{"id":5387,"date":"2026-01-21T22:55:41","date_gmt":"2026-01-21T22:55:41","guid":{"rendered":"https:\/\/www.web-ai-institute.com\/wp\/2026\/01\/21\/govt-agencies-fall-short-on-automation-transparency-information-age\/"},"modified":"2026-01-21T22:55:41","modified_gmt":"2026-01-21T22:55:41","slug":"govt-agencies-fall-short-on-automation-transparency-information-age","status":"publish","type":"post","link":"https:\/\/www.web-ai-institute.com\/wp\/2026\/01\/21\/govt-agencies-fall-short-on-automation-transparency-information-age\/","title":{"rendered":"Govt agencies fall short on automation transparency | Information Age"},"content":{"rendered":"<p><br \/>\n<\/p>\n<div>\n\t    <img decoding=\"async\" id=\"ctl00_ContentPlaceHolder1_ucArticle_imgImage\" src=\"https:\/\/ia.acs.org.au\/content\/dam\/ia\/article\/images\/2026\/woman%20automation%20technology%20shutterstock.jpg\" alt=\"A close up of a woman sitting and using her index finger to point at data on a floating futuristic display in front of her.\" style=\"border-width:0px;width:820px;\"\/><\/p>\n<p>Australia&#8217;s information commissioner says some federal agencies are &#8216;not clear&#8217; about how they use automated decision-making tech. Image: Shutterstock<\/p>\n<\/p><\/div>\n<div>\n<p>Australian government agencies are falling short of transparency expectations when it comes to explaining how they use automated decision-making technologies, according to a new report from the nation\u2019s information watchdog.<\/p>\n<p>Released on Wednesday by the Office of the Australian Information Commissioner (OAIC), the <a href=\"https:\/\/www.oaic.gov.au\/freedom-of-information\/information-commissioner-decisions-and-reports\/foi-reports\/Automated-decision-making-and-public-reporting-under-the-Freedom-of-Information-Act#section-what-we-recommend\">report<\/a> followed an October review that assessed how transparent federal agencies were about their use of automated decision-making (ADM) technology.<i\/><\/p>\n<p>With ADM referring to the use of a computer program to automate a decision-making process, the report showed several government agencies were unclear on their use of ADM \u2013 including cases<i> <\/i>in connection to AI.<\/p>\n<p>The report checked whether 23 de-identified<b> <\/b>Australian government agencies complied with their publication requirements under the Information Publication Scheme (IPS), which encourages agencies to release information about their conduct to the public proactively.<\/p>\n<p>Though each agency \u201cis authorised to use ADM under various legislative statutes\u201d, just 13 agencies mentioned ADM in their IPS information.<\/p>\n<p>Four of those agencies \u2013 the Australian Taxation Office, Services Australia, the Department of Health, Disability and Aging, and the Department of Veterans&#8217; Affairs \u2013 explicitly disclosed ADM use in decisions that affect the public.<\/p>\n<p>The other nine instead referenced or inferred the use of ADM, often in connection to AI.<\/p>\n<p>\u201cHowever, they did not specifically say whether they used ADM in any of their decision-making or recommendation processes,\u201d the OAIC wrote.<\/p>\n<p>The OAIC found these nine agencies often \u201cimplied\u201d ADM may be in use by, for example, mentioning it in a corporate plan or having an inferred reference in their AI strategy, but the watchdog could not \u201cascertain if this was indeed the case\u201d.<\/p>\n<p>The four agencies that did disclose the use of ADM were meanwhile \u201cnot clear about how they used it\u201d, while some 74 per cent of agencies were not able to be identified as using ADM.<\/p>\n<p>The OAIC report arrived after the government\u2019s <a href=\"https:\/\/ia.acs.org.au\/article\/2025\/national-ai-plan-takes-light-touch-regulatory-approach.html\">National AI Plan<\/a> promised legal \u201cconsistency\u201d for ADM as the use of AI <a href=\"https:\/\/ia.acs.org.au\/article\/2025\/australia-sets-ai-standards-for-public-sector.html\">expands<\/a>, alongside <a href=\"https:\/\/ia.acs.org.au\/article\/2025\/chief-ai-officers-coming-to-australian-govt-agencies.html\">chief AI officers<\/a> who will drive adoption in each agency.<\/p>\n<h4><b>Are agencies hiding their hand?<\/b><\/h4>\n<p>Further to IPS statements, the report also examined agencies\u2019 websites and AI transparency statements.<\/p>\n<p>\u201cOur threshold was whether a member of the public, who wanted to know if an agency was using ADM, could reasonably do so by performing relatively simple searches on the agency\u2019s website,\u201d read the report.<b\/><\/p>\n<p>Using this methodology, the commissioner found it was \u201clikely\u201d ADM was in use at two agencies despite it not being appropriately disclosed.<\/p>\n<p>In an anonymous case study, one agency mentioned in a data strategy report that it was \u201cembracing automation and artificial intelligence\u201d which allowed it to make decisions based on data in a timelier manner \u2013 though the agency did not explicitly state on its website if and how it used ADM.<\/p>\n<p>\u201cIt does not elaborate on how these decisions are made, and whether any decisions made by the agency are based solely on automated processes,\u201d the OAIC wrote.