Tuesday, August 11, 2020

India’s New Education Policy (NEP) 2020: What it proposes for Schools

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Vanshita Banuana

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India’s New Education Policy (NEP) 2020: What it proposes for Schools

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Global Views 360

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August 11, 2020

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Students sitting in a classroom

Students sitting in a classroom | Source: Yogendra Singh via Unsplash

On 30th July 2020, the Indian government’s Ministry of Human Resource Development (MHRD) was renamed the Ministry of Education as it announced the new National Education Policy (NEP) 2020.

The National Education Policy is an in-depth framework outlining the future and development of education in India. It’s recommendations guide what the priorities and goals of educational institutions should be in the coming years. The first NEP was passed in 1968; while it gets revised occasionally, a new NEP has only been passed two times since then, in 1986 and now in 2020.

Prime Minister Narendra Modi’s and the government was hailed by RSS-affiliated educational organisations for the NEP as a step to connect the education with the roots of India. They reportedly had quite an influence during the drafting of NEP, even going as far as to say that “60-70 percent” of their demands have been met.

On the other hand, NEP received criticism from the opposition parties like Congress, the Communist Party of India (Marxist), and political figures in West Bengal and Tamil Nadu. The criticism was primarily for bypassing Parliamentary discussion, and its ill-fittedness in the context of the COVID-19 pandemic and the ever-growing digital divide left in its wake in the education sector.

The NEP’s ambitious claims and propositions are divided into two broad categories: school, and higher education.

NEP at School Level

At school level, perhaps the biggest change is the move away from the 10+2 structure to a 5+3+3+4 one, signifying four stages of school education across ages 3-8 years (Foundational), 8-11 years (Preparatory), 11-14 years (Middle) and 14-18 years (Secondary). This new structure claims to be based greatly on the cognitive development of children and prioritising areas of focus through these ages.

The new structure also talks about the Early Childhood Care and Education (ECCE), which aims to include pre-schools and aanganwadis (government sponsored rural child care centres in India) in an effort to impart play and activity focused learning, and train aanganwadi workers to achieve the same.

However, the treatment of the aanganwadi program is already under question from the governance and child right watchdogs and activists . This program is poorly funded and workers are poorly paid which makes the promise of training the workers for implementing the NEP goals seem quite wishful. This means rural students are likely to continue to be many steps behind urban students from the ECCE i.e ‘Foundational’ stage itself.

National Assessment Centre

NEP proposes the establishment of a National Assessment Centre, PARAKH, to set norms and guidelines for evaluations across all school boards. Report-cards are also to be redesigned and include self, teacher and peer assessment. However, the details of what will entail in these, especially peer assessment, are vague and do not take into cognizance the rampant prejudice and bullying experienced by students at the hands of peers as well as teachers on bases of weight, religion, gender, caste, class, sexuality and more. Such discriminatory practices will hurt the students from marginalised communities in both disguised and explicit ways.

The 3 Language Formula

A more controversial change comes with the 3-Language Policy, which essentially asks that “wherever possible,” the regional language or mother tongue of a student be adopted as the medium of instruction “until at least Class 5, but preferably till Class 8 and beyond.”

All schools will teach three languages, of which at least two must be native to India. The draft NEP, in fact, mandated that one of these languages be Hindi; after protests against this ‘Hindi imposition’ such as by the southern state of Tamil Nadu, this provision was removed and it has supposedly been left to the state, school and student to decide which languages would be taught.

The so-called flexibility of the policy comes at the cost of uniformity. Since the colonial era, English education has served as a means of upward social mobility for castes and tribes that had historically been denied education under Brahmanical hegemony, this progress is threatened by making English ‘optional’ in any form.

There are also unaddressed and obvious scenarios of parents who migrate or get transferred to different states, parents who speak another language at home than the regional language, and children who grow up in multilingual homes, all of which are commonplace across India. How likely is it that every student in a classroom speaks the same mother tongue or is from the same region?

