many different names and didn’t tell his students who was human and who was AI, revealing the mystery at the end of each semester. 

This experiment raised questions about whether AI will replace labor, reducing job opportunities for humans who need them. Could AI like Jill cause colleges to hire less professors and TAs to save money? In Goel’s case, he still had 8 other TAs who answered questions that Jill couldn’t. Goel hopes that in the future, as AI becomes more prevalent in education and other fields, AI will allow humans to choose creative careers instead of monotonous jobs, and fill in jobs that have a shortage of humans applying for them. 

In 2020, Carnegie Learning, an organization founded by a professor at Carnegie Mellon University, won an award for their program MATHia, an AI that offers personalized algebra tutoring. MATHia can not only guide students on a math problem, it can also predict why a student may not be understanding the concept, and target their specific misunderstanding, as well as adapting to the student’s pace and level. MATHia supported students during the pandemic, when many students were struggling with the switch to online learning. According to a study, students who used MATHia had grade averages of about 8 points greater compared to those who did not. 

This shows how AI has the potential to be a valuable addition in the classroom compared to human teachers who may not be able to tailor lessons to each student or spend one-on-one time with students who need extra support. Do you think the future of education is Artificial Intelligence? 

References

Boland, A. (2020, May 13). Mathia® by Carnegie Learning Wins “Best Artificial Intelligence Solution” in the edtech awards 2020. Business Wire. Retrieved February 18, 2022, from https://www.businesswire.com/news/home/20200513005646/en/MATHia%C2%AE-by-Carnegie-Learning-Wins-%E2%80%9CBest-Artificial-Intelligence-Solution%E2%80%9D-in-The-EdTech-Awards-2020

Griswold, A. (2014, March 6). This cognitive tutor software is already having a revolutionary effect. Business Insider. Retrieved February 18, 2022, from https://www.businessinsider.com/cognitive-models-and-computer-tutors-2014-3

Maderer, J. (2016). TALK OF TECH: MY TA IS AN AI. Georgia Tech Alumni Magazine. Retrieved February 18, 2022, from https://www.gtalumni.org/s/1481/alumni/17/gt-magazine.aspx?sid=1481&gid=21&pgid=6668

Maderer, J. (2016, May 9). Artificial Intelligence Course creates AI Teaching assistant. Georgia Tech News Center. Retrieved February 18, 2022, from http://news.gatech.edu/news/2016/05/09/artificial-intelligence-course-creates-ai-teaching-assistant

Stirgus, E. (2017, January 31). She has quite a brain, but this teaching assistant is just not human. ajc. Retrieved February 18, 2022, from https://www.ajc.com/news/local-education/she-has-quite-brain-but-this-teaching-assistant-just-not-human/ZD8NiCkbgWOM4XaXkZ6cBL/

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Aparna is a 3rd year computer science major, focused on media and modeling & simulation. In her free time, she likes to doodle, read fantasy & mystery novels, and waste time on TikTok.

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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.