Domain 2: Recognize AI in the Real World

Introduction

AI is all around us, often in places we don’t even notice. From personalized playlists to real-time navigation, AI powers the tools we use daily. But recognizing how AI functions in everyday life is more than just tech awareness; it’s a literacy skill that helps us be intentional, critical, and ethical users of technology.

Whether you are using Spotify, TikTok, Grammarly, or checking your route on Google Maps, you are already engaging with artificial intelligence. Understanding where and how AI operates allows you to ask better questions, understand its influence, and avoid being passively shaped by unseen algorithms. More importantly, it equips you to analyze AI systems in the real world, so you can make informed choices, advocate for responsible AI, and contribute meaningfully to conversations in your career and community.

Sectors Where AI Plays a Major Role

To build toward an empowered understanding of AI, try conducting your own detailed analysis of AI in a sector you care about. Below, we model what that analysis can look like using three industries. As you read, notice how each example explains what the AI system does, what its impact is, and why it matters.

1. Healthcare

• Applications: AI models are used to support diagnostic decision-making (e.g., identifying tumors on X-rays), automate billing, and predict patient risk (e.g., likelihood of readmission).
• Function: Predictive AI and supervised learning models are trained on large datasets of patient history and clinical outcomes to recognize risk patterns.
• Impact: These tools can improve speed and accuracy in care and reduce administrative burden, but they can also replicate existing biases.
• Significance: One study found that a popular AI tool underestimated the healthcare needs of Black patients because it used past healthcare spending as a stand-in for medical need (Obermeyer et al., 2019). But those spending levels were shaped by systemic racism. Black patients often had less access to quality care, were less likely to be referred to specialists, and faced discrimination in the healthcare system. As a result, the AI assumed they needed less care simply because less money had been spent on them. This shows how biased data can lead to real-world harm when used in AI systems.

2. Hiring and Employment

• Applications: Companies use supervised learning to rank resumes and NLP to assess interview responses.
• Function: These systems are trained on past hiring data to detect patterns in successful candidates. NLP tools may rate communication quality based on tone or word choice.
• Impact: Speeds up hiring, reduces recruiter burden.
• Significance: Amazon’s AI hiring tool was discontinued after it was found to penalize resumes that included terms like “women’s chess club.” This example shows how algorithmic tools can reflect and amplify historical bias. If the training data shows a pattern of men being hired more frequently, the model may perpetuate this inequality.

3. Advertising and Social Media

• Applications: Predictive analytics and behavioral tracking tailor ads, recommend content, and monitor engagement.
• Function: These models continuously track your activity to predict what you’re most likely to click on or share.
• Impact: Increases user engagement and advertising revenue.
• Significance: AI-generated deepfakes have been used during elections to spread false information using videos that appear real. In addition, microtargeting allows advertisers to push tailored messages to vulnerable populations without transparency. This raises serious concerns about democracy, consent, and information literacy.

Why This Matters

If you don’t recognize where AI is at work, you risk letting it shape your worldview without your awareness. Algorithms are not neutral, they reflect the data they are trained on. This can result in:

• Hallucinations or fabricated information, sometimes convincingly.
• Misleading results if low-quality input, like vague or biased prompts, are used.
• False claims or sources that presented as credible
• Reinforcement of racial, gender, or socioeconomic biases
• Lack of representation in recommendations or content
• Spread of misinformation (e.g., political deepfakes)
• Invasive data tracking or surveillance

Developing AI literacy helps you:

• Identify how AI impacts your decisions and access to information
• Assess the fairness and transparency of AI systems
• Choose tools that align with your values and protect your agency
• Engage critically with how AI shapes policies, markets, and cultures

A Note on Evolving Tools

By the time you take this course, specific AI tools may look very different or be replaced entirely. That’s okay. The goal is not to memorize brands or apps, but to understand the patterns: What kind of data does this tool use? What type of AI is powering it? What are the risks and benefits?

Tools evolve. AI literacy lasts. Focus on systems, not software names.

Reflection Prompt (Optional)

Choose one sector (e.g., healthcare, education, finance, hiring, transportation). Identify an AI tool or application used in that field. Use published sources such as peer-reviewed articles, professional orgs, government reports, magazines, and news articles. If you’re unsure how to find credible sources, consider scheduling an appointment with a Montgomery College librarian through the library’s Research Help service Write a brief analysis that explains:

1. What does the AI system do?
2. What kind of data and algorithms does it use?
3. Who benefits? Who might be harmed?
4. Why is this significant in the real world?

References

Digital Promise. (2023). AI Literacy Framework. https://digitalpromise.org

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.

Padmanabhan, B., Zhou, B., Gupta, A. K., Coronado, H., Acharya, S., & Bjarnadóttir, M. (2025). Artificial intelligence and career empowerment [Online course]. University of Maryland. Canvas LMS.

Student Guide to AI. (2025). Student Guide to Artificial Intelligence V2.0. https://studentguidetoai.org

UNESCO. (2021). Recommendation on the Ethics of Artificial Intelligence. https://unesdoc.unesco.org