Promotion of Sanskrit

The NEP desires that the rich ancient languages of India be brought back to the forefront and be given more focus as languages that can be taken up by students. In this regard it shines a spotlight on Sanskrit, a classical language rooted in Hinduism which was for centuries only accessible to Brahmins and some other upper castes. The pedestal upon which Sanskrit has been placed is being seen as discriminatory towards the large population of India who either do not have historic ties to Sanskrit or were denied access to it.

While the NEP does mention other languages that have had a strong foothold in India for a long time, such as Persian and Prakrit, it notably omits mention of Urdu and seems especially driven to ‘promote’ Sanskrit.

Vocational Education

The NEP points out that a very small portion of the Indian workforce in the age group 19-24 is exposed to vocational education, and therefore recommends that it be integrated in schools and higher education in a phased manner over the next 10 years.

A focus on vocational education starting from ages as young as 14 is also questionable, since non-formal education, often valued less than degrees, might hinder the education of poor children. This may contribute to deepening the class divide in India since receiving Undergraduate or Postgraduate degrees often guarantees poverty alleviation for such students.

Additionally, vocational education will likely form a vicious cycle with the entrenched caste system in India, reinforcing each other and the inequalities therin.

It has been repeatedly asserted by experts, citizens and politicians alike that the NEP caters more to the corporate interests over the needs of underprivileged students, and has brought much uncertainty around the question of language.

It becomes vague at key points, falling back on the argument that it is only a ‘guiding document,’ which only makes its stances seem weaker, in both theory and practice.

Whether the NEP as a whole manages to turn the tide of education in favour of those who need it the most, and is able to mobilise it as a tool for progress, presently seems more fantastical than plausible.

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February 4, 2021 5:22 PM

Automated Facial Recognition System of India and its Implications

On 28th of June 2019, the National Crime Records Bureau (NCRB) opened bids and invited Turnkey Solution providers to implement a centralized Automated Facial Recognition System, or AFRS, in India. As the name suggests, AFRS is a facial recognition system which was proposed by the Indian Ministry of Home Affairs, geared towards modernizing the police force and to identify and track criminals using Facial Recognition Technology, or FRT.

The aforementioned technology uses databases of photos collected from criminal records, CCTV cameras, newspapers and media, driver’s license and government identities to collect facial data of people. FRT then identifies the people and uses their biometrics to map facial features and geometry of the face. The software then creates a “facial signature” based on the information collected. A mathematical formula is associated with each facial signature and it is subsequently compared to a database of known faces.

This article explores the implications of implementing Automated Facial Recognition technology in India.

Facial recognition software has become widely popular in the past decade. Several countries have been trying to establish efficient Facial Recognition systems for tackling crime and assembling an efficient criminal tracking system. Although there are a few potential benefits of using the technology, those benefits seem to be insignificant when compared to the several concerns about privacy and safety of people that the technology poses.

Images of every person captured by CCTV cameras and other sources will be regarded as images of potential criminals and will be matched against the Crime and Criminal Tracking Networks and Systems database (CCTNS) by the FRT. This implies that all of us will be treated as potential criminals when we walk past a CCTV camera. As a consequence, the assumption of “innocent until proven guilty” will be turned on its head.

You wouldn’t be surprised to know that China has installed the largest centralized FRT system in the world. In China, data can be collected and analyzed from over 200 million CCTVs that the country owns. Additionally, there are 20 million specialized facial recognition cameras which continuously collect data for analysis. These systems are currently used by China to track and manipulate the behavior of ethnic Uyghur minorities in the camps set up in Xinjiang region. FRT was also used by China during democracy protests of Hong Kong to profile protestors to identify them. These steps raised concerns worldwide about putting an end to a person’s freedom of expression, right to privacy and basic dignity.

It is very likely that the same consequences will be faced by Indians if AFRS is established across the country.

There are several underlying concerns about implementing AFRS.

