IELTS Academic Writing Task 2: Step-by-Step Band 9+ Tutorial (LingExam | Ultra-Interactive)
Welcome! In this tutorial, you’ll master IELTS Academic Writing Task 2 (Complex Discussion Essay) using a high-level topic about artificial intelligence and society.
Sample Task:
Some people argue that the widespread use of artificial intelligence (AI) will create more problems than it solves—such as job losses, bias, and privacy risks. Others believe AI will deliver major benefits for individuals and communities. Discuss both views and give your own opinion.
Follow each step below. Hover on any step to highlight and reveal advanced tips for Band 7+ control of task response, coherence, and lexical resource.
Sample Task:
Some people argue that the widespread use of artificial intelligence (AI) will create more problems than it solves—such as job losses, bias, and privacy risks. Others believe AI will deliver major benefits for individuals and communities. Discuss both views and give your own opinion.
Follow each step below. Hover on any step to highlight and reveal advanced tips for Band 7+ control of task response, coherence, and lexical resource.
How to Answer IELTS Academic Writing Task 2 (Complex Discussion Essay): 12 Crucial Steps
1
Identify exactly what the prompt asks and the verbs that govern your response. Notice “discuss both views and give your own opinion,” which requires balanced coverage and a clear stance. Underline keywords such as “widespread use,” “artificial intelligence,” “more problems than it solves,” “job losses,” “bias,” “privacy risks,” “major benefits,” and “for individuals and communities.” Distinguish between effects (social, economic, ethical) and mechanisms (how AI causes those effects). Avoid drifting into a pure advantages–disadvantages list; stay anchored to the debate about overall net impact. Decide whether the scope is present, near future, or general; IELTS expects timeless, academically cautious claims. Recognise that “more problems than it solves” implies a comparative evaluation, not merely listing negatives. Mark the command to include your own opinion; essays without an explicit position risk Band 6 for Task Response. Finally, estimate how many body ideas you can develop well in ~260–320 words without superficiality.
2
Generate concrete ideas for each perspective before choosing a thesis. For the “problems” side, think about displacement of routine jobs, algorithmic bias affecting hiring or lending, data misuse, surveillance risks, and over-reliance on automated systems. For the “benefits” side, consider medical diagnostics, safer transport systems, accessibility tools for disabled users, personalised education, and efficiency gains for essential services. Attach at least one realistic example to every idea so you can write vivid, credible sentences. Prefer domain-neutral examples that readers from any country can recognise. Group overlapping ideas to avoid repetition and to help paragraph unity. Eliminate weak points that you cannot explain in two or three strong sentences. Prioritise ideas with clear causes, effects, and remedies. Keep track of technical terms and plan to paraphrase them simply for clarity and coherence.
3
Adopt a position that you can justify consistently across the essay. You might argue that AI yields net benefits if governance and ethics are robust; or you may claim risks currently outweigh gains due to unregulated deployment. Choose one. Express the stance in one precise sentence at the end of your introduction. Avoid hedging so much that your opinion disappears; equally, avoid absolute certainty that invites contradiction. Ensure your thesis logically predicts your body structure (e.g., first explain concerns, then counterbalance with benefits and conditions). Make sure the thesis answers the comparative dimension embedded in “more problems than it solves.” Keep the wording concise and academic, avoiding emotive exaggeration. Remember: a strong thesis guides paragraph topic sentences, examples, and your conclusion. Consistency here boosts Coherence and Cohesion to Band 7+.
4
Use a clear macro-structure that supports balanced discussion. A reliable pattern is: Introduction (paraphrase + thesis) → Body 1 (arguments for “more problems”) → Body 2 (arguments for “major benefits” + conditionality) → Conclusion (summarise and restate stance). Decide whether to present the side you disagree with first; this often sounds more balanced. Allocate roughly 45–55 words to the introduction, 90–120 words to each body paragraph, and 35–45 words to the conclusion. Ensure each body paragraph has one central idea supported by explanation and a concrete example. Use cause–effect sequencing and comparison–contrast linkers to maintain flow. Avoid inserting new examples in the conclusion. Keep the reader oriented with signposting phrases that reflect your thesis language.
5
Write an opening that restates the debate without copying the prompt. Replace “widespread use of AI” with “the rapid integration of intelligent systems,” and “create more problems than it solves” with “produce net social harm.” Avoid technical jargon; IELTS values clarity over buzzwords. Keep two sentences: one framing the debate, one giving your thesis. Example: “As AI tools permeate workplaces and public services, some fear their social costs will outstrip any gains, whereas others predict transformative benefits. This essay will examine both perspectives and argue that, provided strong safeguards are enforced, AI is more likely to enhance than damage human welfare.” This sets expectations and evidences paraphrasing skill for Lexical Resource.
6
Open with a topic sentence that captures the core concern. For instance: “Critics contend that accelerated automation displaces vulnerable workers and embeds unfairness at scale.” Explain the mechanism: routine tasks are easiest to automate, affecting lower-income groups first; retraining pathways are often underfunded; short-term disruption fuels inequality. Add a second mechanism: biased data can yield discriminatory outcomes in recruitment or credit approval, magnifying existing social divisions. Provide a concise, plausible example that a global examiner can follow. Tie back to consequences—erosion of trust, privacy violations, and safety risks when opaque systems fail. Close the paragraph by linking to the essay’s question: these harms suggest AI may indeed generate more problems unless controls exist.
7
Begin with a mirrored topic sentence that advances the “benefits” case. For example: “Supporters argue that intelligent systems amplify human capability, improving accuracy, safety, and access to essential services.” Explain with health care (pattern recognition assisting diagnosis), transport (driver-assistance reducing errors), and accessibility (real-time captions, translation, and custom interfaces). Offer a concrete example that feels realistic rather than sensational. Emphasise complementarity: AI handles scale and patterning; humans supervise nuance and ethics. Link to productivity gains that can fund social programmes if distributed fairly. Close by connecting benefits to conditions such as transparency, auditing, and user oversight—preparing ground for your balanced conclusion.
8
Articulate a position that remains rigorous under scrutiny. If you argue for net benefits, specify safeguards that make those benefits credible: bias testing, data minimisation, human-in-the-loop, explainability for high-stakes uses, and re-skilling funds. If you argue risks outweigh gains, justify with governance gaps, accountability deficits, and unequal power over data. Use careful modal verbs (“can,” “may,” “tends to”) to avoid over-claiming. Demonstrate evaluation: weigh magnitude, likelihood, and reversibility of harms versus benefits. Keep tone objective and solutions-oriented. This analytical clarity signals Band 8–9 reasoning rather than opinionated assertion. End with a mini-synthesis that anticipates your conclusion’s wording.
