NITI Aayog releases White Paper: Responsible AI for All (RAI) on Facial Recognition Technology (FRT) | Current Affairs | Vision IAS
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    NITI Aayog releases White Paper: Responsible AI for All (RAI) on Facial Recognition Technology (FRT)

    Posted 02 Jul 2024

    2 min read

    This paper examines FRT as the first use case under NITI Aayog’s RAI principles and aims to establish a framework for responsible and safe development and deployment of FRT within India. 

    • FRT is an AI system which allows identification or verification of a person based on certain images or video data using complex algorithms.

    Working of FRT

    • FRT primarily seeks to accomplish three functions - 
      • Facial detection which relies on algorithms to detect presence of human face.
      • Facial extraction which uses mathematical representations to identify distinctive features on individual faces.
      • Facial recognition which involves automatic cross-referencing of a person’s facial features with pre-existing database. 

    Applications of FRT

    • Security Related: Law and order enforcement (surveillance, identification of persons of interest, monitoring of crowd, screening for violation of public norms).
    • Non-Security related: 
      • Ease of access in services (e.g. contactless onboarding at airports through Digi Yatra).
      • Ease in usability such as unique IDs in educational institutions etc.
      • Authentication for access to products, services, and public benefits.

    Risks with FRT systems

    • Design-based risks: Automation bias, discrimination, lack of accountability, misidentification/inaccuracy due to under-representations in databases.
    • Rights-based issues: Privacy and lack of consent, informational autonomy, and processing of sensitive personal data etc.

    Recommendations for responsible use of FRT

    • Principle of Privacy and Security: Establish data protection regime fulfilling a three-pronged test of legality, reasonability and proportionality.
    • Principles of accountability: Address issues pertaining to transparency, algorithmic accountability and AI biases. 
    • Ensuring Safety and Reliability: Publishing standards of FRT related to explainability, bias and errors.
    • Principle of protection and reinforcement of positive human values: Constitute ethical committee to assess ethical implications and oversee mitigation measures. 
    • Tags :
    • NITI AYOG
    • Facial Recognition Technology
    • FRT
    • Responsible AI for All
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