The use of AI and automation in the public sector requires caution to avoid overhyped claims and pitfalls. Missteps can have severe consequences for citizens and public bodies.
Automated systems may impact marginalized communities disproportionately, leading to bias and privacy violations. Examples like Hackney Council and Bristol's RBV system highlight failures in achieving intended outcomes.
Bias in automated systems often arises from training data, resulting in unfair outcomes in tools like predictive policing and A Level algorithms. Discontinuations by West Midlands Police and the Home Office occurred due to bias concerns.
Automation raises data sourcing and consent issues, as seen in controversies such as the Data Protection and Digital Information Bill. Limited transparency from private companies developing these systems complicates public sector oversight.
#ai-in-public-sector #automation-pitfalls #impact-on-marginalized-communities #data-privacy-concerns #transparency-in-automated-systems
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