9
Guide readers with varied, accurate linkers while avoiding repetition. Contrast: “however,” “nevertheless,” “by contrast.” Addition: “furthermore,” “moreover.” Cause–effect: “consequently,” “therefore,” “as a result.” Condition: “provided that,” “assuming,” “if.” Use pronoun referencing to avoid repeating “AI” in every sentence. Maintain parallel grammatical structures when listing. Ensure each sentence connects logically to the previous one; cohesion emerges from idea flow, not just connectors. Avoid starting every sentence with a linker; this sounds mechanical and can lower Coherence/Cohesion.
10
Show controlled flexibility, not random complexity. Replace common verbs (“cause,” “help”) with precise ones (“engender,” “facilitate,” “mitigate”). Use complex noun phrases (“the rapid diffusion of data-driven systems”), relative clauses, and conditional sentences to display grammatical range. Balance simple and complex sentences for readability. Avoid trendy technical jargon that may be misused; clarity beats buzzwords. Use accurate collocations like “algorithmic bias,” “data protection,” “regulatory oversight,” and “ethical safeguards.” Maintain consistent tense and academic register. Check articles and prepositions around abstract nouns, which often lower scores when mishandled.
11
Always reserve 2–3 minutes to refine. Verify that both views are developed, your opinion is unmistakable, and examples are specific and plausible. Remove redundancy and filler phrases that consume word count without adding meaning. Check subject–verb agreement, article usage, punctuation around relative clauses, and comma splices. Replace vague nouns (“things,” “stuff,” “problems”) with precise terms (“externalities,” “displacement,” “privacy breaches”). Ensure paragraph unity; delete any sentence that does not serve the paragraph’s controlling idea. Read silently for flow; then scan for lexical repetition and vary where safe.
12
Write a short conclusion that synthesises and echoes your thesis. One sentence can summarise the debate; one sentence can state your final position with a conditional clause if appropriate. Example: “In summary, while automation can displace workers and entrench unfairness, well-regulated AI is more likely to elevate public services and daily life, provided transparency and accountability are non‑negotiable.” Avoid new evidence or ideas. Keep the tone confident yet measured. Ensure word count remains comfortably above 250 words without exceeding your time budget. Finish with wording that mirrors, not copies, your thesis language for a cohesive feel.
Band 9 Essay Plan & Example Notes (AI & Society)
Example Introduction: “As intelligent systems permeate workplaces and public services, some fear their costs will eclipse any gains, whereas others anticipate broad social improvements. This essay discusses both views and argues that, under firm oversight, AI will deliver more good than harm.”
Example Topic Sentences: “Critics warn that AI can entrench inequality and compromise privacy at scale…” “Supporters counter that AI augments human judgement and expands access to vital services…”
Example Conclusion: “Ultimately, with rigorous governance and inclusive upskilling, AI is likelier to enhance collective welfare than undermine it.”
Example Topic Sentences: “Critics warn that AI can entrench inequality and compromise privacy at scale…” “Supporters counter that AI augments human judgement and expands access to vital services…”
Example Conclusion: “Ultimately, with rigorous governance and inclusive upskilling, AI is likelier to enhance collective welfare than undermine it.”
IELTS Academic Writing Task 2
Task:
Some people argue that the widespread use of artificial intelligence (AI) will create more problems than it solves—such as job losses, bias, and privacy risks. Others believe AI will deliver major benefits for individuals and communities. Discuss both views and give your own opinion.
Write at least 250 words. Present a well‑structured discussion and a clear opinion, supported by relevant examples.
Official Timer (with Custom Option)
Time Left:
40:00
Tip: IELTS recommends spending about 40 minutes on Task 2. You can customise the timer to match your practice routine.
Your Answer:
Words: 0 / 1000
Max capacity: 1000 words
Before you submit: Ensure you discussed both views, gave a clear opinion, used specific examples, and stayed within the 1000‑word capacity.
Band 9 Model Answer & Step‑by‑Step Explanation
Read the sample answer first. Then open the guided breakdown to see exactly how it earns a high band: thesis clarity, balanced coverage, logical development, precise vocabulary, and a decisive conclusion.
Model Answer (≈290 words)
As intelligent systems become woven into daily life, some fear that artificial intelligence will generate more harm than benefit, particularly through job displacement, biased decision‑making, and threats to privacy. Others, however, argue that AI can enhance welfare by improving public services, expanding access, and raising productivity. This essay discusses both views and argues that, with firm oversight, the advantages are likely to prevail.
Critics emphasise that rapid automation typically targets routine roles first, exposing lower‑income workers to heightened insecurity. If retraining opportunities are limited or poorly funded, the transition can deepen inequality. A second concern is algorithmic bias: when historical data reflect social prejudices, models may replicate them at scale in hiring, lending, or policing. Finally, pervasive data collection can erode privacy and trust, particularly where consent is opaque or safeguards are weak. These risks are real and, if ignored, could undermine social cohesion.
Supporters counter that AI can power demonstrable gains in accuracy, safety, and inclusion. In healthcare, pattern‑recognition systems help clinicians detect anomalies earlier and allocate resources more efficiently. In transport, driver‑assistance can reduce human error; in education, adaptive tools personalise practice for underserved learners and those with disabilities. Crucially, AI complements rather than replaces human judgment in high‑stakes settings, enabling professionals to focus on nuance and empathy.
In my view, AI’s net effect is positive provided three conditions are met: rigorous audits to detect bias, data‑protection rules that prioritise user agency, and large‑scale reskilling so workers can transition into newly created roles. Where these constraints are enforced, societies capture most benefits while containing the worst externalities.
In conclusion, although unregulated deployment may entrench inequality and privacy harms, transparent governance and human oversight make it more likely that AI will elevate, not diminish, collective welfare.
Critics emphasise that rapid automation typically targets routine roles first, exposing lower‑income workers to heightened insecurity. If retraining opportunities are limited or poorly funded, the transition can deepen inequality. A second concern is algorithmic bias: when historical data reflect social prejudices, models may replicate them at scale in hiring, lending, or policing. Finally, pervasive data collection can erode privacy and trust, particularly where consent is opaque or safeguards are weak. These risks are real and, if ignored, could undermine social cohesion.
Supporters counter that AI can power demonstrable gains in accuracy, safety, and inclusion. In healthcare, pattern‑recognition systems help clinicians detect anomalies earlier and allocate resources more efficiently. In transport, driver‑assistance can reduce human error; in education, adaptive tools personalise practice for underserved learners and those with disabilities. Crucially, AI complements rather than replaces human judgment in high‑stakes settings, enabling professionals to focus on nuance and empathy.
