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AI & Historical Texts

November 23, 2024

When looking at the past, it’s very hard to decipher how exactly certain situations unfolded. We must look to historical texts in order to garner a revelation on how this domino effect of history unfolded to our present day. The issue with analyzing historical texts is that it is under the umbrella of human error and with human error comes human bias which when analyzing historical texts to reveal historic events to the world, this bias can become very dangerous. Dangerous to the point where it can (and has) altered our perception of history. In this article we will look to AI and how analyzing historical texts through AI can help suppress these human errors.

The implementation of AI in historical analysis has revolutionized how we approach primary sources. Machine learning algorithms can now process vast quantities of historical documents, identifying patterns and connections that human researchers might miss or take years to discover. For instance, the Venice Time Machine project, led by researchers at École Polytechnique Fédérale de Lausanne, has successfully digitized and analyzed thousands of historical Venetian documents, revealing previously unknown patterns in trade relationships and social networks (Williams, 2022). This breakthrough in historical research demonstrates the potential for AI to uncover hidden narratives and connections that traditional methods might overlook.

AI systems offer several significant advantages in historical analysis that extend beyond mere pattern recognition. Advanced natural language processing (NLP) models can analyze historical texts in multiple languages and adapt to archaic writing styles, making cross-cultural comparison more accessible than ever before (Chen & Rodriguez, 2023). Furthermore, these systems can provide comprehensive statistical analysis of historical trends, helping to validate or challenge existing historical narratives with data-driven insights that would be impossible to generate through traditional research methods alone.

However, it’s crucial to acknowledge that AI itself is not free from bias. The algorithms are trained on data sets that may contain their own inherent biases, and the programming decisions made by developers can inadvertently influence results. As noted by Dr. Sarah Thompson (2023) in her work on digital humanities, “AI should be viewed as a complementary tool to traditional historical research methods, not a replacement.” This understanding has led to the development of various safeguards in the field, including regular calibration of AI models against peer-reviewed historical research and cross-validation of findings using multiple AI systems. The collaboration between historians and AI experts has become essential to ensure appropriate interpretation of results and maintain the integrity of historical research.

The integration of AI in historical analysis represents a significant step forward in our ability to understand the past more objectively. Projects like the Historical Document Analysis Initiative (HDAI) demonstrate how machine learning can help identify and correct for biases in historical narratives while preserving the essential human element of historical interpretation (Garcia et al., 2024). These developments have opened new avenues for research and understanding, allowing historians to approach their subjects with unprecedented depth and precision.
The more we refine these technologies, the greater the relationship between AI and historical research becomes. Researchers have found that combining traditional historical methods with AI-powered analysis can provide a more complete and nuanced understanding of historical events and their interconnections. This hybrid approach allows historians to maintain their crucial role in interpretation and analysis while leveraging the powerful capabilities of AI to process and analyze vast amounts of historical data.

The future of historical research lies in this careful balance between human expertise and technological capability. While AI offers powerful tools for analyzing historical texts with reduced human bias, it’s essential to remember that complete objectivity remains elusive. The goal is not to remove human judgment entirely but to enhance it with technological capabilities that can help identify and minimize unconscious biases. As we continue to develop more sophisticated AI tools, the partnership between human historians and machine learning systems will become increasingly valuable in our quest to understand the past more accurately.

By developing more accurate and comprehensive understandings of historical events, we can better inform current decision-making processes and policy development. The ability to analyze large amounts of historical data quickly and efficiently while also accounting for potential biases helps create a more nuanced and accurate picture of our past, which in turn helps us better understand our present and prepare for our future.

 

 

 

 

 

Citations

Donovan, M. (2023). How AI is helping historians better understand our past. *MIT Technology Review*.
Williams, R. (2022). The Venice Time Machine: Digital Humanities at Scale. *Digital History Quarterly*, 18(2), 45-67.
Chen, L., & Rodriguez, A. (2023). Natural Language Processing in Historical Research: Advances and Challenges. *Journal of Digital Humanities*, 12(4), 89-112.
Thompson, S. (2023). Digital Tools and Historical Method: A Critical Assessment. *History and Computing*, 15(3), 201-224.
Garcia, M., et al. (2024). The Historical Document Analysis Initiative: First Year Results. *Digital Scholarship in the Humanities*, 39(1), 1-28.

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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.