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Impact of AI on Policing

The recent advancements in quantum computing will go a long way in disrupting every aspect of life as we know it. The age of quantum computing has begun and it will sooner or later replace or dominate the classical computing.

On one hand the classical computers use 1 and 0 (binary) for carrying out their various tasks including calculations, optimisation etc where as on the other hand quantum computing is based on quantum mechanical phenomena especially quantum superposition and entanglement.

Artificial Intelligence (AI) could be defined as the simulation of human intelligence in computer systems and machines. It involves the development of algorithms, software, and hardware that enable computer systems to perform tasks that usually require human intelligence, such as problem-solving, learning from experience, understanding natural language, recognizing patterns and trends and taking decisions.

The impact of AI is going to be phenomenal in every field including finance, cyber security, administration etc.

The AI powered by classical computing has the ability to solve problems within known limits where as AI powered by quantum computing will have the capabilities beyond known limits. It will be transformational and will revolutionise every field including policing.

Uses in Policing:

AI is being increasingly used to enhance the capabilities of law enforcement agencies to meet their twin goals of upholding law and order and detection and prevention of crime.

It is being used to increase the efficiency and effectiveness and to ensure safe and secure ecosystem for citizens.

Various uses include:
  1. Predictive Policing:

  2. AI could be used to analyse past data and to predict future crimes on the basis of such analysis. The major crime hot spots could be known and resources could be allocated on the basis of such analysis so as to ensure optimal and effective utilisation of resources. The more the power of AI system, more the accurate prediction and this will be a game changer for law enforcement agencies.
  3. Facial Recognition:

  4. As part of invisible policing, law enforcement agencies across the globe are using smart technologies to monitor the safety and security of the public. AI powered system will help in processing huge amounts of data and also in accurate identification of targets.
  5. Crime Analysis and Investigation:

  6. AI systems could help in analysing large amounts of data to find crime patterns and trends which in turn could help law enforcement agencies to perform their tasks effectively.
  7. Natural Language Processing (NLP):

  8. This will help in analysing the cyber world, especially social media posts, dark web to detect any kind of crime/threat to ensure the cyber world is kept safe and secure for common citizens. It will help in preventing rumour mongering and dissemination of false narratives.
  9. Cyber security:

  10. Quantum-powered AI systems will revolutionise the way critical infrastructure and data are safeguarded from cyber threats.
  11. Traffic Management:

  12. AI systems will help in managing the traffic system with much more efficiency and effectiveness.
  13. Training of Law enforcement agency personnel:

  14. The training programs and simulations could be made realistic, and VR/AR/Metaverse will help in providing realistic training scenarios for law enforcement personnel.

Challenges and Concerns:
  • AI systems can carry forward the historical biases and could result in discrimination especially against disadvantaged sections of society.
  • The exhaustive data that needs to be collected to feed AI systems and surveillance that is required could infringe the civil liberties and privacy of citizens.
  • Transparency and Accountability:
    The complexities of AI systems will make it difficult to make them transparent and hold them accountable for their decisions and actions.
  • Public Trust:
    The use of complex systems without any public participation could lead to trust deficit between law enforcement agencies and common citizens.
  • Algorithmic Fairness:
    It is very important to ensure fairness in algorithms and systems for sustainable usage of AI systems.
Policing AI must be designed to treat all individuals equitably, regardless of one's race, gender, caste, colour or socioeconomic status.

  • Transparency and Accountability:
    • AI systems should be made transparent and accountable to public institutions. Regular third-party audits must be the norm to ensure the systems are bias and discrimination-free.
    • Officers or agencies using AI need to be held accountable for any wrongful actions or decisions resulting from these systems.
  • Public Engagement and Awareness:
    • Making a system where common citizens are involved in giving feedback and are thus engaged in ensuring systems are citizen-centric.
  • Data Privacy:
    • Data protection and privacy laws need to be formulated to handle sensitive information of the public.
  • Comprehensive Training:
    • There is a dire need to provide comprehensive training to law enforcement personnel on AI systems, including their limitations and potential biases.
  • Oversight and Regulation Mechanisms:
    • An independent regulatory body needs to be established to oversee the usage of AI systems in policing and ensure compliance with existing guidelines and regulations.
  • Ethical Framework:
    • It is very important to have an ethical framework for the usage of AI systems in policing so as to ensure ethical concerns are dealt with properly. Ethical considerations should form the core of the development and evolution of AI technologies.
AI systems will transform the policing landscape as we know by offering new capabilities to law enforcement agencies.

It is very important to strike a balance between the benefits of AI and the protection of civil liberties and privacy.

Various stakeholders viz lawmakers, administrators, researchers, and law enforcement agencies must collaborate and co-operate to ensure that AI technologies are used ethically to ensure safe and secure ecosystem for public.


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