In my view, AI’s net effect is positive provided three conditions are met: rigorous audits to detect bias, data‑protection rules that prioritise user agency, and large‑scale reskilling so workers can transition into newly created roles. Where these constraints are enforced, societies capture most benefits while containing the worst externalities.
In conclusion, although unregulated deployment may entrench inequality and privacy harms, transparent governance and human oversight make it more likely that AI will elevate, not diminish, collective welfare.
Step‑by‑Step Explanation (Open each point)
1
Task Fulfilment & Thesis Placement.
The introduction paraphrases the question to avoid copying while signalling both sides of the debate. It ends with a concise, arguable thesis (“advantages are likely to prevail with oversight”), which directly answers the comparative prompt (“more problems than it solves?”). Placing the thesis at the end of the introduction provides a roadmap for the reader. The essay then mirrors the thesis through paragraph sequencing: risks first, benefits second, evaluation last. This alignment between thesis and structure is a hallmark of Band 8–9 Task Response. The conclusion restates stance in fresh wording rather than repeating the thesis verbatim, which maintains cohesion without redundancy.
2
Balanced Coverage of Both Views.
The first body develops the “problems” side (displacement, bias, privacy) with mechanisms and plausible consequences, not just labels. The second body develops the “benefits” side (healthcare, transport, education, accessibility), again explaining mechanisms and giving generalisable examples. Balance is not 50–50 word count; it means each perspective is addressed substantively before the writer’s evaluation. This prevents a one‑sided tone that can cap scores.
3
Idea Development & Example Quality.
Each claim is followed by a “how/why” clause and a realistic domain example. For instance, bias arises because historic datasets encode prejudice; healthcare gains come from pattern recognition aiding early detection. IELTS examiners value clear causal chains over name‑dropping technical terms. Examples are internationally recognisable and do not rely on niche statistics the reader cannot verify.
4
Coherence & Paragraph Unity.
Each body paragraph has one controlling idea (risks vs. benefits). Sentences progress logically using cause–effect and contrast. Internal summaries (“These risks are real…”) reinforce unity and prepare transitions. The final body paragraph introduces conditionality (“provided three conditions are met”), knitting the debate into a principled judgement rather than a mere list.
5
Lexical Resource (Range & Precision).
The essay uses accurate collocations such as “algorithmic bias,” “data‑protection rules,” “human oversight,” and “externalities.” Verbs like “erode,” “replicate,” and “elevate” replace generic “make”/“do.” Nominal groups (“rapid automation,” “pervasive data collection”) show advanced control without sounding inflated. Avoiding buzzwords keeps clarity high, which examiners reward.
6
Grammar Range & Accuracy.
The essay blends simple and complex sentences, using relative clauses (“where consent is opaque”), concessive structures (“although unregulated deployment…”), and conditionals (“provided three conditions are met”). Punctuation supports meaning, and subject‑verb agreement and article use are controlled—common error hotspots at lower bands.
7
Answering the Comparative Core.
The prompt’s logic is comparative (“more problems than it solves”). The essay evaluates magnitude and reversibility of harms vs. benefits and introduces mitigation (audits, privacy, reskilling). This shows the writer is not merely listing points but weighing them, which is crucial for a high Task Response score.
8
Academic Tone & Hedging.
Modality (“can,” “may,” “likely to”) keeps claims proportionate. The essay avoids emotive language and quantifies conditions for success. This scholarly tone suggests critical distance rather than advocacy, aligning with Band 8–9 descriptors.
9
Concise Conclusion (No New Ideas).
The conclusion synthesises both sides and restates the stance with the same conditionality. It introduces no fresh examples, thereby preserving coherence. The wording mirrors but does not duplicate the thesis, which gives the essay a finished, professional feel.
10
Word Count & Time Management.
At roughly 270–300 words, the answer meets the minimum comfortably without drifting. The four‑paragraph structure helps allocate time: ~50 words for the introduction, ~100–120 per body, ~40 for the conclusion. This discipline supports clarity and reduces last‑minute edits.
20 Crucial Words for This IELTS Task 2 (AI & Society)
Tap each item to reveal pronunciation (BrE/AmE), parts of speech, patterns, a clear definition, a model sentence with a short meaning note, a common synonym, and typical learner mistakes. Hover to see the soft glow.
algorithmic ▼
Phonetics: /ˌæl.ɡəˈrɪð.mɪk/ (BrE), /ˌæl.ɡəˈrɪð.mɪk/ (AmE)
Part of speech: adjective
Pattern: algorithmic + bias/decision/method
Definition: Relating to rules or procedures a computer follows to solve a problem or make a decision.
Example: “Concerns about algorithmic bias have grown as AI tools spread.” (meaning: bias caused by the way algorithms work or are trained)
Synonym: rule‑based, procedural
Common mistakes: Writing “algorismic”; using it as a noun (“an algorithmic”) instead of adjective.
Part of speech: adjective
Pattern: algorithmic + bias/decision/method
Definition: Relating to rules or procedures a computer follows to solve a problem or make a decision.
Example: “Concerns about algorithmic bias have grown as AI tools spread.” (meaning: bias caused by the way algorithms work or are trained)
Synonym: rule‑based, procedural
Common mistakes: Writing “algorismic”; using it as a noun (“an algorithmic”) instead of adjective.
bias ▼
Phonetics: /ˈbaɪ.əs/ (BrE), /ˈbaɪ.əs/ (AmE)
Part of speech: noun (also verb: to bias)
Pattern: bias in/against/towards; reduce/mitigate bias
Definition: Systematic and unfair preference or prejudice that affects outcomes.
Example: “If training data are skewed, models may reproduce social bias.” (meaning: unfair tilt in results)
Synonym: prejudice, skew
Common mistakes: Using “bias” as an adjective (“a bias system” → “a biased system”).
Part of speech: noun (also verb: to bias)
Pattern: bias in/against/towards; reduce/mitigate bias
Definition: Systematic and unfair preference or prejudice that affects outcomes.
Example: “If training data are skewed, models may reproduce social bias.” (meaning: unfair tilt in results)
Synonym: prejudice, skew
Common mistakes: Using “bias” as an adjective (“a bias system” → “a biased system”).
oversight ▼
Phonetics: /ˈəʊ.və.saɪt/ (BrE), /ˈoʊ.vɚ.saɪt/ (AmE)
Part of speech: noun
Pattern: regulatory/independent oversight; oversight of/over
Definition: Supervision to ensure systems work properly and ethically.
Example: “Robust public oversight can prevent harmful deployments.” (meaning: strong supervision)
Synonym: supervision, monitoring
Common mistakes: Confusing with “overlook” (to fail to notice).
Part of speech: noun
Pattern: regulatory/independent oversight; oversight of/over
Definition: Supervision to ensure systems work properly and ethically.
