Algorithms: They can’t do it alone
Bryce Glendenning
MET program, UBC
ETEC 540: Text Technologies: The Changing Spaces of Reading and Writing
Dr. Rachel Horst
April 12, 2026
Algorithms: They can’t do it alone
Algorithms are a set of sequential rules which are applied in order to solve a problem (How an Algorithm is Born, 2026). Since the development of the first algorithm in 1843, algorithms have permeated our society in countless ways. Over time, algorithms have been adapted and improved upon in order for them to be more successful. However, the definition of success may be different for the creator of the algorithm and those affected by it (O’Neil, 2016).
This article will briefly examine algorithms from a historical and cultural perspective, provide implications for education and literacy, and provide recommendations for the responsible use of algorithms in the future.
Historical and cultural context
Ada Lovelace contributed to what is widely considered the first algorithm in 1843. Her algorithm was a step-by-step process designed for a machine to follow in order to solve a complex math problem (101computing.net, 2025). Even though the machine designed to utilize this algorithm was never built, she successfully demonstrated that a machine was capable of complex computation. (101computing.net, 2025).
Once computers were eventually built, text became increasingly digitized. As humans, we seem to have an innate desire to analyze and attempt to make sense of data. With the increase in digital text (email, word processing, social media, blogs, etc) there was endless data to analyze. The problem was that it was messy, unorganized, and difficult to extract meaning from. This is where algorithms came into play. Algorithms were developed to sort through these masses of data in order to predict where crime will occur (Reply All, 2022), evaluate the effectiveness of grade school teachers, or decide the risk of a criminal to reoffend after being released from prison (O’Neil, 2016),
Implications for literacy and education
Algorithms are not inherently good or bad. They do not make decisions. Humans are the ones that make decisions based on the data that is provided by the algorithm (O’Neil, 2016). Because of this, there are several implications for education and literacy that we should take into consideration before making decisions based on the data provided by the algorithms.
First, we need to understand that the data provided by algorithms may be biased. For example, the predictive policing algorithms that were used in the 1980s in New York city were designed to lower crime rates (Reply All, 2022). High crime areas were identified with data maps, and officers were then deployed to these areas to make arrests. Ticket quotas were put into effect where officers were pressured by their superior officers into giving out a predetermined number of tickets before the end of each shift (Reply All, 2022). Officers suddenly felt that they had to find infractions to give tickets out for, and if they couldn’t find any, they began making them up. Since officers knew many of the charges they were giving out were unlikely to hold up in court, they would give them out to people in poverty stricken neighborhoods where the “perpetrators” were unlikely to be able to afford a lawyer or fight the ticket in court (Reply All, 2022).
To prevent these types of biased practices from occurring, policing agencies should make data education an essential part of their job training programs. Since data is used to make decisions regarding policing and public safety, the data collected and recorded by officers needs to be meaningful and accurate. Policing supervisors also need to oversee their officers’ collection and recording of data to ensure its accuracy and legitimacy. It is their job to ensure that their officers are collecting and reporting data accurately. If an area has low crime, that may mean that the officers are needed elsewhere. This is important data to know and although it may be difficult to move an officer away from a low crime location that they want to be into a higher crime location where they might not want to be, this might need to be done in the best interests of the public safety. Taxpayers who fund police departments have the right to know that policing data is collected and reported accurately. Therefore, they have the right to know that those who are responsible for making decisions based on the data are trained in doing so properly.
In another example, teachers in some parts of the USA are evaluated based on algorithmic data (O’Neil, 2016). The data is based on student achievement on annual standardized tests. Teachers are either rewarded when their students show growth, or punished when they don’t, sometimes even losing their jobs (O’Neil, 2016). When teachers know that they are being evaluated based on their students’ growth, there is an incentive to cheat by inflating their students’ year-end grades. This dishonest reporting makes it almost impossible for the following year’s teacher to receive a fair evaluation unless they cheat in a similar way on their own year-end reporting. This can result in unfair evaluations and even in some cases the unwarranted terminations of teachers (O’Neil. 2016).
Increasing algorithm literacy in teacher education programs will allow teachers to better understand the importance of accurate data collection and reporting. It will help them to understand the harms that can come from data manipulation (inflating your students grades to receive a good evaluation). School principals whose school boards rely on algorithms for teacher evaluations should receive training to ensure that data is being collected and reported accurately. They should ensure that standardized testing data is being recorded by a third party to reduce potential conflicts of interest, especially when incentives are given to teachers based on these reported scores. Finally, algorithms should be designed to take into consideration a broad range of factors that could lead to lower than expected scores on standardized tests such as student mental health, socioeconomic status, developmental delays, teacher training, funding, and much more. If these complex factors are not able to be accurately embedded into the algorithm, then school based teams of expert educators need to be built to analyze these situations on a case by case basis.
