Why in the news?

The emergence of artificial intelligence has revolutionized various facets of our lives and has even prompted us to reevaluate our concepts of the mind, brain, and consciousness.

Antithesis of Machine Learning (ML):

• Machine Unlearning (MUL) is considered the antithesis of Machine Learning (ML). It was first proposed by Cao and Yang in their work “Towards Making Systems Forget with Machine Unlearning.”
• Machine Unlearning focuses on the ability to make AI models forget specific data they have learned. This concept addresses the challenges of removing or correcting sensitive, false, incorrect, or outdated information from trained AI models.
  • While ML is about learning from data to make predictions or decisions, MUL aims to reverse this process, ensuring that certain data can be effectively and completely erased from the models.
• This concept is crucial for maintaining data privacy, reducing AI bias, and complying with regulations that require the deletion of personal or sensitive information.

Implementation approaches

• Private Approach: Data fiduciaries voluntarily implement MUL algorithms, allowing flexibility but potentially limiting access for smaller companies due to cost and expertise barriers.
• Public Approach: Governments can legislate requirements for MUL implementation, potentially creating a standard framework that data fiduciaries must follow. This can include guidelines under existing data protection laws, as seen in the EU’s AI Act, which addresses data poisoning and mandates security controls.
• International Approach: This emphasizes the need for a global framework for MUL, recognizing that AI innovations have cross-border implications. International standard-setting organizations could play a crucial role in developing these standards

Techniques for Machine Unlearning

• Exact Unlearning: This method completely removes the influence of specific data points from the model.
• Approximate Unlearning: Instead of fully erasing the data’s influence, this technique minimizes its impact on the model’s predictions to an acceptable level.
• Data-Centric Approaches: Techniques like data reorganization and pruning are employed to manage the dataset, making it easier to identify and remove unwanted data points.
• Model-Centric Approaches: These methods involve manipulating the model parameters directly. For example, algorithms can adjust the weights associated with the data points that need to be forgotten, thereby reducing their influence on the model’s outputs.
• Prompting-Based Methods: In large language models (LLMs), developers can use carefully crafted prompts to induce behaviours that mimic unlearning.
• Algorithmic Innovations: New algorithms, such as MU-Mis, focus on minimizing the contribution of specific data points to the model’s decision-making process.

Way forward: 

• Development of Standardized Frameworks: The need to establish a comprehensive regulatory framework for MUL can facilitate its adoption across various sectors. Governments and international organizations should collaborate to create guidelines that mandate the use of MUL techniques for data privacy compliance, similar to the EU’s AI Act. 
• Investment in Research and Education: The need to increase funding and resources should be directed toward research in machine unlearning techniques and their applications.

Mains PYQ: 

Q  The emergence of the Fourth Industrial Revolution (Digital Revolution) has initiated e-Governance as an integral part of government”. Discuss. (2020)

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