Firstly, this system has proven to be inefficient in several instances. In August 2018, Delhi police used a facial recognition system which was reported to have an accuracy rate of 2%. The FRT software used by the UK's Metropolitan Police returned more than a staggering 98% of false positives. Another instance was when American Civil Liberties Union (ACLU) used Amazon’s face recognition software known as “Rekognition” to compare the images of the legislative members of American Congress with a database of criminal mugshots. To Amazon’s embarrassment, the results included 28 incorrect matches.. Another significant evidence of inefficiency was the outcome of an experiment performed by McAfee.  Here is what they did. The researchers used an algorithm known as CycleGAN which is used for image translation. CycleGAN is a software expert at morphing photographs. One can use the software to change horses into zebras and paintings into photographs. McAfee used the software to misdirect the Facial recognition algorithm. The team used 1500 photos of two members and fed them into CycleGAN which morphed them into one another and kept feeding the resulting images into different facial recognition algorithms to check who it recognized. After generating hundreds of such images, CycleGAN eventually generated a fake image which looked like person ‘A’ to the naked eye but managed to trick the FRT into thinking that it was person ‘B’. Owing to the dissatisfactory results, researchers expressed their concern about the inefficiency of FRTs. In fact mere eye-makeup can fool the FRT into allowing a person on a no-flight list to board the flight. This trend of inefficiency in the technology was noticed worldwide.

Secondly, facial recognition systems use machine learning technology. It is concerning and uncomfortable to note that FRT has often reflected the biases deployed in the society. Consequently, leading to several facial mismatches. A study by MIT shows that FRT routinely misidentifies people of color, women and young people. While the error rate was 8.1% for men, it was 20.6% for women. The error for women of color was 34%. The error values in the “supervised study” in a laboratory setting for a sample population is itself simply unacceptable. In the abovementioned American Civil Liberties Union study, the false matches were disproportionately African American and people of color. In India, 55% of prisoners undertrial are either Dalits, Adivasis, or Muslims although the combined population of all three just amounts to 39% of the total population (2011 census). If AFRS is trained on these records, it would definitely deploy the same socially held prejudices against the minority communities. Therefore, displaying inaccurate matches. The tender issued by the Ministry of Home Affairs had no indication of eliminating these biases nor did it have any mention of human-verifiable results. Using a system embedded with societal bias to replace biased human judgement defeats claims of technological neutrality. Deploying FRT systems in law enforcement will be ineffective at best and disastrous at worst.

Thirdly, the concerns of invasion of privacy and mass surveillance hasn’t been addressed satisfactorily. Facial Recognition makes data protection almost impossible as publicly available information is collected but they are analyzed to a point of intimacy. India does not have a well established data protection law given that “Personal data Protection Bill” is yet to be enforced. Implementing AFRS in the absence of a safeguard is a potential threat to our personal data. Moreover, police and other law enforcement agencies will have a great degree of discretion over our data which can lead to a mission creep. To add on to the list of privacy concerns, the bidder of AFRS will be largely responsible for maintaining confidentiality and integrity of data which will be stored apart from the established ISO standard. Additionally, the tender has no preference to “Make in India'' and shows absolutely no objections to foreign bidders and even to those having their headquarters in China, the hub of data breach .The is no governing system or legal limitations and restrictions to the technology. There is no legal standard set to ensure proportional use and protection to those who non-consensually interact with the system. Furthermore, the tender does not mention the definition of a “criminal”. Is a person considered a criminal when a charge sheet is filed against them? Or is it when the person is arrested? Or is it an individual convicted by the Court? Or is it any person who is a suspect? Since the word “criminal” isn’t definitely defined in the tender, the law enforcement agencies will ultimately be able to track a larger number of people than required.

The notion that AFRS will lead to greater efficacy must be critically questioned. San Francisco imposed a total ban on police use of facial recognition in May, 2019. Police departments in London are pressurized to put a stop to the use of FRT after several instances of discrimination and inefficiency. It would do well to India to learn from the mistakes of other countries rather than committing the same.

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