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Decoding Reservation in India: Is it a Constitutional Flaw or Unnecessary Favor?

The idea of 'reservation' has generated contradictory views from teachers and students all around the world

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Representational image. Pixabay

November 12, 2016: The word ‘reservation’ came up with the idea of representative government, where for the first time numbers mattered. The inequality of Indian society has solidified the need for numeric representation. The caste based representation, no doubt created a more confident lower class mass with their greater involvement in the public sphere. Reservation in education has evolved as a major challenge for lakhs of students. Far from providing an equal opportunity it has an electoral agenda. Education has been politicized based on reservation.

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However, the backward class proportion is still underrepresented. Article 15 (1) of the Constitution says, “State shall not discriminate any citizen on grounds of religion, race, caste, sex, place of birth or any of them”, it also provides for compensatory or protective discrimination in favor of certain sections of the disadvantaged people. Article 15(4) of the constitution stipulates that notwithstanding the provision stated above, the state can make “special provision for the advancement of any socially and educationally backward classes of citizens or for the Scheduled Castes and Scheduled Tribes”. Thus constitution itself provides contradictory clause.

The idea of ‘reservation’ has generated contradictory views from teachers and students all around the world. ‘Caste should no longer be the eligibility criteria for reservation, rather income should be’ said HemangoAkshayHiwale, an M.phill aspirant in Jamia Millia Islamia University. Prakash, another student of same university claims reservation as a ‘good thing but in present scenario in India need to be reformed.’

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In August 1999, the Supreme Court ruled that for admissions at super-specialty level in medicine and engineering faculties, no special provisions like SCs, STs, BCs were permissible. Even among the quotas there are also sub-quotas. For example, in Andhra Pradesh, 15% of the seats in each course of study reserved for Scheduled Castes are in turn allotted, in proportion to their population, to four categories of SCs classified as A, B , C and D.

This affirmative step has so far brought with it social justice. US Carnegie Mellon University, published a study in American Economic Review, which shows that reservations do place those who do not qualify for affirmative action at a disadvantage, 53,374 scheduled caste, scheduled tribe, other backward classes and general category students are at a loss.

Reservation in the past decades has increased the numbers of scheduled castes and scheduled tribe families with highly educated members, who can encourage and provide support for younger family members to continue their education. Thus, reservation in education as of now is more of a luxury scheme for these classes as the benefit is only confined to a limited population, whether they need it or not. The real needy ones are at a loss to whom the information or the financial access is debarred.

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Instead of favoring reservation, the government should increase the number of universities and government jobs for the benefit of its people. Nationalization of education can also be a solution to this issue. When the discrepancies within the universities are omitted; i.e. equal access to education without compromising the quality of education the disadvantaged students in remote areas will get justice. The proliferation of universities in villages with good teachers can also be an alternative.

Reservation should not be treated as a vote bank or an emotional quotient but a practical measure to help the lower section of the society. It should be kept in mind that the extended favor to the marginalized section might create an insufficiency for the other classes. With the critical Indian class structure, it should be kept in mind that any reform of upliftment will be judiciously measured before its implementation.

by Saptaparni Goon of NewsGram. Twitter: @saptaparni_goon

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Scientists Develop AI Tool to Detect Racial, Gender Discrimination

"To avoid discrimination on the basis of race, gender or other attributes you need effective tools for detecting discrimination. Our tool can help with that," he said

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"We're beginning to see the first instances of artificial intelligence operating as a mediator between humans, but it's a question of: 'Do people want that?" Pixabay

Scientists have developed a new artificial intelligence (AI) tool for detecting unfair discrimination — such as on the basis of race or gender.

Preventing unfair treatment of individuals on the basis of race, gender or ethnicity, for example, has been a long-standing concern of civilised societies.

However, detecting such discrimination resulting from decisions, whether by human decision makers or automated AI systems, can be extremely challenging.

“Artificial intelligence systems — such as those involved in selecting candidates for a job or for admission to a university — are trained on large amounts of data,” said Vasant Honavar, a professor at Pennsylvania State University (Penn State) in the US.

“But if these data are biased, they can affect the recommendations of AI systems,” Honavar said.

He said if a company historically has never hired a woman for a particular type of job, then an AI system trained on this historical data will not recommend a woman for a new job.

“There’s nothing wrong with the machine learning algorithm itself,” said Honavar.

“It’s doing what it’s supposed to do, which is to identify good job candidates based on certain desirable characteristics. But since it was trained on historical, biased data it has the potential to make unfair recommendations,” he said.

The team created an AI tool for detecting discrimination with respect to a protected attribute, such as race or gender, by human decision makers or AI systems.

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“Artificial intelligence is now one of the fastest-growing areas in all of science and one of the most talked-about topics in society.” VOA

“We can minimise gender-based discrimination in salary if we ensure that similar men and women receive similar salaries,” said Aria Khademi, graduate student at Penn State.

The researchers tested their method using various types of available data, such as income data from the US Census Bureau to determine whether there is gender-based discrimination in salaries.

They also tested their method using the New York City Police Department’s stop-and-frisk programme data to determine whether there is discrimination against people of colour in arrests made after stops.

“We analysed an adult income data set containing salary, demographic and employment-related information for close to 50,000 individuals,” said Honavar.

“We found evidence of gender-based discrimination in salary. Specifically, we found that the odds of a woman having a salary greater than USD 50,000 per year is only one-third that for a man.

“This would suggest that employers should look for and correct, when appropriate, gender bias in salaries,” he said.

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Although the team’s analysis of the New York stop-and-frisk dataset — which contains demographic and other information about drivers stopped by the New York City police force — revealed evidence of possible racial bias against Hispanics and African American individuals, it found no evidence of discrimination against them on average as a group.

“You cannot correct for a problem if you don’t know that the problem exists,” said Honavar.

“To avoid discrimination on the basis of race, gender or other attributes you need effective tools for detecting discrimination. Our tool can help with that,” he said. (IANS)