Sunday, August 2, 2020

Yemen's Multilayered Civil War: A Brief History

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Anant Jani

Article Title

Yemen's Multilayered Civil War: A Brief History

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Global Views 360

Publication Date

August 2, 2020

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Children in Yemen

Children in Yemen | Source: Rod Waddington via Flickr

This is the 1st part of a short explainer article series on the current crisis in Yemen.

Since 2015, Yemen has been at war on two different fronts, 1) The Civil War between the Iran-backed Houthi rebels and the UAE-Saudi Arabia backed government headed by Abdrabbuh Mansur Hadi, and 2) the war against the local terrorist outfits of Al-Qaeda and ISIS.

However, last year one more complexity was added to the conflict when UAE withdrew from the coalition backing Hadi government and later threw its support behind another secessionist force in southern Yemen, which seeks to re-create the State of South Yemen, as it was before the unification of Yemen in 1990.

As of early this year, it has added another layer to the war: the failing healthcare infrastructure and the rise of COVID-19.

The staggering cost of this war in the past five years has prompted the UN to name it the worst man-made humanitarian crisis in history, with Some 24 million Yemeni people - 80 percent of the country's population - requiring assistance or protection.

This series of articles seeks to build historical context to follow the current events in Yemen, believing much of the recent media coverage to have been ignored, or otherwise made wholly uncontextualized in the process of following the crisis for over a decade.

Yemen and the greater neighbourhood | Source: Google Map

The History

Much of the current conflict can only be understood as a result of the events of the latter half of the 20th century. Here is a brief look at the history that has shaped today’s wars in Yemen.

At the heart of several issues in the conflict is the fact that modern day Yemen was initially divided into North Yemen and South Yemen until 1990, when it was unified.

Yemen and the greater neighbourhood | Source: Wikimedia

North Yemen:

The Yemen Arab Republic (YAR), a coalition in North Yemen, overthrew the Mutawakilite Kingdom in 1970, which had been ruling since Yemen’s decolonization, in 1918. The YAR established their capital at Sana’a, a site which will often be the site of conflict in the following years.
This part of Yemen, during the cold war  was backed the countries aligned with the anti-communist block like Saudi Arabia, Jordan, the US, the UK and West Germany. The influence of Saudi Arabia and their relations with the US will come to play a greater role in the following decades.

South Yemen:

This referred to the region that was under the British Raj as the Aden Protectorate, since 1874. It consisted of two-thirds of present-day Yemen. In 1937 it became a Province of the British Raj, and in 1963, it collapsed and an emergency declared. The collapse was the joint effort of the National Liberation Front (NLF) and the Front for the Liberation of Occupied South Yemen (FLOSY).

Aden was used by the East India Company as a coal depot, and to stop Arab pirates from harassing British-India trade. Until 1937, Aden was part of British India, officially titled the Aden Protectorate.

Aden, like Sana’a will come to be the capital of southern Yemen, and the site of many conflicts.

This part of Yemen, during the cold war was backed by the Cummunist bloc countries like USSR, Cuba, and East Germany.

The Unification:

North and South Yemen united in 1990, after several years of conflict with one another. The leader of North Yemen, Ali Abdullah Saleh, was named President of unified Yemen in 1990. He was to continue ruling over Yemen for over three decades.

The unification of Yemen finally fulfilled almost a century of struggle that started during the British occupation and continued at different paces throughout the monarchy and cold war period. This unification also took away the privileges and power vested with many important tribes and people. Unlike the political forces, the armed forces of North and South Yemen were not unified at the time of political unification of the country.

The disgruntled former elites and the partisan army provided the fertile ground for the first civil war of Yemen which followed shortly after the unification.

Link to the second part.

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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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