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/ia.acs.org.au\/content\/dam\/ia\/article\/images\/2026\/australian%20parliament%20canberra%20shutterstock.jpg\" alt=\"\"\/><br \/>&#13;<br \/>\n<sub><i>Nine government agencies which &#8216;implied&#8217; they used ADM had not publicly confirmed so, the OAIC said. Image: Shutterstock<\/i><\/sub><\/p>\n<h4><b>We don\u2019t need another Robodebt<\/b><\/h4>\n<p>The report comes after years of fallout from the federal government\u2019s Robodebt scandal, where an automated debt recovery program wrongfully accused welfare recipients of owing the government money.<\/p>\n<p>With the government having since committed to $587 million in <a href=\"https:\/\/ia.acs.org.au\/article\/2025\/govt-settles-second-robodebt-class-action-for--475m.html\">compensation to victims<\/a>, the OAIC specifically noted \u201cpublic examples of failures of oversight of ADM\u201d such as those outlined by the Robodebt Royal Commission had \u201chighlighted the need for transparency about the use of ADM by government\u201d.<\/p>\n<p>\u201cThe benefits of utilising ADM technology in government will only be realised if risks are appropriately mitigated and trust is built with the Australian community,\u201d the OAIC wrote.<\/p>\n<p>Indeed, the commissioner said Robodebt \u201crelied heavily\u201d on ADM for its \u2018income averaging\u2019, while <i>Information Age <\/i>last year found staff at Centrelink and Medicare agency Services Australia had <a href=\"https:\/\/ia.acs.org.au\/article\/2025\/centrelink-defends-ai-pilots.html\">tested AI\u2019s ability to predict fraudulent welfare claims<\/a>.<\/p>\n<p>The agency has since outlined a <a href=\"https:\/\/ia.acs.org.au\/article\/2025\/services-australia-releases-ai-and-automation-strategy.html\">three-year plan<\/a> which aims to ensure its use of AI and automation is \u201chuman-centric, safe, responsible, transparent, fair, ethical, and legal\u201d.<\/p>\n<h4><b>Commissioner calls for transparency<\/b><\/h4>\n<p>The OAIC ultimately recommended all agencies authorised to use ADM publish as such in their IPS, and clarify whether they \u201cutilise ADM to provide information and services to the public\u201d.<\/p>\n<p>Other recommendations included clear statements of the types of ADM agencies used, including technologies from \u201csimple calculators to machine learning\u201d.<\/p>\n<p>The report further called for the publication of lists of decisions ADM is used for, alongside easy-to-understand examples.<\/p>\n<p>As a result of the report, the OAIC will update Freedom of Information (FOI) guidelines so ADM is expressly included as an example of \u2018operational information\u2019 \u2013 which agencies are specifically required to publish.<\/p>\n<p>\u201cInformation about decision-making and the exercise of agencies functions is important information for the Australian community,\u201d said information commissioner, Elizabeth Tydd.<\/p>\n<p>\u201cIt improves integrity, accountability, and trust.\u201d<\/p>\n<\/p><\/div>\n<p><script>    (function(d, s, id) {\n\t\t\tvar js, fjs = d.getElementsByTagName(s)[0];\n\t\t\tif (d.getElementById(id)) return;\n\t\t\tjs = d.createElement(s); js.id = id;\n\t\t\tjs.src = \"\/\/connect.facebook.net\/en_GB\/sdk.js#xfbml=1&version=v2.0\";\n\t\t\tfjs.parentNode.insertBefore(js, fjs);\n\t\t\t} (document, 'script', 'facebook-jssdk'));\n\t\t\t<\/script><br \/>\n<br \/><br \/>\n<br \/><a href=\"https:\/\/ia.acs.org.au\/article\/2026\/govt-agencies-fall-short-on-automation-transparency.html\">Source link <\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Australia&#8217;s information commissioner says some federal agencies are &#8216;not clear&#8217; about how they use automated decision-making tech. Image: Shutterstock Australian government agencies are falling short of transparency expectations when it comes to explaining how they use automated decision-making technologies, according to a new report from the nation\u2019s information watchdog. Released on Wednesday by the Office&#8230;<\/p>\n","protected":false},"author":1,"featured_media":5388,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"pagelayer_contact_templates":[],"_pagelayer_content":"","footnotes":""},"categories":[33],"tags":[],"class_list":["post-5387","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-n8n"],"_links":{"self":[{"href":"https:\/\/www.web-ai-institute.com\/wp\/wp-json\/wp\/v2\/posts\/5387","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.web-ai-institute.com\/wp\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.web-ai-institute.com\/wp\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.web-ai-institute.com\/wp\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.web-ai-institute.com\/wp\/wp-json\/wp\/v2\/comments?post=5387"}],"version-history":[{"count":0,"href":"https:\/\/www.web-ai-institute.com\/wp\/wp-json\/wp\/v2\/posts\/5387\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.web-ai-institute.com\/wp\/wp-json\/wp\/v2\/media\/5388"}],"wp:attachment":[{"href":"https:\/\/www.web-ai-institute.com\/wp\/wp-json\/wp\/v2\/media?parent=5387"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.web-ai-institute.com\/wp\/wp-json\/wp\/v2\/categories?post=5387"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.web-ai-institute.com\/wp\/wp-json\/wp\/v2\/tags?post=5387"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}