How do algorithms make, and reveal meaning?
Since algorithms are tools used by organizations in society to make decisions, the way that that data is used can reveal information about what our society values. For example, according to O’Neil (2016) health data on American citizens is being collected and stored. This data can be very useful to doctors and with the right algorithms, can be analyzed to increase the timeliness and effectiveness of patient care. However, the same data can also be used for nefarious purposes if a corporation wanted to use it that way. For example, someone could potentially be asked to provide health data when applying for a job. An algorithm could tell the company to screen out certain candidates because it could deem them to be a liability to the company based on poor health outcomes which could cost the company money in lost time or health benefit payouts. Hypothetical examples like this one could reveal whether a company cares more about hiring a qualified employee, or protecting their bottom line.
Additionally, algorithms reveal that societal values are not universal. Algorithms are used to censor and moderate content on social media sites like tiktok and instagram (Medford-Kerr, 2025), however, users of these platforms are changing the way they write so that their posts can evade detection of the algorithms. This is resulting in the development of a “coded language” (Medford-Kerr, 2025, p. 1). The language is a mix of intentionally misspelled words (seggs, instead of sex), acronyms (S.A. instead of sexual assault), emojis (an eggplant emoji representing male genatalia), and words likely to be flagged replaced with others less likely to be flagged (unalive instead of suicide) (Medford-Kerr, 2025). Although the algorithms are developed to prevent what the social media platforms deem inappropriate content, either users do not agree that this content should be censored, or they enjoy the thrill that comes from finding a way to circumvent protection measures with what has been determined to be inappropriate content. Either way, as the algorithms evolve to better detect what the social media platforms deem inappropriate online content, the language used will also evolve to continue to evade detection. This could ultimately result in the evolution in online forms of communication which separate so far from the norms of the English spelling and grammar, that they become their own unique dialect understandable only to subgroups who frequent these platforms.
Conclusion
Algorithms are at play in many aspects of our lives. The algorithms themselves are neither harmful nor beneficial. They are tools to analyze data. The way in which the data is used in society is where the harm or benefit is revealed.
According to Sofronieva et al, (2024) when people are aware that algorithms are used to influence and affect the online content that they interact with, and have an understanding of how algorithms work, they are able to avoid potential risks that come with internet use. They are also more likely to engage with online content when they believe they have control over the algorithmically generated suggestions.
I would argue that Sofronieva et al’s (2024) research applies to all situations in society where algorithms are at play. For example, if members of the public were made aware of exactly how the algorithms which provide information to policing departments were designed, they would be more trustful of policing strategies. If this information is kept secret, there is more likely to be a distrust between the public and the policing agencies.
Similarly, teachers will be much more receptive to evaluation feedback that is algorithmically generated if they have a complete understanding of how that feedback is generated, what inputs go into generating it, and that the data has all been verified to be accurate before the evaluation was completed.
The more algorithms influence our lives, the more important it is to increase algorithmic literacy in our schools, and job training programs. We have the opportunity to use data to improve society and people’s lives, but we also have the opportunity to create distrust if information is not used transparently and ethically. Lets use this power wisely to create a better future.
References
101computing.net. (2025, April 10). Ada Lovelace and the First Computer Algorithm. https://www.101computing.net/ada-lovelace-and-the-first-computer-algorithm/
How an algorithm is born. (2026). Canvas. UBC. https://canvas.ubc.ca/courses/179955/pages/11-dot-1-how-an-algorithm-is-born-2?module_item_id=8719813
Medford-Kerr, M. (2025, July 27). Dead or ‘unalive’? How social platforms — and algorithms — are shaping the way we talk. CBC Radio. https://www.cbc.ca/radio/sunday/algospeak-and-content-moderation-1.7594212
Reply All (podcast). (2022, September 23). In Wikipedia.
Sofronieva, E., Beleva, C., Georgieva, G., & Markov, S. (2024). Artificial Intelligence, Algorithm Literacy, Locus of Control, and English Language Skills: A Study among Bulgarian Students in Education. Pedagogy / Pedagogika (0861-3982), 96, 579–599. https://doi.org/10.53656/ped2024-5.01
Talks at Google. (2016, November 2). Weapons of math destruction | Cathy O’Neil | Talks at Google [Video]. YouTube.