## 4 Discussion
### Artificial Intelligence (AI) Technologies as Public Safety Goods  
The evidence presented in the study suggests that AI technologies that enhance policing could be deployed more effectively as public safety goods, with the well-being of community members central to the demand signal for such implementation. Moreover, if emerging technologies such as predictive policing, facial recognition, surveillance technologies, and social media scraping and monitoring generating massive volumes of data are well-regulated and carefully implemented, AI could detect criminal activities that would otherwise go unnoticed and facilitate crime prevention and public safety [30]. The resulting AI technologies as public safety goods can potentially increase community confidence in policing and the criminal justice system. However, the study participants expressed concerns about the risks of algorithm bias (diversity and representativeness challenges), the challenge of replicating the human factor of empathy, and concerns about privacy and trust. In addition, fairness, accountability, transparency, and explainability challenges remain as presented in the broader academic debate [1; 12].  
Artificial intelligence has the potential to bridge or hamper police and community engagement and relationships. To ensure AI can serve as a bridge for police and community engagement, AI policing technologies must be fair, accountable, transparent, and explainable. If algorithmic biases are not reined in, they create the potential to create a feedback loop that disproportionately targets minority and low-income communities, recreating the same public perceptions and issues of police discrimination from human officers. Privacy and safety protocols, as well as fairness and non-discrimination regulations, should be put into place in order to protect law enforcement professionals working around and with artificial intelligence and for members of the public who are either direct beneficiaries of the AI or perhaps a suspected criminal target based on historical crime data and datasets reflective of higher crime rates [1]. Moreover, any use of AI policing technologies must respect due process and the presumption of innocence while avoiding policing that discriminates against selected populations. A culture of accountability must be established at an institutional and organizational level that is transparent and showshow AI technologies in the context of police work develop conclusions and reach decisions. As a final point, AI policing technologies must be explainable, at least generally, in how decisions are reached [31]. Law enforcement professionals should, at a minimum, have a broad understanding of the AI technologies used in their jurisdictions and the criminal justice system as a whole. Procedural training for police officers who employ artificial intelligence technologies as they become more prevalent in law enforcement should start with basic police officer training to close the knowledge gap and foster the explainability principle. Moreover, AI technologies should not be so incomprehensible that the public cannot determine their use as a public good to promote public safety [32]. Figure 5 conceptualizes the findings from the study and from the background literature through the responsible design of AI policing technologies that addresses ethical and societal concerns.  
Figure 5: Addressing ethical and societal concerns by responsible design in AI policing technologies. This figure captures the findings from the study and synthesizes a framework with existing literature for addressing the ethical and societal concerns in AI policing by responsible design [1; 2; 10; 11].