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Wednesday, August 5, 2020

Yemen's Multilayered War: Al Qaeda in Arab Peninsula

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Anant Jani
Anant Jani
MENA

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Anant Jani

Article Title

Yemen's Multilayered War: Al Qaeda in Arab Peninsula

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Global Views 360

Publication Date

August 5, 2020

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Sailors render honors at the USS Cole Memorial

Sailors render honors at the USS Cole Memorial | Source: Flickr

This is the 4th part of a short explainer article series on the current crisis in Yemen. To read the earlier parts of the series click on the following links.

To read the 1st part of the series click on the link.

To read the 2nd part of the series click on the link.

To read the 3rd part of the series click on the link.

The unification of Yemen in 1990 was a direct result of the military defeat of South Yemen at the hand of North Yemen forces. This military defeat and coerced unification implied that Unified Yemen could not achieve real cohesion, preventing the functioning of the nation as a democratic unit.

Meanwhile, newer elements were added to the dangerous mix of sub-nationalism, intra religious division, and tribal loyalty in Yemen. These were the Yemeni veterans of Soviet-Afghan war who fought with the Afghan mujahideen against the Soviet army backing the Afghan government.

These were hardline Wahabi and Salafi fighters, following an idealogy that mandated a strict interpretation of Islam. The fighters returned to Yemen in the early 1990s, after the withdrawal of Soviet forces from Afghanistan. The local Yemeni, both the Zaidi Shias or Maliki Sunni have traditionally followed a more liberal version of Islamic and social practices. Unlike the local Sunnis who were living in peaceful coexistence with the Zaidis Shia, these hardliners were antagonistic to the Shias.

Their arrival was followed by a forceful realignment of the local residents’ religious practices, mandating the local population to strict interpretations and social practices. Osama bin Laden, who had family roots in Yemen, was a conveniently placed ideological mentor. This led to a pushback from both the government forces as well as Shia groups, especially the Houthi-led Ansar Allah movement. In time, these former mujahideen, who were battle hardened and well versed in guerilla warfare, allied themselves with Al-Qaeda to start a low level insurgency in Yemen.

The Gulf war and subsequent stationing of American forces in Saudi Arabia and other gulf countries provided another impetus for the growth of Al Qaeda in Yemen. Consequently, they demanded that coalition forces leave Arabian land, failing which would result in more terror attacks.

Al-Qaeda affiliated groups attacked many installations associated with the US-led coalition forces in Yemen and nearby countries. The most successful of those was the famous bombing of USS Cole in Aden, in 2000. It was followed by a series of attacks leading up to  9/11.

Al-Qaeda in the Arab Peninsula (AQAP) is also known as the Ansar al-Sharia in Yemen is fighting to set up an emirate amidst the lack of leadership post the Houthi rebellion. It was this outfit that claimed responsibility for the attack on the French satirical magazine, Charlie Hebdo, in 2015 and is now considered the most dangerous al-Qaeda outfit by the US.

The CNN reported that “AQAP set out its objectives in a May 2010 statement as the "expulsion of Jews and crusaders" from the Arabian Peninsula, the re-establishment of the Islamic caliphate, the introduction of Sharia, or Islamic law, and the liberation of Muslim lands.”

The full list of attacks and places captured by terrorist insurgents in chronological order can be accessed here.

One of the outcomes of continual terrorist attacks has been a reduction in Hadi’s popularity. He is also seen as weak for not being able to stop al-Qaeda from terrorising Southern Yemen, as well as for not being able to alleviate them from their feeling of marginalization ever since the unification.

To read the 5th part of the series click on the link.

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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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