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Wednesday, August 12, 2020

Expat Exodus In The Middle East

This article is by
Vanshita Banuana
Vanshita Banuana
MENA

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Vanshita Banuana

Article Title

Expat Exodus In The Middle East

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Global Views 360

Publication Date

August 12, 2020

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A photograph of Dubai

A photograph of Dubai | Source: Fredrik Öhlander via Unsplash

The COVID-19 pandemic has hit people and economies worldwide, sparking a global recession and financially destabilising millions of people. In the Middle East, dipping oil prices have only worsened the threat to the economy. Businesses are shutting down, and many are trying to survive by cutting the salaries or laying off of workers. Large segments of the workers in these countries are expatriates, and many have struggled to make ends meet as unemployment soared.

The development of the Gulf countries has always been intertwined with their large expat populations. These workers are often vital to the economy, not just as part of the workforce but also as consumers by enabling successful malls, restaurants and other forms of recreation and tourism. Countries like Saudi Arabia gain valuable non-oil revenue in the form of increased Value Added Taxes (VAT) and by imposing a monthly fee on migrants who want to sponsor family members.

Many of these workers are from developing Southeast Asian countries such as India and Pakistan, and contribute greatly to their home country’s economy in the form of remittances, i.e sending money back home. Those who are facing unemployment or salary cuts are eager to be repatriated, especially since in many Gulf countries visas, rent, and even phone lines are linked to jobs, and expats have little to no social safety nets to fall back on.

“Panicked” Indians applying to go back home crashed the Dubai aviation ministry’s website for applications in the process. The consulate says it has received around 200,000 applications for repatriation of expats from as many as 12 countries.

For some, closing businesses are forcing them to go home. For others, the cost of education is the major concern. The Emirates group, Uber’s Middle Eastern counterpart Careem, and hotels are some of the few major employers considering laying off large portions of their staff or reducing salaries.

Dubai has been one of the hardest hit, as expats form an estimated 92% of the population. Dubai based movers estimate that they’re getting up to seven calls a day to ship belongings abroad. It is extremely hard to gain permanent resident status in countries such as the UAE, and the costs of living and education are quite high and often provided by employers, which has made leaving the only option left for many laid-off workers across all fields.

The UAE has tried to offset the damage by granting automatic extensions to expiring work permits, waiving of work permit fees and fines, and providing interest-free loans and repayment breaks.

Meanwhile, governments in Kuwait and Oman are trying to mould the exodus into an opportunity to boost local employment. On the other hand, the Saudi Arabian government has been criticised for not taking enough measures to protect the local workforce.

While the Gulf countries have been trying to decrease their dependence on oil wealth and foreign workforce, it is not something that can be accomplished soon, especially given the great dependence of the Gulf economies on both those factors.

There is still too unavoidable a gap between the current skill of local workers and the training needed to compete with foreign professionals, making it hard to simply employ domestic workers in place of foreign ones. The pandemic, however, might not leave much of a choice.

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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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