{ "id": "AIBD-0337", "title": "ATO's nearest-neighbor myTax prompts reached nearly 340,000 taxpayers at tax time 2020; resulting adjustments had an estimated $37 million revenue impact", "summary": "ATO page: at tax time 2020 nearly 340,000 myTax users (7.5%) got real-time prompts from nearest-neighbor checks; adjustments had an estimated revenue impact of about $37 million.", "date_observed": "2021-07-14", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The Australian Taxation Office (ATO) page “How we use data and analytics” states that the ATO uses machine learning, a common form of artificial intelligence. Nearest neighbor methods compare amounts being entered into myTax with those of other people in similar circumstances. If a claim is significantly different from what is expected, a message prompts the taxpayer to check the figures. During tax time in 2020 nearly 340,000 taxpayers, around 7.5% of myTax users, received a pop-up message, the page reports. It suggested they review a specific label. These prompts caused taxpayers to make adjustments estimated to have a revenue impact of around $37 million. The same page says the ATO also uses machine-learning algorithms to forecast call-center volumes and speech recognition to transcribe inbound calls. Separately, in 2020 it automated 93,000 franking-credit refunds worth over $42 million for self-preparers over 60 (not described as machine learning). The only cited source independent of the ATO is The Mandarin's report of 25 February 2025. It says the Australian National Audit Office (ANAO) found the ATO's AI governance arrangements only partly effective, and gives no outcome figures. No non-vendor primary source on record gives a recovered figure attributable to machine learning. The $37 million is the ATO's own estimate. The page is undated; the outcome concerns tax time 2020. An Internet Archive copy dated 14 July 2021 contains the 340,000 and $37 million figures.", "category": "benefit created", "unexpected": false, "purpose": "Built to prompt myTax users in real time to check claims that differ from those of similar taxpayers before lodgment. The recorded adjustments are that purpose.", "system": "ATO myTax real-time analytics prompts using nearest-neighbor comparison models", "model_version": null, "developer": "Australian Taxation Office (in-house data and analytics)", "deployer": "Australian Taxation Office", "location": "Australia (national, online myTax lodgment)", "domain": "Public administration: tax compliance", "segment": "National revenue agency", "benefit_type": "Reduced Risk", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "Taxpayers who corrected claims are not individually documented; the page gives no individual outcome beyond the aggregate prompt count." }, { "group": "organizational", "depth": 1, "reason": "The ATO's own estimate of about $37 million revenue impact from one tax time, with no independent verification and no comparison with total collections given." }, { "group": "societal", "depth": 1, "reason": "Protects public revenue at modest scale: prompts reached 7.5% of myTax users and the stated impact is an estimate." } ], "ai_role": { "status": "assessed", "statement": "The ATO page states that nearest-neighbor methods generate the prompts and that the ATO uses machine learning. It attributes the adjustments to the prompts, though taxpayers themselves make the changes. Only the deployer's account is among the sources for the outcome. The ATO built and runs the system in-house, so its page is both the developer's and the deployer's account. The ANAO audit reported by The Mandarin found AI governance “partly effective” and offers no outcome figures in the cited excerpt." }, "evidence": [ { "kind": "report", "citation": "Australian Taxation Office (n.d.). How we use data and analytics. ato.gov.au (undated page).", "title": "How we use data and analytics", "url": "https://www.ato.gov.au/about-ato/commitments-and-reporting/information-and-privacy/how-we-use-data-and-analytics", "date": null, "written_by": "makers", "quote": "During tax time in 2020, nearly 340,000 taxpayers (around 7.5% of myTax users), received a pop-up message through myTax suggesting they review a specific label." }, { "kind": "report", "citation": "Australian Taxation Office (2021). How we use data and analytics. ato.gov.au, Internet Archive capture of 14 Jul 2021 (earliest capture found; same figures as the live page).", "title": "How we use data and analytics (Internet Archive capture, 14 Jul 2021)", "url": "https://web.archive.org/web/20210714025448/https://www.ato.gov.au/About-ATO/Commitments-and-reporting/Information-and-privacy/How-we-use-data-and-analytics/", "date": "2021-07-14", "written_by": "makers", "quote": "These prompts caused taxpayers to make adjustments estimated to have a revenue impact of around $37 million." }, { "kind": "report", "citation": "Australian Taxation Office (n.d.). How we use data and analytics, section on real-time analytics prompts.", "title": "How we use data and analytics (revenue impact of prompts)", "url": "https://www.ato.gov.au/about-ato/commitments-and-reporting/information-and-privacy/how-we-use-data-and-analytics", "date": null, "written_by": "makers", "quote": "These prompts caused taxpayers to make adjustments estimated to have a revenue impact of around $37 million." }, { "kind": "report", "citation": "Australian Taxation Office (n.d.). How we use data and analytics, description of the nearest neighbour method.", "title": "How we use data and analytics (nearest neighbour)", "url": "https://www.ato.gov.au/about-ato/commitments-and-reporting/information-and-privacy/how-we-use-data-and-analytics", "date": null, "written_by": "makers", "quote": "Nearest