Thursday, July 2, 2020

Are Black Americans victims of Police Militarization in the US

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Nikhita Gautam

Article Title

Are Black Americans victims of Police Militarization in the US

Publisher

Global Views 360

Publication Date

July 2, 2020

URL

Chicago Police

Chicago Police | Source: noahwesley via Creative Commons

In the USA, there are reports of police using tear gas, flashbangs, and many other weapons to fight against the riots which are occurring now in over 350 cities against police brutality against the Black Community. There have been many reports on how the police brutality is disproportionate in terms of race; the Black people are thrice as likely to have violence committed on them by the police force than the whites, and the factor is 1.5 for the Hispanics. There is a first-hand account of a person present in the recent protests who talks about the use of batons on demonstrators.

This, however, leads to the question whether it was the militarization of the police force that caused violence towards minority communities. The police militarization was, in the aftermath of the 9/11 US terrorist attack, justified by the policymakers as a necessary tool to prevent the terrorist attacks in the future. This policy decision led to the military grade weapons and military style training regime for the police force. Some of the states in the US partnered with highly militarized police of Israel for training their police force. Such lethal weapons which were provided to the police force  used against terrorists were gradually used by the police force against common civilians on suspicion of minor crimes and the group of protestors.

The civil right groups were voicing concerns for many years about the use of disproportionate force on the Black and Hispanic Americans, which they blamed on the arming of police with lethal weapons. It was the death of Michael Brown, an unarmed black teenager, who was shot and killed on Aug. 9, 2014, by Darren Wilson, a white police officer, in Ferguson, Montana, USA that galvanised the public to demand for demilitarization of police force. As a response to public anger against the killing of Michael Brown, President Obama set up a Task Force on 21st Century Policing. This task force, in its report put special emphasis on de-escalating situations, with civilians in training and policies, and reduced funding by the Department of Homeland Security for such weapons. However these recommendations failed to have much effect on solving the issues at hand.

The continued use of such lethal weapons casts the police force as a separate, powerful entity which is to be feared, instead of a friendly cop who is trying to provide security to a citizen in distress. Such equipment serves to distance the police from the people, giving them power, and if left unchecked, entitlement over the rest of the citizens. In many instances the presence of a weapon itself leads to more aggressive behaviour and there have been calls to make the police wear body cams to restrain them from acting with disproportionate lethal force.

The racial profiling and discriminatory actions against the black and other communities that was already practiced by the police forces was now being enforced by more lethal power in the force’s hands. A study by Olugbenga Ajilore shows that counties with more race segregation were more likely to request additional weapons, and counties with an African American/Asian American population are more likely to acquire military equipment. Another report of 2017 shows a direct correlation between the degree of police militarization and the killing of civilians in police action.

It can be reasonably said that the militarization, in some sense, inflated the already existing racial profiling based violent actions of police force.

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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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