Example: “Robust public oversight can prevent harmful deployments.” (meaning: strong supervision)
Synonym: supervision, monitoring
Common mistakes: Confusing with “overlook” (to fail to notice).
displacement ▼
Phonetics: /dɪsˈpleɪs.mənt/ (BrE), /dɪsˈpleɪs.mənt/ (AmE)
Part of speech: noun
Pattern: job/workforce displacement; displacement of workers
Definition: When workers lose roles because tasks are automated.
Example: “Routine roles face higher risks of job displacement.” (meaning: being pushed out of jobs)
Synonym: replacement, redundancy
Common mistakes: Using “misplacement” (different meaning).
Part of speech: noun
Pattern: job/workforce displacement; displacement of workers
Definition: When workers lose roles because tasks are automated.
Example: “Routine roles face higher risks of job displacement.” (meaning: being pushed out of jobs)
Synonym: replacement, redundancy
Common mistakes: Using “misplacement” (different meaning).
reskilling ▼
Phonetics: /ˌriːˈskɪl.ɪŋ/ (BrE), /ˌriːˈskɪl.ɪŋ/ (AmE)
Part of speech: noun (also verb: reskill)
Pattern: invest in/reskilling programmes; reskilling for + sector
Definition: Training people to learn new abilities for different jobs.
Example: “Targeted reskilling helps employees move into AI‑complementary roles.” (meaning: training to change role)
Synonym: retraining
Common mistakes: Confusing with “upscaling” (should be “upskilling” for higher skills).
Part of speech: noun (also verb: reskill)
Pattern: invest in/reskilling programmes; reskilling for + sector
Definition: Training people to learn new abilities for different jobs.
Example: “Targeted reskilling helps employees move into AI‑complementary roles.” (meaning: training to change role)
Synonym: retraining
Common mistakes: Confusing with “upscaling” (should be “upskilling” for higher skills).
governance ▼
Phonetics: /ˈɡʌv.ə.nəns/ (BrE), /ˈɡʌv.ɚ.nəns/ (AmE)
Part of speech: noun
Pattern: AI/data/technical governance; governance framework
Definition: Rules and processes used to direct and control systems.
Example: “Clear governance is vital for high‑risk AI applications.” (meaning: structured control)
Synonym: regulation, stewardship
Common mistakes: Using “government” when you mean rules/controls.
Part of speech: noun
Pattern: AI/data/technical governance; governance framework
Definition: Rules and processes used to direct and control systems.
Example: “Clear governance is vital for high‑risk AI applications.” (meaning: structured control)
Synonym: regulation, stewardship
Common mistakes: Using “government” when you mean rules/controls.
transparency ▼
Phonetics: /trænˈspær.ən.si/ (BrE), /trænˈsper.ən.si/ (AmE)
Part of speech: noun
Pattern: transparency about/in; increase/ensure transparency
Definition: Openness about how systems make decisions.
Example: “Greater transparency helps users trust automated decisions.” (meaning: being open/clear)
Synonym: openness, clarity
Common mistakes: Misspelling “transparancy”.
Part of speech: noun
Pattern: transparency about/in; increase/ensure transparency
Definition: Openness about how systems make decisions.
Example: “Greater transparency helps users trust automated decisions.” (meaning: being open/clear)
Synonym: openness, clarity
Common mistakes: Misspelling “transparancy”.
accountability ▼
Phonetics: /əˌkaʊn.təˈbɪl.ɪ.ti/ (BrE), /əˌkaʊn.t̬əˈbɪl.ə.ti/ (AmE)
Part of speech: noun
Pattern: ensure/assign accountability; accountability for outcomes
Definition: Responsibility for decisions and their consequences.
Example: “Clear accountability lines deter irresponsible AI use.” (meaning: someone is answerable)
Synonym: responsibility, answerability
Common mistakes: Using “accountable” as a noun (“the accountable”).
Part of speech: noun
Pattern: ensure/assign accountability; accountability for outcomes
Definition: Responsibility for decisions and their consequences.
Example: “Clear accountability lines deter irresponsible AI use.” (meaning: someone is answerable)
Synonym: responsibility, answerability
Common mistakes: Using “accountable” as a noun (“the accountable”).
explainability ▼
Phonetics: /ɪkˌspleɪ.nəˈbɪl.ɪ.ti/ (BrE), /ɪkˌspleɪ.nəˈbɪl.ə.ti/ (AmE)
Part of speech: noun
Pattern: model/system explainability; improve/examine explainability
Definition: How easily a system’s decisions can be understood.
Example: “In healthcare, explainability is essential for clinical trust.” (meaning: clarity of reasons)
Synonym: interpretability
Common mistakes: Confusing with “explanation” (a specific account).
Part of speech: noun
Pattern: model/system explainability; improve/examine explainability
Definition: How easily a system’s decisions can be understood.
Example: “In healthcare, explainability is essential for clinical trust.” (meaning: clarity of reasons)
Synonym: interpretability
Common mistakes: Confusing with “explanation” (a specific account).
surveillance ▼
Phonetics: /səˈveɪ.ləns/ (BrE), /sɚˈveɪ.ləns/ (AmE)
Part of speech: noun
Pattern: mass/automated surveillance; surveillance of + group
Definition: Close observation, often of people or data, usually by authorities or companies.
Example: “Extensive surveillance can chill free expression.” (meaning: monitoring that affects behaviour)
Synonym: monitoring
Common mistakes: Spelling “surveilance” (missing second “l”).
Part of speech: noun
Pattern: mass/automated surveillance; surveillance of + group
Definition: Close observation, often of people or data, usually by authorities or companies.
Example: “Extensive surveillance can chill free expression.” (meaning: monitoring that affects behaviour)
Synonym: monitoring
Common mistakes: Spelling “surveilance” (missing second “l”).
data minimisation ▼
Phonetics: /ˈdeɪ.tə ˌmɪn.ɪ.maɪˈzeɪ.ʃən/ (BrE), /ˈdeɪ.t̬ə ˌmɪn.ə.məˈzeɪ.ʃən/ (AmE)
Part of speech: noun phrase
Pattern: adopt/practise data minimisation; data minimisation policies
Definition: Collecting only the data strictly needed for a task.
Example: “Privacy frameworks encourage data minimisation to reduce risk.” (meaning: limit data collection)
Synonym: data reduction (context‑dependent)
Common mistakes: Writing “minimum data” when you mean the practice.
Part of speech: noun phrase
Pattern: adopt/practise data minimisation; data minimisation policies
Definition: Collecting only the data strictly needed for a task.
