The sterile walls of the Tennant Creek watch house hold a silence broken only by the echoes of grief. Last Saturday, that silence became a shroud for a 44-year-old Aboriginal mother of five, who died in custody, igniting a familiar, painful question: who will truly investigate? While NT police point to a medical episode as the likely cause, the North Australian Aboriginal Justice Agency (NAAJA) is adamant – an internal investigation is simply not enough. This tragedy underscores a persistent call for independent oversight, a demand amplified by the disproportionate number of Indigenous deaths in custody across Australia.

The death, occurring on December 27th, followed the woman's arrest on Christmas Day for an alleged aggravated assault. Details surrounding the circumstances inside her cell remain scarce, fueling concerns about transparency and accountability. The NT police major crime unit is currently investigating, with oversight from the coroner, but for many, this offers little reassurance. The inherent conflict of interest in police investigating themselves is a long-standing point of contention.

This case arrives at a time when artificial intelligence is increasingly being explored as a potential tool for enhancing transparency and objectivity in law enforcement. AI-powered systems, for example, could be used to analyze body-worn camera footage, identify potential biases in policing practices, and even predict and prevent adverse events in custody. However, the implementation of such technologies raises complex ethical questions. Who controls the algorithms? How is data privacy protected? And can AI truly eliminate human bias, or does it simply reflect the biases of its creators?

"An independent investigation is crucial to ensure transparency and accountability," said a spokesperson for NAAJA. "The community needs to have confidence that this death will be thoroughly and impartially examined. We cannot continue to allow internal investigations to be the sole mechanism for addressing these tragedies." This sentiment reflects a broader distrust in the system, fueled by historical injustices and a perceived lack of responsiveness to Indigenous concerns.

The application of AI in this context is not without its challenges. Algorithmic bias, where AI systems perpetuate or amplify existing societal biases, is a significant concern. If the data used to train an AI system reflects biased policing practices, the system may inadvertently reinforce those biases. Furthermore, the "black box" nature of some AI algorithms can make it difficult to understand how decisions are being made, hindering accountability and transparency.

Despite these challenges, AI offers potential solutions. For example, AI-powered systems could be used to analyze data on deaths in custody, identify patterns and risk factors, and develop strategies for prevention. AI could also be used to monitor conditions in detention facilities, detect signs of distress in detainees, and alert staff to potential emergencies.

Looking ahead, the integration of AI into the investigation of deaths in custody requires careful consideration of ethical and legal implications. It is essential to ensure that AI systems are used in a way that promotes fairness, transparency, and accountability. This includes developing clear guidelines for data collection and use, ensuring that AI algorithms are free from bias, and providing mechanisms for independent oversight and review.

The death in Tennant Creek serves as a stark reminder of the urgent need for systemic reform. While AI offers potential tools for improving transparency and accountability, it is not a panacea. Ultimately, addressing the issue of Indigenous deaths in custody requires a multifaceted approach that includes independent investigations, cultural awareness training for law enforcement officers, and a commitment to addressing the underlying social and economic factors that contribute to Indigenous incarceration rates. The silence in the Tennant Creek watch house demands a response – one that is both just and effective.