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Machine AI vs. Human Algorithms

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In 1956, at the first AI conference, there was a consensus that machines could simulate intelligence. The debate was whether this would be best achieved through machine learning algorithms or human-written code. Today, many believe that AI will revolutionize university software. However, some argue that human algorithms, which have been effective and legally compliant for years, are still essential.

We believe that human algorithms are crucial for legally significant decisions, such as calculating final grades on a diploma supplement or determining a faculty’s budget. Algorithmic Intelligence, which is code written by humans, remains vital for universities. Human algorithms follow specific rules, are precise, and lack creativity. They don’t alter themselves. When a decision is made by an algorithm, it’s possible to trace back the reasoning, ensuring legal compliance. Plus, you don’t need tons of examples to train a human algorithm.

Consider a new university using generative AI to assign final grades. They would need many examples to train the model. Then, they might end up giving different grades to similar students on different days without understanding why. That would be disastrous.

Therefore, SMARTA focuses on using AI not for decision-making tasks but for areas where creativity is key, especially in generating text. We use various language models like GPT. Instead of the standard interface, we strictly utilize the API. This approach allows us to seamlessly integrate AI into existing systems like a Campus Management System and control both input and output.

Chatbots and Legal Compliance

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What legal risks do universities face with chatbots? Is there a risk of a chatbot’s wrong answer leading to legal issues? Absolutely! In a 2022 experiment with thousands of students, we observerd that roughly 10% of chatbot responses could have posed legal problems for the university. For example, if a student asks, ‘I am ill. Do I need to send a doctor’s certificate?’ and the official chatbot incorrectly responds, ‘No, just stay at home and get well,’ the university could face legal issues if the student is later de-registered for not submitting the certificate.

Are scripted chatbots a solution? Pro: Scripted chatbots have a low probability of giving wrong answers. They always follow set rules and typically use a limited database, making them generally safe. (This is Position 1 in our matrix.) However, interacting with scripted chatbots can be unsatisfying because they struggle to understand the user’s intent or to respond to unforeseen questions. In SMARTA, we didn’t choose to improve scripted bots (Development-path A).

In Position 9 of the matrix is an AI-chatbot like ChatGPT. These chatbots understand user questions and respond in fluent sentences, offering a high level of user satisfaction. However, the risk of legal issues is greater due to the data they use. For instance, the chatbot might not know a specific university’s examination regulations but might reference another university’s regulations from its general training data. Therefore, we need to both limit (e.g., ‘do not hallucinate faculty staff’) and expand (e.g., ‘use these specific examination regulations’) the data used by the chatbot, which is Development-path B. At SMARTA, we are pursuing this path to control the input, processing, and output of the chatbot.