Example: “Privacy frameworks encourage data minimisation to reduce risk.” (meaning: limit data collection)
Synonym: data reduction (context‑dependent)
Common mistakes: Writing “minimum data” when you mean the practice.
augmentation ▼
Phonetics: /ˌɔːɡ.menˈteɪ.ʃən/ (BrE), /ˌɔːɡ.menˈteɪ.ʃən/ (AmE)
Part of speech: noun (verb: augment)
Pattern: human/skill augmentation; augment + performance/capacity
Definition: Enhancing human ability with tools or systems.
Example: “AI‑driven augmentation lets clinicians focus on complex judgement.” (meaning: support, not replacement)
Synonym: enhancement
Common mistakes: Using only “replacement” when the idea is “support”.
Part of speech: noun (verb: augment)
Pattern: human/skill augmentation; augment + performance/capacity
Definition: Enhancing human ability with tools or systems.
Example: “AI‑driven augmentation lets clinicians focus on complex judgement.” (meaning: support, not replacement)
Synonym: enhancement
Common mistakes: Using only “replacement” when the idea is “support”.
diagnostics ▼
Phonetics: /ˌdaɪ.əɡˈnɒs.tɪks/ (BrE), /ˌdaɪ.əɡˈnɑːs.tɪks/ (AmE)
Part of speech: noun (plural in form, singular meaning in this context)
Pattern: medical/AI‑assisted diagnostics; improvements in diagnostics
Definition: Processes and tools used to identify diseases or faults.
Example: “AI can improve diagnostics by spotting subtle patterns in scans.” (meaning: better detection)
Synonym: detection, analysis (context‑dependent)
Common mistakes: Treating it like a verb (“to diagnostic”).
Part of speech: noun (plural in form, singular meaning in this context)
Pattern: medical/AI‑assisted diagnostics; improvements in diagnostics
Definition: Processes and tools used to identify diseases or faults.
Example: “AI can improve diagnostics by spotting subtle patterns in scans.” (meaning: better detection)
Synonym: detection, analysis (context‑dependent)
Common mistakes: Treating it like a verb (“to diagnostic”).
inequity ▼
Phonetics: /ɪnˈek.wɪ.ti/ (BrE), /ɪnˈek.wə.t̬i/ (AmE)
Part of speech: noun
Pattern: social/health/wealth inequity; inequity in access to + noun
Definition: Unfair or unjust differences between groups.
Example: “Poor connectivity can worsen digital inequity.” (meaning: unfair gap)
Synonym: injustice, disparity
Common mistakes: Confusing with “inequality” (broader statistical difference); “inequity” stresses unfairness.
Part of speech: noun
Pattern: social/health/wealth inequity; inequity in access to + noun
Definition: Unfair or unjust differences between groups.
Example: “Poor connectivity can worsen digital inequity.” (meaning: unfair gap)
Synonym: injustice, disparity
Common mistakes: Confusing with “inequality” (broader statistical difference); “inequity” stresses unfairness.
scalability ▼
Phonetics: /ˌskeɪ.ləˈbɪl.ɪ.ti/ (BrE), /ˌskeɪ.ləˈbɪl.ə.ti/ (AmE)
Part of speech: noun
Pattern: high/low scalability; assess/improve scalability
Definition: Ability of a system to handle growth without loss of performance.
Example: “Cloud tools offer the scalability needed for national services.” (meaning: can grow smoothly)
Synonym: expandability
Common mistakes: Using “scale” as a noun for this property (“good scale”).
Part of speech: noun
Pattern: high/low scalability; assess/improve scalability
Definition: Ability of a system to handle growth without loss of performance.
Example: “Cloud tools offer the scalability needed for national services.” (meaning: can grow smoothly)
Synonym: expandability
Common mistakes: Using “scale” as a noun for this property (“good scale”).
mitigation ▼
Phonetics: /ˌmɪt.ɪˈɡeɪ.ʃən/ (BrE), /ˌmɪt.əˈɡeɪ.ʃən/ (AmE)
Part of speech: noun (verb: mitigate)
Pattern: risk/harms mitigation; mitigation measures/strategies
Definition: Actions taken to reduce the seriousness of a problem.
Example: “Bias tests and audits are key mitigation steps.” (meaning: ways to reduce harm)
Synonym: reduction, alleviation
Common mistakes: Using “mitigate” with “against” (say “mitigate risk,” not “mitigate against”).
Part of speech: noun (verb: mitigate)
Pattern: risk/harms mitigation; mitigation measures/strategies
Definition: Actions taken to reduce the seriousness of a problem.
Example: “Bias tests and audits are key mitigation steps.” (meaning: ways to reduce harm)
Synonym: reduction, alleviation
Common mistakes: Using “mitigate” with “against” (say “mitigate risk,” not “mitigate against”).
safeguards ▼
Phonetics: /ˈseɪf.ɡɑːdz/ (BrE), /ˈseɪf.ɡɑːrdz/ (AmE)
Part of speech: noun (plural; singular: safeguard)
Pattern: legal/ethical/technical safeguards; safeguards for/against
Definition: Protective rules or tools that prevent harm.
Example: “Strong safeguards protect privacy in public services.” (meaning: protections)
Synonym: protections, checks
Common mistakes: Treating as a verb in formal writing (prefer “put in place safeguards”).
Part of speech: noun (plural; singular: safeguard)
Pattern: legal/ethical/technical safeguards; safeguards for/against
Definition: Protective rules or tools that prevent harm.
Example: “Strong safeguards protect privacy in public services.” (meaning: protections)
Synonym: protections, checks
Common mistakes: Treating as a verb in formal writing (prefer “put in place safeguards”).
externalities ▼
Phonetics: /ˌek.stɜːˌnælˈɪt.iːz/ (BrE), /ˌek.stɚˌnælˈɪt̬.iːz/ (AmE)
Part of speech: noun (plural)
Pattern: negative/positive externalities; externalities of + process
Definition: Side‑effects of an activity that affect others not directly involved.
Example: “Unregulated AI can create social externalities like misinformation.” (meaning: costs to society)
Synonym: side effects, spillovers
Common mistakes: Using as singular (“an externalities”).
Part of speech: noun (plural)
Pattern: negative/positive externalities; externalities of + process
Definition: Side‑effects of an activity that affect others not directly involved.
Example: “Unregulated AI can create social externalities like misinformation.” (meaning: costs to society)
Synonym: side effects, spillovers
Common mistakes: Using as singular (“an externalities”).
autonomy ▼
Phonetics: /ɔːˈtɒn.ə.mi/ (BrE), /ɔːˈtɑː.nə.mi/ (AmE)
Part of speech: noun (adjective: autonomous)
Pattern: personal/decision autonomy; autonomous systems/vehicles
Definition: Ability to act or decide independently.
Example: “Human autonomy must be preserved in AI‑supported choices.” (meaning: people stay in control)
Synonym: independence, self‑direction
Common mistakes: Writing “autonomy of to do” (use “autonomy to do”).
