6 Threats AI and ML Technologies Pose to Data Governance | Magic EdTech

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6 Threats AI and ML Technologies Pose to Data Governance

  • Published on: September 28, 2023
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  • Updated on: July 3, 2025
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  • Reading Time: 6 mins
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  • 2,930
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According to a recent report by Grand View Research, the AI in education market is projected to expand by 36.0% from 2022 to 2030. As educational institutions increasingly invest in AI and EdTech, the volume of data generated will skyrocket. That’s an avalanche of information to manage, and it’s not without its perils. So, how do we mitigate the threats of AI and machine learning (ML) to educational institutions?

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6 Risks AI and Machine Learning Pose to Educational Data Governance

AI in education can bring significant benefits to educational institutions. But without the right data governance, it also carries certain risks that need careful consideration. Let’s categorize and explore these risks and discuss ways to manage them effectively.

1. Data-Related Risks

AI systems heavily rely on the data they are trained on. Poor quality, incomplete, or data in the wrong context can lead to erroneous or biased outcomes. Ensure your data is high quality and relevant to the AI system’s purpose.

2. AI/ML Attacks

There is a growing concern about machine learning data governance and potential security weaknesses in AI systems. Data governance in education is prone to attacks that can fall into categories such as data privacy attacks, data poisoning, and model extraction. Assessing and addressing these vulnerabilities is essential to protect AI systems from malicious intent.

  • Data Privacy Attacks: In data privacy attacks, attackers may infer sensitive information from the training dataset, compromising data privacy.
  • Training Data Poisoning: Data poisoning involves contaminating the training data, affecting the AI system’s learning process or output.
  • Adversarial Inputs: Adversarial inputs are designed to bypass AI systems’ classifiers and can be used maliciously.
  • Model Extraction: Model extraction attacks involve stealing the AI model itself, which can lead to further risks and misuse of the model.

3. Testing and Trust

AI systems may evolve and become sensitive to environmental changes. Testing and validating AI systems can be challenging due to their dynamic nature. Lack of transparency in AI systems can also lead to trust issues. Bias in AI systems is another concern that could result in unfair outcomes.

4. Compliance

AI implementations should comply with existing internal policies and regulations. Regulatory bodies are increasingly interested in AI deployments, and organizations must monitor and adhere to relevant regulations.

5. Discrimination in AI

AI systems can potentially lead to discriminatory outcomes if not implemented correctly. Factors such as biased data, improper training, or alternative data sources can contribute to discrimination. Existing legal and regulatory frameworks prohibit discrimination and must be adhered to.

6. Interpretability

Interpretability relates to the ability to understand how AI systems make decisions. This is crucial for detecting and appealing incorrect decisions, conducting security audits, and ensuring regulatory compliance. Interpretability also helps build trust in AI systems.

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Strategies and Best Practices to Reduce AI and ML-Related Risks

Here are some of the best practices you can follow to reduce AI and ML-related risks in your educational institution.

Keep People Involved

It would be best to involve teachers and others to see what’s happening and make decisions when using AI. You must ensure people stay a part of the process when using AI.

Make AI Fit Education Goals

Decision makers in education and those who study it judge how good an AI tool is by what it does and how well it matches our teaching and learning goals. Think about what makes a good AI tool for education.

Use Modern Teaching Ideas

You need to use the latest teaching ideas and learn from experienced teachers. It would help if you also made sure that AI is fair for everyone, including students with disabilities and those who are learning English.

Build Trust

Many people need to learn about educational technology and AI. You must work on building trust and ensure that new educational technology is trustworthy.

Study How AI Fits Different Situations

Research how AI can be used in different situations, like with different types of learners and in other places. Researchers should also find ways to make AI safer and more trustworthy for education.

Create Rules for AI in Education

We already have rules for privacy and security in educational technology. But with AI, we need new rules and guidelines to make sure it’s safe and functional. Include everyone’s opinions in creating these rules, and they should cover things like how AI is used and how data is handled in education.

AI presents both opportunities and risks. Understanding and categorizing these risks, implementing strong governance, and ensuring interpretability and fairness are essential to managing AI in organizations effectively.

At Magic EdTech, a robust data governance framework to empower educators. We help you stand at the forefront, shaping the future of education through technology. Schedule a call with our experts today and create a platform that offers AI-powered educational excellence.

 

FAQs

Track three signals nightly:

1. Sudden shifts in class‑level prediction accuracy

2. Anomalous spikes in input distributions (e.g., grade files with out‑of‑range values)

3. Divergence between production‑model outputs and a locked “shadow” model trained on clean data. Any two triggers in the same 24‑hour window should auto‑escalate to the security team for sample review.

Offer decision tracebacks: a short, human‑readable log that lists the top features influencing each recommendation (e.g., “assignment‑completion rate: high impact, quiz mastery: medium impact”). Pair this with a periodic bias audit summary—an external reviewer’s report comparing model output across demographic groups, so stakeholders see both the “how” and the “fairness check” in plain language.

Use a 3‑bucket model:

1. Core license & hosting.

2. Governance overhead” (model monitoring, bias audits, staff training—budget ≈15 % of license)

3. Continuous improvement” (feature tweaks, retraining data—≈10 %).

4. Present ROI as cost per additional student mastering a standard vs. baseline; including governance costs up front prevents nasty surprises in year two.

Phase it:

Months 0–3: Require every new AI product to supply model cards and feature‑importance reports.

Months 4–9: Implement an internal interpretability toolkit (e.g., SHAP or LIME wrappers) so analysts can probe models in‑house.

Months 10–12: Publish an annual transparency brief summarising findings and improvements.
This staged approach lets you deploy promising tools quickly while building the interpretability muscle in parallel.

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