Sunday, July 26, 2020

Suppressing the Minority Voting: An effective discrimination tactic of the US Conservatives

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Nikhita Gautam

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Suppressing the Minority Voting: An effective discrimination tactic of the US Conservatives

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Global Views 360

Publication Date

July 26, 2020

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Protester at George Floyd protest in USA

Protester at  George Floyd protest in USA | Source: Clay Banks via Unsplash

The recent protests over George Floyd’s death and reactions of the conservatives against the protest laid bare the systemic injustice and oppression faced by the people of color in the USA.

The other, albeit invisible form of discrimination perpetuated by the conservative political establishment in the USA is “Minority Voter Suppression”.

Though it may seem improbable that long after the Jim Crow laws are junked and Civil Right Laws are in place, the effort to disenfranchise the Black people is still going on.

The major piece of legislation which protected minorities from electoral exploitation was the Voter Registration Act which underpins the basic ideal of a universal adult franchise by specifically addressing and combating voting discrimination.

To ensure the representation to minority communities, this act mandated that “At-Large Elections”, where the whole of the jurisdiction elects all of the city council, were replaced by the single member districts in which each community selects a person to represent them in the city council.

It was also prohibited to draw the voting district in such a way that  minorities could be clubbed in only a few of the districts. It was also made mandatory for those states which have a history of discrimination to get pre-clearance from the justice department before changing their voting laws.

This law, however, lost its power in a process which began in 1980. In 1980 the Supreme Court ruled that at-large elections were not unconstitutional, on their own. In 1995, the Court began restricting the construction of majority minority districts on grounds that it segregates people on the basis of race.

In 2008, the court ruled that a photo voter ID law in Indiana was constitutional and was in state interest to protect against voter fraud (research shows that photo voter IDs provide disincentive to vote for people of color). The voter ID law requires the voters to have a government-issued photo ID to cast a ballot.

In 2013, the Supreme Court scrapped the part of the law which stated that some states (which had an alleged history of discrimination) needed federal preclearance in any changes of their voting laws, meaning that the state laws would need approval from the federal government before being put into practice. This was done so citing that the methods which determined discriminatory states were invalid.

All of these slowly chipped away at the laws, and especially the 2013 Shelby County vs Holder case which led to a host of issues whVoter Suppression is Still One of the Greatest Obstacles to a More Just Americaich directly/indirectly keep a significant proportion of minorities from voting. Few of such actions are closure or relocation of precincts in majority black areas, purge of minority voters from the voter lists, and elimination of Sunday early voting days which are preferred by black voters.

There have been attempts to restrict registration drives in Tennessee on the basis that many of the forms were incomplete.

There have also been laws enacted which needed people to participate regularly in elections to keep their voting rights and reply to a letter sent to their residence, which makes it difficult for Black and Hispanics due to obscure areas and the fact that they’re half as likely than other people to get a day off work to vote.

The governor of Georgia, Brian Kemp, has been accused of using intimidation tactics to scare minority communities.

In Texas, the acting secretary of state said that he had a list of 95,000 non-citizens who were registered for voting in the state, and 58,000 of them had already cast a vote. That claim was proven untrue when it was noted that there were tens of thousands of people who were naturalized citizens.

In many states, felons are not allowed to vote even after they have served their sentence, and in Florida felons are allowed to vote only if they have paid an array of fees after serving their sentence, which sets an economic bar on their ability to vote.

This is evident that forces working against the equal rights for the minority communities are still working at full force to reverse the gains of civil right movements. The fight for the unhindered voting rights for the minority communities in the USA at the social, political, and judicial front will continue in the foreseeable future.

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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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