Part of speech: noun (adjective: autonomous)
Pattern: personal/decision autonomy; autonomous systems/vehicles
Definition: Ability to act or decide independently.
Example: “Human autonomy must be preserved in AI‑supported choices.” (meaning: people stay in control)
Synonym: independence, self‑direction
Common mistakes: Writing “autonomy of to do” (use “autonomy to do”).
generalisation / generalization ▼
Phonetics: /ˌdʒen.ə.rəl.aɪˈzeɪ.ʃən/ (BrE), /ˌdʒen.ə.rəl.əˈzeɪ.ʃən/ (AmE)
Part of speech: noun
Pattern: model/data generalisation; poor/strong generalisation
Definition: How well a model performs on new, unseen data beyond the training set.
Example: “Overfitting harms generalisation and real‑world reliability.” (meaning: weak performance on new cases)
Synonym: transferability (context‑dependent)
Common mistakes: Spelling confusion (BrE “‑isation” vs AmE “‑ization”); using as a verb (“to generalisation”).
Part of speech: noun
Pattern: model/data generalisation; poor/strong generalisation
Definition: How well a model performs on new, unseen data beyond the training set.
Example: “Overfitting harms generalisation and real‑world reliability.” (meaning: weak performance on new cases)
Synonym: transferability (context‑dependent)
Common mistakes: Spelling confusion (BrE “‑isation” vs AmE “‑ization”); using as a verb (“to generalisation”).
20 Crucial Phrases & Expressions for This IELTS Task 2 (AI & Society)
Tap any phrase to reveal: BrE/AmE phonetics, part of speech, word patterns, a clear definition, a model sentence + quick meaning, a useful synonym, and common learner mistakes. Hover to enjoy the soft glow.
widespread use (of AI) ▼
Phonetics: /ˌwaɪd.spred ˈjuːs/ (BrE), /ˌwaɪd.spred ˈjuːs/ (AmE)
Part of speech: noun phrase
Pattern: widespread use of + technology/system
Definition: Very common adoption of a tool or system across society.
Example: “The widespread use of AI has reshaped hiring and customer service.” (meaning: AI is used almost everywhere)
Synonym: broad adoption
Common mistakes: Saying “wide use” in formal writing—prefer “widespread use.”
Part of speech: noun phrase
Pattern: widespread use of + technology/system
Definition: Very common adoption of a tool or system across society.
Example: “The widespread use of AI has reshaped hiring and customer service.” (meaning: AI is used almost everywhere)
Synonym: broad adoption
Common mistakes: Saying “wide use” in formal writing—prefer “widespread use.”
create more problems than it solves ▼
Phonetics: /kriːˈeɪt mɔː ˈprɒb.ləmz ðæn ɪt sɒlvz/ (BrE), /kriːˈeɪt mɔːr ˈprɑː.bləmz ðæn ɪt sɑːlvz/ (AmE)
Part of speech: clause/idiomatic frame
Pattern: X creates more problems than it solves
Definition: Overall negative impact outweighs benefits.
Example: “Critics argue that predictive policing may create more problems than it solves.” (meaning: harms surpass gains)
Synonym: produce net harm
Common mistakes: Writing “more problem” (plural needed).
Part of speech: clause/idiomatic frame
Pattern: X creates more problems than it solves
Definition: Overall negative impact outweighs benefits.
Example: “Critics argue that predictive policing may create more problems than it solves.” (meaning: harms surpass gains)
Synonym: produce net harm
Common mistakes: Writing “more problem” (plural needed).
job displacement ▼
Phonetics: /dʒɒb dɪsˈpleɪs.mənt/ (BrE), /dʒɑːb dɪsˈpleɪs.mənt/ (AmE)
Part of speech: noun phrase
Pattern: job displacement in/among + sector/group
Definition: Workers losing roles as tasks become automated.
Example: “Routine clerical tasks face higher risks of job displacement.” (meaning: jobs are pushed out)
Synonym: workforce redundancy
Common mistakes: Using “job replacement” (different meaning).
Part of speech: noun phrase
Pattern: job displacement in/among + sector/group
Definition: Workers losing roles as tasks become automated.
Example: “Routine clerical tasks face higher risks of job displacement.” (meaning: jobs are pushed out)
Synonym: workforce redundancy
Common mistakes: Using “job replacement” (different meaning).
algorithmic bias ▼
Phonetics: /ˌæl.ɡəˈrɪð.mɪk ˈbaɪ.əs/ (BrE & AmE)
Part of speech: noun phrase
Pattern: detect/mitigate algorithmic bias in + domain
Definition: Systematic unfairness in automated decisions due to data/models.
Example: “Audits are needed to curb algorithmic bias in recruitment.” (meaning: unfair patterns)
Synonym: systemic skew
Common mistakes: Spelling “algorismic” (incorrect).
Part of speech: noun phrase
Pattern: detect/mitigate algorithmic bias in + domain
Definition: Systematic unfairness in automated decisions due to data/models.
Example: “Audits are needed to curb algorithmic bias in recruitment.” (meaning: unfair patterns)
Synonym: systemic skew
Common mistakes: Spelling “algorismic” (incorrect).
privacy risks ▼
Phonetics: /ˈprɪv.ə.si rɪsks/ (BrE), /ˈpraɪ.və.si rɪsks/ (AmE)
Part of speech: noun phrase (plural)
Pattern: pose/mitigate privacy risks; privacy risks from + data use
Definition: Threats to personal data and the right to control it.
Example: “Facial recognition can raise serious privacy risks.” (meaning: dangers to personal data)
Synonym: data‑protection concerns
Common mistakes: Using “privacies” (privacy is uncountable).
Part of speech: noun phrase (plural)
Pattern: pose/mitigate privacy risks; privacy risks from + data use
Definition: Threats to personal data and the right to control it.
Example: “Facial recognition can raise serious privacy risks.” (meaning: dangers to personal data)
Synonym: data‑protection concerns
Common mistakes: Using “privacies” (privacy is uncountable).
deliver major benefits ▼
Phonetics: /dɪˈlɪv.ər ˈmeɪ.dʒə ˈben.ɪ.fɪts/ (BrE), /dɪˈlɪv.ɚ ˈmeɪ.dʒɚ ˈben.ə.fɪts/ (AmE)
Part of speech: verb phrase
Pattern: deliver major benefits to + group/sector
Definition: Provide significant advantages or improvements.
Example: “AI can deliver major benefits to rural healthcare.” (meaning: give big advantages)
Synonym: yield substantial gains
Common mistakes: “Bring major benefits for” → prefer “to” after “benefits.”
Part of speech: verb phrase
Pattern: deliver major benefits to + group/sector
Definition: Provide significant advantages or improvements.
Example: “AI can deliver major benefits to rural healthcare.” (meaning: give big advantages)
Synonym: yield substantial gains
Common mistakes: “Bring major benefits for” → prefer “to” after “benefits.”
for individuals and communities ▼
Phonetics: /fɔː ˌɪn.dɪˈvɪdʒ.u.əlz ənd kəˈmjuː.nɪ.tiz/ (BrE), /fɔːr ˌɪn.dəˈvɪdʒ.u.əlz ənd kəˈmjuː.nə.tiz/ (AmE)
Part of speech: prepositional phrase
Pattern: benefits/drawbacks for individuals and communities
Definition: Affecting both people personally and society locally.
Example: “AI’s effects matter for individuals and communities alike.” (meaning: at personal and local levels)
Synonym: at personal and community levels
Common mistakes: Writing “to individuals and communities” after “effects” (use “for”).
Part of speech: prepositional phrase
Pattern: benefits/drawbacks for individuals and communities
Definition: Affecting both people personally and society locally.
Example: “AI’s effects matter for individuals and communities alike.” (meaning: at personal and local levels)
Synonym: at personal and community levels
Common mistakes: Writing “to individuals and communities” after “effects” (use “for”).
discuss both views and give your own opinion ▼
Phonetics: /dɪˈskʌs bəʊθ vjuːz ənd ɡɪv jɔːr əʊn əˈpɪn.jən/ (BrE), /dɪˈskʌs boʊθ vjuːz ənd ɡɪv jɔːr oʊn əˈpɪn.jən/ (AmE)
Part of speech: instruction phrase
Pattern: discuss both views + state/opine that…
Definition: Cover each side fairly and make your stance explicit.
Example: “The prompt asks you to discuss both views and give your opinion clearly.” (meaning: balance + stance)
Synonym: examine both perspectives and state a position
Common mistakes: Forgetting to include a direct opinion → task penalty.
Part of speech: instruction phrase
Pattern: discuss both views + state/opine that…
Definition: Cover each side fairly and make your stance explicit.
Example: “The prompt asks you to discuss both views and give your opinion clearly.” (meaning: balance + stance)
Synonym: examine both perspectives and state a position
Common mistakes: Forgetting to include a direct opinion → task penalty.
net effect (is positive/negative) ▼
Phonetics: /net ɪˈfekt/ (BrE & AmE)
Part of speech: noun phrase
Pattern: the net effect is + adj / that‑clause
Definition: The overall outcome after weighing pros and cons.
Example: “With safeguards, the net effect of AI is likely positive.” (meaning: overall result)
Synonym: overall impact
Common mistakes: Using “net affect” (affect ≠ effect).
Part of speech: noun phrase
Pattern: the net effect is + adj / that‑clause
Definition: The overall outcome after weighing pros and cons.
Example: “With safeguards, the net effect of AI is likely positive.” (meaning: overall result)
Synonym: overall impact
Common mistakes: Using “net affect” (affect ≠ effect).
under firm oversight ▼
Phonetics: /ˌʌn.də ˌfɜːm ˈəʊ.və.saɪt/ (BrE), /ˌʌn.dɚ ˌfɝːm ˈoʊ.vɚ.saɪt/ (AmE)
Part of speech: prepositional phrase
Pattern: under firm/independent/public oversight
Definition: Subject to strong supervision and control.
Example: “High‑risk systems should operate under firm oversight.” (meaning: closely supervised)
Synonym: with rigorous supervision
Common mistakes: Writing “oversighting” (not a noun/verb form here).
Part of speech: prepositional phrase
Pattern: under firm/independent/public oversight
Definition: Subject to strong supervision and control.
Example: “High‑risk systems should operate under firm oversight.” (meaning: closely supervised)
Synonym: with rigorous supervision
Common mistakes: Writing “oversighting” (not a noun/verb form here).
human‑in‑the‑loop (HITL) ▼
Phonetics: /ˌhjuː.mən ɪn ðə luːp/ (BrE & AmE)
Part of speech: adjective/noun phrase
Pattern: human‑in‑the‑loop + system/decision; keep a human in the loop
Definition: A design where people review, guide, or override AI outputs.
Example: “Hospitals prefer human‑in‑the‑loop diagnostics for safety.” (meaning: humans supervise)
Synonym: human oversight
Common mistakes: Hyphenation inconsistencies—keep all hyphens.
Part of speech: adjective/noun phrase
Pattern: human‑in‑the‑loop + system/decision; keep a human in the loop
Definition: A design where people review, guide, or override AI outputs.
Example: “Hospitals prefer human‑in‑the‑loop diagnostics for safety.” (meaning: humans supervise)
Synonym: human oversight
Common mistakes: Hyphenation inconsistencies—keep all hyphens.
data‑protection rules / regulations ▼
Phonetics: /ˌdeɪ.tə prəˈtek.ʃən ruːlz/ (BrE), /ˈdeɪ.t̬ə prəˈtek.ʃən ruːlz/ (AmE)
Part of speech: noun phrase (plural)
Pattern: comply with/enforce data‑protection rules
Definition: Laws or policies that safeguard personal information.
Example: “Firms must comply with data‑protection rules to earn trust.” (meaning: follow privacy laws)
Synonym: privacy regulations
Common mistakes: Writing “data protections” (typically uncountable).
Part of speech: noun phrase (plural)
Pattern: comply with/enforce data‑protection rules
Definition: Laws or policies that safeguard personal information.
Example: “Firms must comply with data‑protection rules to earn trust.” (meaning: follow privacy laws)
Synonym: privacy regulations
Common mistakes: Writing “data protections” (typically uncountable).
large‑scale reskilling ▼
Phonetics: /ˌlɑːdʒ ˈskeɪl ˌriːˈskɪl.ɪŋ/ (BrE), /ˌlɑːrdʒ ˈskeɪl ˌriːˈskɪl.ɪŋ/ (AmE)
Part of speech: noun phrase
Pattern: fund/roll out large‑scale reskilling (programmes)
Definition: Training many people to move into new kinds of work.
Example: “Governments should invest in large‑scale reskilling.” (meaning: mass retraining)
Synonym: nationwide retraining
Common mistakes: Confusing with “upscaling” (use “upskilling”).
Part of speech: noun phrase
Pattern: fund/roll out large‑scale reskilling (programmes)
Definition: Training many people to move into new kinds of work.
Example: “Governments should invest in large‑scale reskilling.” (meaning: mass retraining)
Synonym: nationwide retraining
Common mistakes: Confusing with “upscaling” (use “upskilling”).
transparent governance ▼
Phonetics: /trænˈspær.ənt ˈɡʌv.ə.nəns/ (BrE), /trænˈsper.ənt ˈɡʌv.ɚ.nəns/ (AmE)
Part of speech: noun phrase
Pattern: ensure/strengthen transparent governance
Definition: Open, accountable rules and processes for AI use.
