Aditorial 1 : Discriminatory: Why SC Struck Down Caste Rules in Jail Manuals
Context: Supreme Court strike down discriminatory rules across state prison manuals
Introduction: The Supreme Court struck down a series of rules in several state prison manuals which reinforce caste differences and target members of marginalised communities, especially those dubbed criminal tribes in the colonial era for violating the fundamental rights of the prisoners.
The Petition and the Ruling
- Plea: The decision follows a plea filed by journalist Sukanya Shantha, highlighting a series of rules and provisions in prison manuals from states including Uttar Pradesh, West Bengal, Andhra Pradesh, Madhya Pradesh, Odisha, Kerala, Tamil Nadu, Maharashtra, Karnataka, Rajasthan and Himachal Pradesh.
- The rules deal with the classification of prisoners and the assignment of work based on such classifications.
- Decision: According to the 148-page decision authored by Chief Justice of India D Y Chandrachud, these manuals assigned prison work in ways that “perpetuate(s) caste-based labour divisions and reinforce social hierarchies”, violating the fundamental rights of prisoners.
- Example: Under the Madhya Pradesh Jail Manual, 1987, prisoners from the Mehtar caste, a Scheduled Caste community are specifically assigned latrine cleaning work.
- The Supreme Court has declared all the provisions and rules in question unconstitutional, and directed states and union territories to revise their prison manuals within three months.
- The Supreme Court directed the Centre to make necessary changes to address caste discrimination in the Model Prison Manual 2016 and the draft Model Prisons and Correctional Services Act, 2023 within three months.
Manuals Reinforcing Caste Prejudices and Stereotypes
- Criminal Tribes Act of 1871: It allowed the British Raj to declare any community as a criminal tribe if they were deemed addicted to systematic commission of non-bailable offences.
- The tribes were forced to settle in designated locations, subjected to constant checks and the threat of arrest without a warrant, and more draconian restrictions based on a stereotype which considered several marginalized communities as born criminals.
- It was repealed in 1952 and the former criminal tribes became known as denotified tribes.
- According to the Supreme Court, the jail manuals/rules reinforce stereotypes against denotified tribes through the classification between habitual and non-habitual criminals.
- The court used examples of Madhya Pradesh, Andhra Pradesh, Tamil Nadu, Kerala and West Bengal.
Fundamental Rights of Prisoners Violated
- Article 14: Right to Equality
- Segregating prisoners on the basis of caste reinforces caste differences or animosity that ought to be prevented at the first place and such classification deprives some of them of equal opportunity to be assessed for their correctional needs, and consequently, opportunity to reform.
- Article 15: Right Against Discrimination
- By assigning cleaning and sweeping work to the marginalized castes, and allowing the high castes to do cooking, the Manuals directly discriminate.
- Article 17: Abolition of Untouchability
- The notion that an occupation is considered as degrading or menial is an aspect of the caste system and untouchability.
- Article 21: Right to Life with Dignity
- The rules in prison manuals, restrict the reformation of prisoners from marginalised communities and deprive(s) prisoners from marginalized groups of a sense of dignity and the expectation that they should be treated equally, violating Article 21.
- Article 23: Prohibition of Forced Labour
Imposing labour or work, which is considered impure or low-grade, upon the members of marginalized communities amounts to forced labour under Article 23.
Editorial 2 : Making Machines Learn
Context: 2024 Nobel Prize in Physics
Introduction
- This year’s Nobel Prize in Physics recognises two scientists whose work laid the foundations of the AI technology and tools.
- John Hopfield and Geoffrey Hinton were awarded the Nobel Prize for their foundational discoveries and inventions that enable machine learning with artificial neural networks.
- The two scientists, working separately, did most of their ground-breaking research in the 1980s, but the impact of their work is beginning to be felt only now.
Hopfield’s Research: Mimicking the Brain
- The big success of Hopfield and Hinton has been in developing computer algorithms that mimic the functioning of the human brain in performing common tasks.
- The origin of the term AI dates back to the mid-1950s, when scientists began speaking of computers as intelligent machines.
- Efforts to get a computer to imitate the functioning of the human brain did not make much headway until Hopfield’s revolutionary work in the 1980s.
- Hopfield built an artificial neural network, resembling the network of nerve cells in the human brain, that allowed computer systems to remember and learn.
- Hopfield’s network processed information using the entire network structure, and not its individual constituents.
- This was unlike traditional computing in which information is stored or processed in the smallest bits.
- Hopfield’s network was inspired by a physical system called spin glass, alloys with some very special properties.
Significance
- When a Hopfield network is given new information, like an image or a song, it captures the entire pattern in one go, remembering the connections or relationships between the constituting parts, like pixels in the case of images.
- It allows the network to recall, identify, or regenerate that image or song when an incomplete, or similar-looking, image is passed as input.
- Hopfield’s work was a leap towards enabling pattern recognition in computers, something that allows face recognition or image improvement tools that are common now.
Hinton’s Research: Deep Learning
- Hinton took forward the work of Hopfield and developed artificial networks that could perform much more complex tasks.
- Hopfield networks could recognise simple patterns of shape or sound, Hinton’s advanced models could understand voices and pictures.
- Hinton’s neural networks could be strengthened through training i.e. repeated inputs of data.
- Hinton developed a method called backpropagation that enabled the artificial neural networks to learn from previous mistakes and improve itself.
- The process of continuous learning and improvement by training on large datasets led to the development of deep neural networks that contained multiple layers of networks.
- Deep learning is at the heart of modern speech and image recognition, translation, voice assistance and self-driving cars.
- A pattern recognition algorithm using deep neural networks developed by Hinton and his students, called AlexNet, showed dramatic improvements in recognising images at the 2012 ImageNet Visual Recognition Challenge.
- In 2018, Hinton was awarded the Turing Prize, the most prestigious award in computer science