Algorithms example sentences

Related (6): algorithm, automation, computation, bias, code, programming

"Algorithms" Example Sentences

  1. The algorithms that run social media are designed to keep you scrolling for hours.
  2. She spent three years studying machine learning algorithms before landing the job at Google.
  3. Nobody really understands how these algorithms decide what you see online anymore.
  4. My feed is completely different from my sister's because of the algorithms.
  5. The algorithms keep recommending videos I've already watched — it's frustrating.
  6. He wrote his dissertation on optimization algorithms and now works for a hedge fund.
  7. Honestly, I think the algorithms are making us all angrier than we need to be.
  8. The company claims their algorithms are fair, but the data says otherwise.
  9. I've noticed the algorithms show me more ads right after I talk about something.
  10. These algorithms are basically predicting what you want before you know it yourself.
  11. She doesn't trust the algorithms — she prefers to browse manually.
  12. The algorithms failed spectacularly during the election cycle last year.
  13. What if we just... didn't let algorithms decide everything for us?
  14. The algorithms are getting smarter, but are they getting better?
  15. He spent all night tweaking the algorithms to improve the recommendation system.
  16. Most people have no idea how deeply algorithms control their daily lives.
  17. The algorithms caught the fraud before any human analyst could.
  18. I'm tired of algorithms knowing me better than I know myself.
  19. These algorithms are trained on biased data — that's the real problem.
  20. The algorithms suggested I buy something I literally just looked at once.
  21. She's been warning about bias in the algorithms used for hiring for years.
  22. The algorithms are neutral — it's how we use them that matters.
  23. Spotify's algorithms know my taste better than my best friend does.
  24. The algorithms filtered out half the job applications before anyone even saw them.
  25. He realized the algorithms were showing him only what he already believed.
  26. The algorithms don't care if the information is true — just if it gets clicks.
  27. Netflix's algorithms are so good at predicting what I'll watch it's almost creepy.
  28. The algorithms are making decisions that affect people's lives, and nobody's checking them.
  29. I disabled all the algorithms I could, but there's always more running underneath.
  30. The algorithms learned to manipulate human behavior — and now we're stuck with it.

Common Phrases

  1. machine learning algorithms
  2. recommendation algorithms
  3. sorting algorithms
  4. search algorithms
  5. algorithmic bias
  6. algorithmic transparency
  7. complex algorithms
  8. optimize algorithms
  9. train algorithms
  10. algorithms determine
  11. algorithms suggest
  12. algorithms filter

Examples by Level

A1
  1. The algorithms show you videos you might like.
  2. Algorithms help computers make decisions very fast.
A2
  1. Social media algorithms decide what you see on your feed every day.
  2. The algorithms learned what kind of music I prefer after I listened for a while.
B1
  1. Most people don't realize how much the algorithms shape what they see online.
  2. The algorithms are supposed to be neutral, but they often reflect the biases of whoever built them.
B2
  1. There's something unsettling about knowing that algorithms are predicting your behavior more accurately than you can yourself.
  2. The company's algorithms were designed to maximize engagement, which meant they naturally promoted the most divisive content.
C1
  1. The opacity of these algorithms has become a genuine threat to informed decision-making in democratic societies.
  2. What troubles her most isn't that algorithms make mistakes — it's that their mistakes are often invisible and systematically biased.
C2
  1. The algorithms that govern content distribution have become so sophisticated that they've essentially created parallel realities for different users.
  2. There exists a peculiar irony in our relationship with algorithms: we've built systems to understand us better than we understand ourselves, yet we remain fundamentally unable to understand how they work.

Examples by Scenario

🏠 Everyday Life
  1. My YouTube recommendations are so weird — the algorithms must think I'm a completely different person than I actually am.
  2. I noticed the algorithms started showing me baby products right after I mentioned wanting kids to my friend.
💼 Workplace
  1. The team spent the morning debugging the algorithms that were causing the system to crash every afternoon.
  2. Our manager asked us to explain how the algorithms work, and honestly, none of us could give her a straight answer.
📊 Business
  1. The algorithms increased our conversion rate by forty percent, which is great until you realize they're also driving away customers who don't fit the profile.
  2. We hired a consultant to audit our algorithms for bias before the regulatory investigation started.
🛒 Buying / Shopping
  1. Amazon's algorithms are too good — they know what I want before I even search for it, which is both convenient and terrifying.
  2. I tried to find a specific product, but the algorithms kept showing me similar items instead of the exact thing I was looking for.
✈️ Travel
  1. The flight booking site's algorithms raised the price every time I refreshed the page — I had to use a VPN to see the real cost.
  2. The hotel recommendations from the algorithms were actually pretty good, but I wonder if they were just showing me the most profitable options.
📚 Education
  1. The professor explained how algorithms work in her intro computer science class, and half the students looked completely lost.
  2. My daughter's school uses algorithms to track which students might drop out, which feels invasive but probably helps some kids.
❤️ Relationships
  1. We met through a dating app's algorithms, which is funny because we're nothing like what either of us thought we were looking for.
  2. He joked that the algorithms knew we'd be compatible before we even knew each other's names.
📰 News / Politics
  1. The algorithms amplified the misinformation so effectively that by the time fact-checkers caught up, millions of people already believed it.
  2. Regulators are finally demanding transparency about how the algorithms decide what news reaches people.
💻 Technology
  1. The new AI uses deep learning algorithms that can generate images from text descriptions — the results are honestly mind-blowing.
  2. She's building algorithms that can detect deepfakes before they spread across social media.
📝 Formal Writing
  1. This study examines how recommendation algorithms in e-commerce platforms disproportionately favor products from established brands over independent sellers.
  2. The proposed legislation would require companies to disclose the basic mechanisms of their algorithms and submit them for regular audits.

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