The application of Artificial Intelligence as a dependable source of input for administrative rational decision-making is a debatable issue. Critically examine the statement from the ethical point of view.

Introduction 

Artificial intelligence (AI) is the ability of machines to perform human-like cognitive functions, such as learning, problem-solving, and decision making.

Arguments in favour of administrative rational AI-driven decision-making:

• Efficiency and accuracy:

• • • Example: AI-powered chatbots help citizens track their passport applications, reducing query resolution time by 75% (India’s Passport Seva Project).

• Objectivity:

• • • Example: AI-driven recruitment tools used by Indian Railways to reduce bias.

• Scalability:

• • • Example: AI-powered tax systems for GST processing and compliance

•  Data-driven insights:

• • • Example: AI analyses satellite images to predict crop yields, enabling informed agricultural decisions.e.g. Kisan Suvidha

• Fairness and justice:

• • Example: AI-powered bail decision systems aim to reduce racial bias (Arnold Foundation’s Public Safety Assessment).

Ethical concerns and limitations:

• Bias in data and algorithms:

• • Example: COMPAS AI system disproportionately labelled black defendants as high-risk, perpetuating racial bias.

• Lack of transparency and explainability:

• • Example: Google’s AI-powered job search algorithm was criticised for lack of transparency, potentially perpetuating bias.

• Accountability and responsibility:

• • Example: Tesla’s Autopilot feature raised questions about accountability when involved in accidents.

• Job displacement and social impact:

• • Example: Amazon’s AI-powered warehouse automation will replace thousands of human workers.

• Privacy and surveillance:

• • Example: China’s AI-powered surveillance system, Social Credit System, raises concerns about citizen’s privacy.

• Value alignment:

• • Example: AI-powered healthcare systems prioritise cost efficiency over patient well-being (UK’s NHS AI-powered triage).

• Dependence on data quality:

• • Example: Poor data quality led to inaccurate COVID-19 predictions, highlighting AI’s limitations.

The integration of Artificial Intelligence in decision-making processes presents a double-edged sword. While AI enhances efficiency, accuracy, and scalability, it also introduces ethical concerns that compromise fairness, transparency, accountability, and human values.

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