Friday, December 11, 2020

Anti NRC-CAA Protests: How it shattered the Stereotypes of “Voiceless Indian Muslim Women”

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Nishitha Mandava

Article Title

Anti NRC-CAA Protests: How it shattered the Stereotypes of “Voiceless Indian Muslim Women”

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Global Views 360

Publication Date

December 11, 2020

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Mural featuring Muslim Women in Shaheen Bagh

Mural featuring Muslim Women in Shaheen Bagh | Source: DTM via Wikimedia

The anti CAA-NRC protest that erupted in December 2019 across many places in India has broken many widely stereotypes associated with Muslim women. The most common narrative of Indian Women in general and Indian Muslim Women in particualar revolves around the oft repeated claims of them being oppressed at home, discriminated in society, and confined to the household. However the widespread participation of Muslim women in the pro-constitution anti-NRC-CAA movement has broken numerous stereotypes regarding women in general and Muslim women in particular. They did not limit their role to silent bystanders; instead, they were actively involved in every dimension of these movements and demonstrated that they are not only capable of understanding complex issues, but can also orchestrate grassroot movements to oppose the oppressive and discriminatory policies introduced by the government.

Shaheen Bagh, a neighbourhood in South Delhi, became a prominent symbol for their non-violent resistance. It was the longest protest site against NRC-CAA. “I hardly ever leave my house alone. My son or husband accompanied me even to the nearby market. So I found it tough at first to be out here. But I feel compelled to protest” said Firdaus Shafiq, one of the protestors at Shaheen Bagh. What made the protests unusual was that protestors like Firdaus Shafiq were not activists they were everyday Muslim women and mostly homemakers.

Shaheen Bagh inspired women across India to stand together. Muslim women in Central Mumbai came up with ‘Mumbai Bagh’ to express their solidarity to Shaheen Bagh. Mumbai Bagh included almost four thousand women protesting. These large scale agitations encouraged women to join from different walks of life and religion to protest for the shared cause of revoking CAA and NRC.

Safoora Zargar Leading a Protest | Source: thescrbblr.in

However, all these protests have come with a price. To repress these agitations, several women have been arrested, some under the draconian Unlawful Activities Prevention Act (UAPA). Women like Safoora Zargar and Gulfisha Fatima who have become icons of dissent have been arrested under the same. Even though Safoora Zargar was given bail on humanitarian grounds since she was pregnant, Gulfisha Fatima’s petition was dismissed. What is highly unfortunate and surprising is that most of these arrests have been made when the country is going through a pandemic.

Muslim women in India have been predominantly labelled as veiled, submissive, uneducated and voiceless. Thus, their mass level involvement has come as a surprise to many Indians. These women have reclaimed their spot in the public sphere, but this is not a sudden change. On one level, their participation could be attributed to the growing anxieties among the Muslim community about NRC-CAA. Even though officially NRC is meant to act as a check against illegal immigration, there has been a growing belief that it is being used to marginalise the Muslims and strip them of their identity. Thus this fear of losing their home is one of the motivators for active participation of the Muslim women, but the origin for this high self-awareness among them also has several other reasons—one of the prominent one being the increasing rate of education among the women of the Muslim community.

The All India Survey on Higher Education (AISHE) report for 2017-2018 indicates the same. The enrolment rate in schools for Muslim girls has increased by 46%. The same survey also indicates that in the same period, 49% of Muslims that were enrolled in higher education were women. Such data suggest that anti-NRC-CAA protests acted as a portal to show the sociological changes that Muslim women were going through and that the belief that Muslim women are uneducated or illiterate is far from the truth.

Muslim women’s participation in these political movements has not only incorporated a sense of novelty to these movements but also helped women to recognise the strength within them and that they too can be the ones that lead change.  It has also challenged several social constructs of patriarchy and provided a more prominent place for women in India’s socio-political fabric.

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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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