Algorithmic decision-making

The use of Automated Decision Systems (ADS) and mathematical models to automate conclusions, recommendations, or actions that affect human lives, often reducing the role of human oversight.

Critical Dimensions

• Algorithmic Bias: Systematic and unfair discrimination resulting from biased training data or model design.
• Transparency and Explainability: The “Black Box” problem, where the logic behind a decision is inaccessible to users or regulators.
• Algorithmic Accountability: The difficulty in assigning legal or ethical responsibility for errors or harms caused by automated processes.
• Automation Bias: The human tendency to over-rely on or trust automated suggestions, even when they are erroneous.

Case Studies & Failures

• Robodebt Scheme: Australia’s Unlawful Algorithm Causing Deaths
  • An automated system implemented by the Australian government in 2016 to identify and recover alleged welfare overpayments.
  • Utilized unlawful automated processes to issue debt notices.
  • Linked to significant human harm and documented deaths.
  • Reference: 2026 04 24 Robodebt Scheme Australias Unlawful Algorithm Causing Deaths