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Critical stakeholders like the Australian Tax Office, legal experts, and welfare agencies were not involved in the design and development of the system.

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The Robodebt system relied heavily on ADM to automate decision-making processes, reducing human oversight in the detection of welfare debts.

Earlier citizens used to receive phone calls for confirmation of debts.

According to Process Virtualization Theory, explains that not all processes can or should be fully virtualized, especially those requiring human judgment. he Robodebt system virtualized the entire debt recovery process without considering the need for human intervention in verifying debt calculations

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Human intervention at key stages could have prevented inaccurate debt notices from being sent to vulnerable citizens.

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No Pilot Testing: The system was rolled out without pilot testing to identify potential flaws, such as incorrect debt calculations and mismatches between annual and fortnightly income data. If a pilot test had been conducted, these significant issues would have likely been uncovered early, reducing harm to citizens.

Failure to Account for Real-World Variations: The system failed to consider real-world income variations, such as irregular job patterns or fluctuating employment. It relied on annual data from the Australian Tax Office (ATO) and compared it to fortnightly welfare payments. The discrepancy between these data sources created a situation where legitimate payments appeared as overpayments, resulting in erroneous debt notices.

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The nature of welfare decisions was complex and required human judgment and context, which the automated system failed to provide.

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Centrelink shifted the responsibility to citizens to prove that they did not owe a debt (retrieve old payslips, bank statements, sometimes from years prior...It is time consuming and stressful) rather than ensuring the system accurately verified debts before notification.

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The core issue was that the system completely removed the human element in verifying and validating debt claims, as the system bypassed essential human checks that would have verified whether the debts being flagged were legitimate.

Further, human involvement was deliberately minimized to create a self-service process where citizens were directed to online platforms to resolve their issues, further reducing direct interactions with welfare officers. This decision to rely heavily on automation without proper testing or piloting and with insufficient stakeholder input led to flawed debt calculations being accepted as final, which ultimately caused widespread harm and controversy.

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Their insights could have highlighted the complexities of welfare payments and helped create a more effective system, thereby reducing the risk of errors and enhancing public trust.

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No stakeholder feedback key parties, like legal experts and welfare groups, were not consulted during development.

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The system was fundamentally flawed because it automated debt recovery processes without ensuring the debts were legally enforceable or inaccurate. As it was wrongly assumed that any discrepancy between income records and welfare payments indicated overpayment.

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the decision to ask citizens to prove they did not owe a debt, often requiring them to retrieve old financial records, was particularly damaging, as it increased stress and breached privacy. It compromised the democratic right to keep to yourself. The harm is has done to trusting and people's low illiteracy to search for long ago debt, made it seem it was unlawful from bypassing the trust.

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The system automatically calculated debts using flawed income-averaging methods that failed to account for irregular earnings, resulting in numerous false debt notices. Without a human checking or validating, all these errors went unchallenged, placing the burden of proof on vulnerable welfare recipients. This governance failure violated principles of IT risk management, transparency, and ethical responsibility, causing significant harm to individuals and leading to legal repercussions for the government. Proper oversight and risk management could have prevented these issues, highlighting the dangers of unchecked automation in critical public systems.

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The system lacks proper testing. All the bugs that were identified could be eliminated easily if this was properly tested. For example, it considered consistent income without thinking of variables such as additional income. Moreover, if there was a pilot test, they could have identified all the bugs before introducing this to public.

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Flawed calculations: The Robodebt system was designed to automatically calculate debts by comparing welfare payments with income data from the Australian Tax Office (ATO). However, the algorithm used a flawed method of averaging yearly income to determine the final payments. Without human oversight, these errors went unchecked, leading to many incorrect debts being issued.

Inflexible and Rigid system: Automated systems like Robodebt are designed to follow rigid, predefined rules. When the system started to fail, there was no capacity for immediate adaptation or flexibility to address the emerging issues.

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The system was developed and implemented without proper consultation with essential stakeholders, such as the Australian Tax Office (ATO), legal experts, the Australian Digital Transformation Agency, and public sector unions.

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The automation process removed human oversight, which was essential for understanding complex individual financial circumstances. While automation could work well for certain processes, debt calculation requires careful, contextual analysis that the Robodebt system overlooked.

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Their insights might have shed light on the complexities of welfare payments, contributing to the development of a more efficient system, which could reduce errors and strengthen public trust.

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Automated systems without proper human oversight may lack adjustment. Debt notices were automatically generated and sent to citizens without any human verification. This meant that incorrect debt calculations went without being checked by humans for any adjustment.

It impacted placing the burden of proof on citizens.

Citizens become the ones to put in burden. Automated systems cause confusion and frustration among citizens due to incorrect debt notices. Also, citizens become the ones who need to prove to Centrelink that they have received the wrong debt notice.

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Automated decision-making without checks: The system's core relied on automated data-matching without adequate human review, resulting in errors and injustice.

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Failure to take into account human emotions, being overly assertive in dealing with debt, and not taking into account the current living conditions of the indebted person, which adds extra pressure on them and is prone to cause psychological problems.

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Previously, Centrelink employees would contact individuals to clarify discrepancies and confirm whether there was indeed a debt to be repaid. Robodebt removed this step, replacing it with automated notifications that lacked transparency.

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The automation process eliminated human oversight, which was crucial for understanding the intricate financial situations of individuals. Although automation can be effective for some tasks, the calculation of entitlements requires careful, contextual analysis, which the Robodebt system failed to account for.

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The lack of proper human oversight in debt verification

unverified AI decision-making without human intervention (eliminated the necessary checks, creating significant harm)

without adequate verification caused widespread errors in debt calculations

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The problem was, the Robodebt system was supposed to be an automated assessment of welfare debt, matching the income data from two sources: the Centrelink, with information on income on a two-weekly basis, and the Australian Tax Office with annual income. It didn't provide for fluctuations in employment and therefore income that occur throughout the year.

The system auto-generated notices of indebtedness, without appropriate human verification. In fact, many citizens had quite irregular income or temporary jobs, which were reported as owing money.

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The system was not properly piloted or tested, leading to incorrect debt calculations. Warnings from staff about potential overcalculations were ignored​.

1. Lack of Pilot Testing

2. No Testing for Debt Calculation Accuracy

3. Ignored Warnings from Staff

4. Urgency Over Quality

5. No user experience testing

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Did not listen to the warnings given by Centrelink staff about flaws.

They are one of the key stakeholders since they interact with the systems and users who would be affected by this change.

They also did not listen to legal experts who would arguably be able to give better judgement to implementation that would affect the whole country.

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The design process did not include key players such as public sector unions, the Australian Tax Office (ATO), and legal experts.
During the deployment, the absence of engagement resulted in misunderstandings, false public pronouncements, and poor communication.

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The system relies on algorithms to automatically generate debt notices, lacks the necessary human scrutiny, and fails to consider the complexity of individual incomes and the reasonableness of data mismatches. Moreover, the system's inability to make accurate judgements about specific situations resulted in many citizens receiving incorrect debt notices. In addition, system-generated notices are not subject to any human review or even basic accuracy checks.

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The technology was not thoroughly tested prior to full implementation, resulting in widespread errors in debt estimations.

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The system was completely automated, with no human intervention, allowing errors to go unreported and causing concern among individuals.

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The system was rolled out without proper consultation with legal experts, welfare organizations, or thorough testing, further deepening its flaws. This combination of poor data handling, lack of human intervention, and inadequate planning led to the system being declared unlawful in 2019