neighbour methods are used to compare amounts being entered into myTax, with those of other people in similar circumstances, along with other checks." }, { "kind": "report", "citation": "Australian Taxation Office (n.d.). How we use data and analytics, section on machine learning.", "title": "How we use data and analytics (machine learning)", "url": "https://www.ato.gov.au/about-ato/commitments-and-reporting/information-and-privacy/how-we-use-data-and-analytics", "date": null, "written_by": "makers", "quote": "We use a common form of artificial intelligence known as machine learning." }, { "kind": "news report", "citation": "Bajkowski, J. (2025). Auditor whacks ATO over artificial intelligence. The Mandarin, 25 Feb 2025 (ANAO finding).", "title": "Auditor whacks ATO over artificial intelligence", "url": "https://www.themandarin.com.au/287193-auditor-whacks-ato-over-artificial-intelligence/", "date": "2025-02-25", "written_by": "independent", "quote": "partly effective" } ], "replication": { "produced_by": [ "Large volumes of pre-filled third-party data (over 85 million pieces in 2020) giving a comparison base", "Nearest-neighbor comparison of entered amounts against taxpayers in similar circumstances", "A real-time prompt inside myTax before lodgment", "Taxpayers' own decision to adjust a flagged label" ], "must_hold": [ "Comparison groups and thresholds that keep prompts credible to taxpayers", "Continued use of myTax self-preparation at scale", "AI governance that the ANAO found only partly effective in 2025 being maintained or improved", "The ATO's method of estimating revenue impact remaining the only measure, since no independent evaluation is among the sources" ], "echoes": [] }, "paired_incident": null, "headline": "myTax prompts had an estimated $37 million revenue impact", "metric": "adjustments estimated to have a revenue impact of around $37 million", "comparator": null, "later": { "status": "unknown", "date": null, "note": "The Mandarin reported on 25 February 2025 that the ANAO had judged the ATO's AI governance only partly effective. That report gives no later outcome figure for the myTax prompts.", "quote": null, "url": null }, "value": { "amount": "$37 million", "what": "revenue impact of the adjustments taxpayers made after myTax prompts at tax time 2020 (ATO's own estimate)", "who": "the Australian Taxation Office (Australian Government revenue)", "estimate": true, "quote": "During tax time in 2020, nearly 340,000 taxpayers (around 7.5% of myTax users), received a pop-up message through myTax suggesting they review a specific label. These prompts caused taxpayers to make adjustments estimated to have a revenue impact of around $37 million.", "url": "https://web.archive.org/web/20210714025448/https://www.ato.gov.au/About-ATO/Commitments-and-reporting/Information-and-privacy/How-we-use-data-and-analytics/" }, "developer_country": "Australia", "deployer_country": "Australia", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added the value as stated from the cited sources.", "source": "https://web.archive.org/web/20210714025448/https://www.ato.gov.au/About-ATO/Commitments-and-reporting/Information-and-privacy/How-we-use-data-and-analytics/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Australian_Taxation_Office; https://en.wikipedia.org/wiki/Australian_Taxation_Office", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report and two reports from the makers. 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The Mandarin, 25 Feb 2025 (ANAO finding).", "title": "Auditor whacks ATO over artificial intelligence", "url": "https://www.themandarin.com.au/287193-auditor-whacks-ato-over-artificial-intelligence/", "date": "2025-02-25", "written_by": "independent", "quote": "partly effective" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0337-source-0001", "AIBD-0337-source-0002", "AIBD-0337-source-0003", "AIBD-0337-source-0004", "AIBD-0337-source-0005", "AIBD-0337-source-0006" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "Australian Taxation Office (n.d.). 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How we use data and analytics. ato.gov.au, Internet Archive capture of 14 Jul 2021 (earliest capture found; same figures as the live page).", "title": "How we use data and analytics (Internet Archive capture, 14 Jul 2021)", "url": "https://web.archive.org/web/20210714025448/https://www.ato.gov.au/About-ATO/Commitments-and-reporting/Information-and-privacy/How-we-use-data-and-analytics/", "date": "2021-07-14", "written_by": "makers", "quote": "These prompts caused taxpayers to make adjustments estimated to have a revenue impact of around $37 million." }, { "kind": "report", "citation": "Australian Taxation Office (n.d.). How we use data and analytics, section on real-time analytics prompts.", "title": "How we use data and analytics (revenue impact of prompts)", "url": "https://www.ato.gov.au/about-ato/commitments-and-reporting/information-and-privacy/how-we-use-data-and-analytics", "written_by": "makers", "quote": "These prompts caused taxpayers to make adjustments estimated to have a revenue impact of around $37 million." }, { "kind": "report", "citation": "Australian Taxation Office (n.d.). How we use data and analytics, description of the nearest neighbour method.", "title": "How we use data and analytics (nearest neighbour)", "url": "https://www.ato.gov.au/about-ato/commitments-and-reporting/information-and-privacy/how-we-use-data-and-analytics", "written_by": "makers", "quote": "Nearest neighbour methods are used to compare amounts being entered into myTax, with those of other people in similar circumstances, along with other