Example: “Public trust relies on transparent governance.” (meaning: open oversight)
Synonym: open oversight
Common mistakes: “Transparency governance” (wrong order).
Part of speech: noun phrase
Pattern: ensure/strengthen transparent governance
Definition: Open, accountable rules and processes for AI use.
Example: “Public trust relies on transparent governance.” (meaning: open oversight)
Synonym: open oversight
Common mistakes: “Transparency governance” (wrong order).
augment human judgement ▼
Phonetics: /ɔːɡˈment ˈhjuː.mən ˈdʒʌdʒ.mənt/ (BrE), /ɔːɡˈment ˈhjuː.mən ˈdʒʌdʒ.mənt/ (AmE)
Part of speech: verb phrase
Pattern: augment + human judgement/decision‑making
Definition: Help people make better decisions without replacing them.
Example: “Decision tools should augment human judgement, not replace it.” (meaning: support humans)
Synonym: enhance human decision‑making
Common mistakes: “Judgement” (BrE) vs “judgment” (AmE)—be consistent.
Part of speech: verb phrase
Pattern: augment + human judgement/decision‑making
Definition: Help people make better decisions without replacing them.
Example: “Decision tools should augment human judgement, not replace it.” (meaning: support humans)
Synonym: enhance human decision‑making
Common mistakes: “Judgement” (BrE) vs “judgment” (AmE)—be consistent.
high‑stakes decisions ▼
Phonetics: /haɪ ˈsteɪks dɪˈsɪʒ.ənz/ (BrE & AmE)
Part of speech: noun phrase (plural)
Pattern: in/for high‑stakes decisions; deploy in + context
Definition: Choices where errors have serious consequences.
Example: “Explainability is essential for high‑stakes decisions.” (meaning: critical choices)
Synonym: critical decisions
Common mistakes: Hyphen omitted (“high stakes decisions”)—hyphenate for clarity.
Part of speech: noun phrase (plural)
Pattern: in/for high‑stakes decisions; deploy in + context
Definition: Choices where errors have serious consequences.
Example: “Explainability is essential for high‑stakes decisions.” (meaning: critical choices)
Synonym: critical decisions
Common mistakes: Hyphen omitted (“high stakes decisions”)—hyphenate for clarity.
operate at scale ▼
Phonetics: /ˈɒp.ə.reɪt ət skeɪl/ (BrE), /ˈɑː.pə.reɪt ət skeɪl/ (AmE)
Part of speech: verb phrase
Pattern: operate/run at scale; deploy at scale
Definition: Function across many users/contexts reliably.
Example: “Only tested systems should operate at scale in public services.” (meaning: serve lots of users)
Synonym: run at large scale
Common mistakes: Writing “on scale” (use “at”).
Part of speech: verb phrase
Pattern: operate/run at scale; deploy at scale
Definition: Function across many users/contexts reliably.
Example: “Only tested systems should operate at scale in public services.” (meaning: serve lots of users)
Synonym: run at large scale
Common mistakes: Writing “on scale” (use “at”).
entrench inequality ▼
Phonetics: /ɪnˈtrentʃ ˌɪn.ɪˈkwɒl.ɪ.ti/ (BrE), /ɪnˈtrentʃ ˌɪn.əˈkwɑː.lə.t̬i/ (AmE)
Part of speech: verb phrase
Pattern: entrench + inequality/disadvantage
Definition: Make unfair differences harder to change.
Example: “Biased datasets risk entrenching inequality in lending.” (meaning: locking in unfairness)
Synonym: cement disparities
Common mistakes: Using “intrench” (archaic spelling).
Part of speech: verb phrase
Pattern: entrench + inequality/disadvantage
Definition: Make unfair differences harder to change.
Example: “Biased datasets risk entrenching inequality in lending.” (meaning: locking in unfairness)
Synonym: cement disparities
Common mistakes: Using “intrench” (archaic spelling).
erode privacy/trust ▼
Phonetics: /ɪˈrəʊd ˈpraɪ.və.si/ (BrE), /ɪˈroʊd ˈpraɪ.və.si/ (AmE)
Part of speech: verb phrase
Pattern: erode + privacy/trust/confidence
Definition: Gradually reduce or damage something important.
Example: “Opaque tracking can erode privacy and civic trust.” (meaning: slowly destroy)
Synonym: undermine
Common mistakes: “Erode the privacy” (article unnecessary in general statements).
Part of speech: verb phrase
Pattern: erode + privacy/trust/confidence
Definition: Gradually reduce or damage something important.
Example: “Opaque tracking can erode privacy and civic trust.” (meaning: slowly destroy)
Synonym: undermine
Common mistakes: “Erode the privacy” (article unnecessary in general statements).
provided (that) / provided… ▼
Phonetics: /prəˈvaɪ.dɪd (ðæt)/ (BrE), /prəˈvaɪ.dɪd (ðæt)/ (AmE)
Part of speech: conjunction (condition)
Pattern: provided (that) + clause
Definition: On the condition that; if and only if.
Example: “AI yields net benefits provided strict audits are enforced.” (meaning: only if this condition holds)
Synonym: as long as
Common mistakes: Using it as a verb (“it was provided that…”)—here it’s a conjunction.
Part of speech: conjunction (condition)
Pattern: provided (that) + clause
Definition: On the condition that; if and only if.
Example: “AI yields net benefits provided strict audits are enforced.” (meaning: only if this condition holds)
Synonym: as long as
Common mistakes: Using it as a verb (“it was provided that…”)—here it’s a conjunction.
Interactive Exercise 1 — IELTS Task 2 Vocabulary & Phrases (AI & Society)
Choose the best answer. As soon as you select an option, you’ll see a detailed explanation (10–12 sentences) describing the reasoning, common traps, and how to reuse the language in your own essay. Hover over questions to enjoy the soft glow.
Q1. Which sentence best uses the term algorithmic bias correctly in an IELTS-style argument?
Q2. In the context of privacy, which option best captures data minimisation?
Q3. Which sentence correctly illustrates a human‑in‑the‑loop approach?
Q4. In machine‑learning contexts, strong generalisation means a model…
Q5. Which scenario best exemplifies surveillance concerns?
Q6. Which option best shows mitigation of AI risks in an IELTS essay?
Q7. The phrase augment human judgement is best paraphrased as…
Q8. In public policy writing, accountability in AI most directly means…
Q9. Which sentence best demonstrates transparent governance?
Q10. In IELTS formal style, high‑stakes decisions most accurately refers to…