checks." }, { "kind": "report", "citation": "Australian Taxation Office (n.d.). How we use data and analytics, section on machine learning.", "title": "How we use data and analytics (machine learning)", "url": "https://www.ato.gov.au/about-ato/commitments-and-reporting/information-and-privacy/how-we-use-data-and-analytics", "written_by": "makers", "quote": "We use a common form of artificial intelligence known as machine learning." }, { "kind": "news report", "citation": "Bajkowski, J. (2025). Auditor whacks ATO over artificial intelligence. The Mandarin, 25 Feb 2025 (ANAO finding).", "title": "Auditor whacks ATO over artificial intelligence", "url": "https://www.themandarin.com.au/287193-auditor-whacks-ato-over-artificial-intelligence/", "date": "2025-02-25", "written_by": "independent", "quote": "partly effective" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "ATO page: at tax time 2020 nearly 340,000 myTax users (7.5%) got real-time prompts from nearest-neighbor checks; adjustments had an estimated revenue impact of about $37 million.", "account": "The Australian Taxation Office (ATO) page “How we use data and analytics” states that the ATO uses machine learning, a common form of artificial intelligence. Nearest neighbor methods compare amounts being entered into myTax with those of other people in similar circumstances. If a claim is significantly different from what is expected, a message prompts the taxpayer to check the figures. During tax time in 2020 nearly 340,000 taxpayers, around 7.5% of myTax users, received a pop-up message, the page reports. It suggested they review a specific label. These prompts caused taxpayers to make adjustments estimated to have a revenue impact of around $37 million. The same page says the ATO also uses machine-learning algorithms to forecast call-center volumes and speech recognition to transcribe inbound calls. Separately, in 2020 it automated 93,000 franking-credit refunds worth over $42 million for self-preparers over 60 (not described as machine learning). The only cited source independent of the ATO is The Mandarin's report of 25 February 2025. It says the Australian National Audit Office (ANAO) found the ATO's AI governance arrangements only partly effective, and gives no outcome figures. No non-vendor primary source on record gives a recovered figure attributable to machine learning. The $37 million is the ATO's own estimate. The page is undated; the outcome concerns tax time 2020. An Internet Archive copy dated 14 July 2021 contains the 340,000 and $37 million figures." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "ATO myTax real-time analytics prompts using nearest-neighbor comparison models" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Australian Taxation Office (in-house data and analytics)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "ATO's nearest-neighbor myTax prompts reached nearly 340,000 taxpayers at tax time 2020; resulting adjustments had an estimated $37 million revenue impact", "headline": "myTax prompts had an estimated $37 million revenue impact" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "d2f5abf5d3213502938cfd894230a985bb5b4c03032873aec1aeebf4b1f80f2e" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The Australian Taxation Office (ATO) page “How we use data and analytics” states that the ATO uses machine learning, a common form of artificial intelligence. Nearest neighbor methods compare amounts being entered into myTax with those of other people in similar circumstances. If a claim is significantly different from what is expected, a message prompts the taxpayer to check the figures. During tax time in 2020 nearly 340,000 taxpayers, around 7.5% of myTax users, received a pop-up message, the page reports. It suggested they review a specific label. These prompts caused taxpayers to make adjustments estimated to have a revenue impact of around $37 million. The same page says the ATO also uses machine-learning algorithms to forecast call-center volumes and speech recognition to transcribe inbound calls. Separately, in 2020 it automated 93,000 franking-credit refunds worth over $42 million for self-preparers over 60 (not described as machine learning). The only cited source independent of the ATO is The Mandarin's report of 25 February 2025. It says the Australian National Audit Office (ANAO) found the ATO's AI governance arrangements only partly effective, and gives no outcome figures. No non-vendor primary source on record gives a recovered figure attributable to machine learning. The $37 million is the ATO's own estimate. The page is undated; the outcome concerns tax time 2020. An Internet Archive copy dated 14 July 2021 contains the 340,000 and $37 million figures.", "replication": { "produced_by": [ "Large volumes of pre-filled third-party data (over 85 million pieces in 2020) giving a comparison base", "Nearest-neighbor comparison of entered amounts against taxpayers in similar circumstances", "A real-time prompt inside myTax before lodgment", "Taxpayers' own decision to adjust a flagged label" ], "must_hold": [ "Comparison groups and thresholds that keep prompts credible to taxpayers", "Continued use of myTax self-preparation at scale", "AI governance that the ANAO found only partly effective in 2025 being maintained or improved", "The ATO's method of estimating revenue impact remaining the only measure, since no independent evaluation is among the sources" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 36, "reviewed": 0, "record_mapped": 36, "not_assessed": 55, "complete": false, "by_state": { "value_present": 36, "not_reported": 0, "unresolved": 0, "not_assessed": 55, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } }