1. A judicial pushback to a draconian legal regime

The Delhi court ruling is a way forward in finding a balance between civil rights and the imperatives of anti-terror laws

The judgment of the Delhi High Court granting bail to activists Devangana Kalita, Natasha Narwal, and Asif Iqbal Tanha — they have been in jail for over a year (without trial) for their alleged role in the 2020 Delhi riots — is significant for many reasons. Most importantly, it brings to a close many months of jail time for three people who are yet to be proven guilty of any crime, something that should be anathema to any civilised justice system. What is also significant, however, is that the judgment represents an important judicial pushback to the authoritarian legal regime under the Unlawful Activities (Prevention) Act (“UAPA”).

The root of the issue

Ostensibly designed to check and address terrorism, the UAPA is perhaps one of the most abused laws in India today. The root of the problem lies in Section 43(D)(5) of this Act, which prevents the release of any accused person on bail if, on a perusal of the case diary, or the report made under Section 173 of the Code Of Criminal Procedure, the court is of the opinion that “there are reasonable grounds for believing that the accusation against such person is prima facie true”.

It is important to break this down. Broadly speaking, India follows the adversarial system of criminal justice, where two sides to a dispute attempt to persuade the court that their version of events is true. At the heart of the adversarial system of justice is the testing of evidence through cross-examination. Each side is afforded the opportunity to scrutinise, challenge, and question the evidence produced by its opponent; and the best way for a judge to unearth the truth — or the closest approximation of it — is to consider which side’s evidence is left standing, and appears more persuasive, after the rigours of cross-examination.

Production of evidence, and cross-examination, involves witnesses, recoveries of incriminating objects, tests of handwriting or voice samples, and many other elements. It constitutes the bulk of a criminal trial. In India, with our overburdened courts and creaking justice system, criminal trials take years. In high-profile cases such as the Delhi riots case, where the record is bulky, and the witnesses number in their hundreds, trials can take many years — even a decade or more.

Importance of bail

For this reason, bail becomes of utmost importance. If an individual is not able to secure bail from the courts, they will languish as under-trials in prison, for the duration of the case, no matter how many years it takes (in recent memory, there are cases of people being found innocent in terrorism cases after 14 and even 23 years in prison). Bail, thus, becomes the only safeguard and guarantee of the constitutional right to liberty

In ordinary circumstances, when considering the question of bail, a court is meant to take into account a range of factors. These include whether the accused is a flight risk, whether he or she might tamper with the evidence or attempt to influence witnesses, and the gravity of the offence. But it is here that Section 43(D)(5) of the UAPA plays such a damaging role. As we have just seen, under the classical vision of criminal justice, truth — about innocence or guilt — can only be determined after the evidence of both the prosecution and the defence has been subjected to the rigours of cross-examination. However, as lawyers and scholars such as Abhinav Sekhri and Anjana Prakash have also pointed out, Section 43(D)(5) short-circuits that core assumption. For the grant of bail, it only looks at the plausibility of one side’s evidence — that is, the Prosecution’s. It binds the court to look at only the case diary or the police report, which has not been challenged by cross-examination, and requires that bail be denied as long as the unchallenged prosecution case appears to be prima facie true.

One-sided

The perversity of Section 43(D)(5), thus, is that it forces the court to make an effective determination of guilt or innocence based on one side’s unchallenged story, and on that basis to deprive individuals of their freedom for years on end. In a democratic polity, which is committed to the rule of law, this is a deeply troubling state of affairs.

The effect of Section 43(D)(5), as one can see, is that once the police elect to charge sheet an individual under the UAPA, it becomes extremely difficult for bail to be granted. Even outlandish or trumped-up cases can sound convincing until people have a chance to interrogate and challenge them. In short, unless the police prepare an extremely shoddy case — that is riddled with internal contradictions, for example — a case diary or a report will invariably make out a “prima facie” case against an individual.

Finer points of the judgment

It is here that the Delhi High Court’s judgment becomes important. The Bench of Justices Siddharth Mridul and Anup Jairam Bhambani correctly note that even though Section 43(D)(5) departs from many basic principles of criminal justice, there are other fundamental principles that remain of cardinal significance. These include, for example, that the initial burden of demonstrating guilt must always lie upon the prosecution; and also, that criminal offences must be specific in their terms, and read narrowly, to avoid bringing the innocent within their net. On this basis, the court’s judgment notes that as the UAPA is meant to deal with terrorist offences, its application must be limited to acts that can reasonably fall within a plausible understanding of “terrorism”. “Terrorism” is a term of art, and not a word that can be thrown around loosely. Thus, to attract the provisions of the UAPA — the judgment holds — the charge sheet must reveal factual, individualised, and particular allegations linking the accused to a terrorist act.

The judgment then finds that even if the police’s claims are taken to be true, no such allegations exist. At the highest, the accusations against the activists involve calls for protests and chakka jams (road blockages). There is no act, overt or covert, attributed to the activists that could constitute a terrorist offence. And, importantly, inferences or hypotheticals drawn by the police do not count at the stage of granting bail. Coupled with the significance of the right to protest and to dissent under our constitutional scheme, the judgment therefore holds that even prima facie, a case under the UAPA has not been made out, and therefore, there is no question of the application of Section 43(D)(5).

The Delhi High Court’s judgment indicates a pathway forward in the quest for finding a balance between citizens’ civil rights and the imperatives of anti-terrorism legislation such as the UAPA. A position under which citizens can be jailed for years on end just on the basis of police reports and case diaries, with courts precluded from granting them bail, is completely inconsistent with democracy, and redolent of authoritarian or tyrannical states. However, the court’s analysis shows how even within — and consistent with — the terms of the UAPA, there is an important role for a conscientious judiciary to play. By scrutinising the police case on its own terms, and according a strict interpretation to draconian legislation such as the UAPA, courts can ensure that civil rights are not left entirely at the mercy of the state.

At the time of writing, the High Court’s judgment has been appealed by the Delhi Police to the Supreme Court of India. It now remains to be seen whether the highest court will also endorse this crucial ruling, which restates the responsibility of an independent judiciary in checking executive impunity.

Background:

Delivering a judgment defining the contours of the otherwise “vague” Section 15 of the Unlawful Activities (Prevention) Act, 1967, (UAPA) a division bench of the Delhi High Court has laid down some important principles upon the imposition of Section 15, 17 & 18 of the Act.

What’s the case?

The  issue came up while granting bail to Delhi-riots accused who faced charges for being part of a “larger conspiracy” during the anti-Citizenship (Amendment) Act, 2019 protests which erupted into violence resulting in deaths across North-East Delhi.

Sections 15, 17 and 18 of UAPA:

1. S. 15 engrafts the offence of ‘terrorist act’.
2. S. 17 lays-down the punishment for raising funds for committing a terrorist act.
3. S. 18 engrafts the offence of ‘punishment for conspiracy etc. to commit a terrorist act or any act preparatory to commit a terrorist act’.

2. A place for disruptive technology in India’s health sector

Artificial intelligence, autonomous systems and data analytics have a defining role to play in shaping the medical sector

As frontline warriors fighting COVID-19, the medical community has been selfless, but also losing a number of staff in the process. Nurses and attendants, on full-time duty, donning mainly masks and gloves as the only protective gear have been exposed to great risk. It is in such a situation that the relevance of disruptive technology and its applications comes into focus, potentially helping to reduce the chances of hospital staff contracting the infection.

There are reports in the global media of established innovative field hospitals using robots to care for COVID-19 affected patients. There are hospitals, in China, that use 5G-powered temperature measurement devices at the entrance to flag patients who have fever/fever-like symptoms. Other robots measure heart rates and blood oxygen levels through smart bracelets and rings that patients wear; they even sanitise wards. Last year, in India, the Sawai Man Singh government hospital in Jaipur held trials with a humanoid robot to deliver medicines and food to COVID-19 patients admitted there.

The critical aspect is how new technologies can improve the welfare of societies and reduce the impact of communicable diseases, spotlighting the importance of technologies such as artificial intelligence (AI), autonomous systems, blockchain, cloud and quantum computing, data analytics, 5G. Blockchain technology can help in addressing the interoperability challenges that health information and technology systems face. The health blockchain would contain a complete indexed history of all medical data, including formal medical records and health data from mobile applications and wearable sensors. This can also be stored in a secure network and authenticated, besides helping in seamless medical attention.

Big data analytics can help improve patient-based services tremendously such as early disease detection. Even hospital health-care facilities can be improved to a great extent. AI and the Internet of Medical Things, or IoMT (which is defined as a connected infrastructure of medical devices, software applications, and health systems and services) are shaping health-care applications.

Medical autonomous systems can also improve health delivery to a great extent and their applications are focused on supporting medical care delivery in dispersed and complex environments with the help of futuristic technologies. This system may also include autonomous critical care system, autonomous intubation, autonomous cricothyrotomy and other autonomous interventional procedures. Cloud computing is another application facilitating collaboration and data exchanges between doctors, departments, and even institutions and medical providers to enable best treatment.

Furthering UHC

According to the World Health Organization (https://bit.ly/3gtHBtT), “Universal health coverage (UHC) is the single most powerful concept that public health has to offer. It is a powerful social equalizer and the ultimate expression of fairness.” The question is about how UHC can be achieved through the application of digital technologies, led by a robust strategy integrating human, financial, organisational and technological resources. Studies by WHO show that weakly-coordinated steps may lead to stand-alone information and communication technology solutions, leading to a fragmentation of information and resulting in poor delivery of care. India needs to own its digital health strategy that works and leads towards universal health coverage and person-centred care. Such a strategy should emphasise the ethical appropriateness of digital technologies, cross the digital divide, and ensure inclusion across the economy. ‘Ayushman Bharat’ and tools such as Information and Communication Technology could be be fine-tuned with this strategy to promote ways to protect populations. Online consultation through video conferencing should be a key part of such a strategy, especially in times when there is transmission of communicable diseases.

Using local knowledge

In addition to effective national policies and robust health systems, an effective national response must also draw upon local knowledge. Community nurses, doctors, and health workers in developing countries do act as frontline sentinels. An example is the Ebola virus outbreak in Africa, where communities proactively helped curtail the spread much before government health teams arrived. Another example is from Indonesia, where the experience of backyard poultry farmers was used to tackle bird flu. Primary health centres in India could examine local/traditional knowledge and experience and then use it along with modern technology.

Possible challenges

In the developing world, and this includes India, initial efforts in this direction should involve synchronisation and integration, developing a template for sharing data, and reengineering many of the institutional and structural arrangements in the medical sector. Big data applications in the health sector should help hospitals provide the best facilities and at less cost, provide a level playing field for all sectors, and foster competition. The possible constraints in this effort are a standardisation of health data, organisational silos, data security and data privacy, and also high investments. However, there is no doubt that disruptive technology can play an important role in improving the health sector in general.

Surjith Karthikeyan is an Indian Economic Service Officer, serving as Deputy Secretary in the Ministry of Finance. Gowtham Daas Rajendran is a post graduate in public policy from the Lee Kuan Yew School of Public Policy. The views expressed are personal

What is Artificial Intelligence (AI)?

• It is a branch of computer science that deals with creating computers or machines as intelligent as human beings.
• It refers to the ability of the machines to perform human intelligence processes like thinking, perceiving, learning, problem-solving and decision making.
• Thus in simple terms, Artificial Intelligence is the intelligence showed by machines.
• The term Artificial Intelligence was coined by John McCarthy in 1956 at the Dartmouth conference, Massachusetts Institute of Technology (MIT).
• There are two subsets under the Umbrella term AI, they are – Machine Learning and Deep Learning.

What is the difference between Machine Learning and Deep Learning?

Machine Learning:

• A subset of artificial intelligence that deals with the creation of algorithms that can modify itself without human intervention to generate desired output- by feeding itself via structured data.
• Machine learning algorithms are built to “learn” to do things by understanding labeled data, then use it to produce further outputs with more sets of data. However, they need to be retrained through human intervention when the actual output isn’t the desired one (errors).

Deep Learning:

• A subset of machine learning where algorithms are created and function similar to those in machine learning, however, there are different layers of these algorithms- each providing a different interpretation to the data it feeds on.
• Such a network of algorithms is known as artificial neural networks, as it imitates the function of the human neural networks present in the brain.
• Deep learning networks do not need human intervention as the nested layers in the neural networks put data via hierarchies of different concepts, which eventually learn from their own errors. But even these are subject to flawed outputs if the quality of data is not good enough.

To put it simply, the key difference between deep learning and machine learning stems from the way data is presented to the system. Machine learning algorithms almost always require structured data, whereas deep learning networks rely on layers of the ANN (artificial neural networks). Thus Data is the governor here. It is the quality of data which ultimately determines the quality of the result.

What are some of the examples of Artificial Intelligent Technologies?

• Robotics and Automation: Robots can be programmed to perform high-volume, repeatable tasks normally performed by humans.
• Natural Language Processing (NLP) is the processing of human language by a computer program. For example, spam detectors look at the subject line and text of an email in order to decide whether it is junk.
• Pattern recognition is a subset of machine learning that seeks to identify patterns in data. For example, a machine learning program can differentiate cats from dogs among 1000 images of cats and dogs through pattern recognition like face, whiskers, etc.
• Machine vision is the science of giving computers a vision by capturing and analyzing visual information using a camera, analog-to-digital conversion, and digital signal processing. It is mostly compared to human eyesight, however, machine vision is not constrained by biology = it can even be programmed to see through walls.

What are the applications/advantages of Artificial Intelligence (AI)?

Self-driving Cars:  AI algorithms are one of the primary components that facilitate self-driving cars to make sense of their surroundings, taking in feeds from cameras installed around the vehicle and detecting objects like roads, traffic signs, other cars, and people.

Digital assistants and smart speakers: Siri, Alexa, Cortana, and Google Assistant utilise artificial intelligence to convert spoken words to text and map the text to certain commands. AI assists digital assistants to make sense of various nuances in spoken language and synthesize human-like voices.

Translation: For several decades, translating text between various languages was a pain point for computers. But deep learning created a revolution in services such as Google Translate. But to be precise, AI still has a long way to go before it perfects human language, but so far, the advances are outstanding.

Facial recognition: Facial recognition is one of the most prominent applications of artificial intelligence. It has different uses, including unlocking your phone, paying with your face, and detecting intruders in your home.

Medicine:

• In the medical field also, we will find the wide application of AI. Doctors assess the patients and their health risks with the help of artificial machine intelligence. It educates them about the side effects of various medicines.
• Medical professionals are often trained with artificial surgery simulators. It finds a huge application in detecting and monitoring neurological disorders as it can simulate the brain functions.
• Robotics is often used in helping mental health patients to come out of depression and remain active.
• A popular application of artificial intelligence is radiosurgery. Radiosurgery is used in operating tumours and this can actually help in the operation without damaging the surrounding tissues.

Agriculture Sector: AI can be utilised to predict advisories for sowing, pest control, input control = enable increased income and giving stability for the agricultural community . Image classification tools in addition to remote and locally sensed data can bring a revolutionary change in – utilisation and efficiency of farm machinery, weed removal, early disease identification, harvesting, and grading.

Business Sector:

• In order to take care of highly repetitive tasks – robotic automation is applied which perform faster, effortlessly and tirelessly than humans.
• Moreover, Machine learning algorithms are being integrated into analytics and CRM (Customer Relationship Management) platforms to provide better customer service. Chatbots being used in the websites to provide instant service to customers.
• Automation of job positions has also become a discussion point among academics and IT consultancies like Gartner and Forrester.

Education Sector:

• Artificial Intelligence can make certain educational processes automated like grading, rewarding marks, etc. thus giving educators more time.
• Furthermore, it can analyse students and adapt to their requirements so as to help them work at their own pace.
• AI can change where and how students learn, perhaps even replacing a few teachers.

Financial Sector:

• AI is applied to personal finance applications and could compile personal data and give financial advice. In fact, nowadays software trades more than humans in Wall Street.
• Detection of financial fraud uses artificial intelligence in a smart card-based system.

Legal Sector: Automation can result in a faster resolution of pending cases by minimising the time taken while analyzing cases = better use of time and more efficient legal & judicial processes.

Manufacturing sector: Robots are being utilised for manufacturing since a long time now but more advanced exponential technologies have emerged like additive manufacturing (3D Printing) which with the support of AI can revolutionize the whole manufacturing supply chain ecosystem.

Intelligent Robots: Robots can do the tasks given by a human with the help of sensors to detect physical data from the real world like light, heat, temperature, movement, sound, bump, and pressure. Furthermore, they have effective processors, multiple sensors and enormous memory, to showcase intelligence. Also, they can learn from their errors and hence can adapt to the new environment.

Gaming: AI has a significant role in strategic games like chess, poker, tic-tac-toe, etc., where the machine can think of a huge number of possible positions according to heuristic rule (A set of rules intended to increase the probability of solving some problem).

Cyber Security: In the 20th conference on e-governance in India it was discussed that AI has the capability to strengthen cybersecurity ecosystem in India and should be explored further.

Smart Cities and Infrastructure: AI is used to monitor patronage and accordingly control associated systems such as pavement lighting, park maintenance, and other operational conditions = lead to cost savings + improving safety and accessibility.

Space sector: Intelligent robots are fed with information and are sent to explore space. Since they are machines with metal bodies, they are more resistant and have a higher ability to endure the space and hostile atmosphere. Because they are created in such a way that they cannot be modified or get disfigured or breakdown in a hostile environment.

Mining sector: Artificial intelligence and the science of robotics can be put to use in mining and other fuel exploration processes. Not only that, these complex machines can be used for exploring the ocean floor and hence overcome the human limitations.

Defence Sector: Artificial Intelligence (AI) based tools would aid the defence forces constructively in areas such as decision support, sensor data analysis, predictive maintenance, situational awareness, accurate data extraction, security, etc.  These tools will assist defence personnel in better operations, maintenance, and logistics support.

3. Recovery takes more than reforms

In any serious attempt at economic recovery, the focus must be on food supply and not money supply

The most recent growth estimates of the National Statistical Office show that after a steep contraction in the first quarter of last year, growth accelerated steadily afterwards. This would have assured a recovery had we not experienced the second wave of the pandemic that came with the current financial year. Overlapping State-level lockdowns that started in April have now lasted for almost as long the nationwide lockdown of 2020, and there is no gainsaying their impact on the economy. Output may well have contracted in the beginning of this year. So, though recovery will eventually come, it could be W-shaped rather than V-shaped.

Meaning of reforms

When the issue of economic recovery was raised in public, a minister asserted that the economy will recover due to the reforms planned or already implemented by the government. We do not know what the government has in mind but we should be sceptical of the claim that reforms can make a difference at this stage. Since 1991, the term ‘reforms’ has been used to mean both policy changes that remove restrictions on private sector activity in certain areas and those that increase profits in existing lines of production. Recent examples of these are allowing greater private sector participation in defence as part of the Atmanirbhar Bharat Abhiyaan launched in 2020 and the significant lowering of corporate tax in 2019, respectively. However, more reforms may be ineffective in spurring recovery. Presently for the private sector, entry into a new area or undertaking investment in an existing activity may not appear profitable given their expectation of the state of the economy in the near future, upon which their revenue will depend.

We may assume that the private sector is fully aware of the following history. In February, believing that the peak of the epidemic had been crossed, the government reverted to its principal macroeconomic pre-occupation, namely fiscal consolidation or the paring down of the fiscal deficit. Accordingly, it raised its budgeted expenditure by less than 1% in the last Budget. The onset of the second wave of COVID-19 in April has thrown the economic policy calculations of the government out of gear. Back in February it was already known that the economy had contracted in 2020-21. To keep public expenditure virtually unchanged under such circumstances had been heroic enough then, but now, with a possible further contraction of the economy, to continue with the frigid fiscal stance would be disastrous. Though we do not know how output has fared so far this financial year, data from the Centre for Monitoring Indian Economy show that unemployment has risen in May, indicating slack demand for output. With this knowledge, the private sector is unlikely to respond with alacrity to liberalising reforms.

Public spending is the key

Right now, raising public spending is the only game in town left to the policymaker serious about bringing on a recovery. If we are to have it, though, we should accept a higher than budgeted deficit. A debate involving economists and central bankers has been set off on whether the government should now ‘print money’. This is the wrong way to approach the problem. It puts the cart before the horse. It is also alarmist. The objective is to revive the economy, public spending is the instrument and the funding must be found. It need not involve money creation. India’s public debt is low by comparison with the OECD countries, and debt financing remains an option. Even if money financing is adopted, it need not cause accelerating inflation as some predict. Experience in India suggests otherwise. However, studies do show that any economic expansion would be inflationary if the production of food does not respond adequately. How the expansion is financed is less relevant for inflation at least in the near term. In any serious attempt at economic recovery, the focus must be on the food supply and not the money supply.

Atmanirbhar Bharat which translates to ‘self-reliant India’, is a Hindi phrase used and popularized by the Prime Minister of India Narendra Modi and the Government of India in relation to economic development in the country during and after the COVID-19 pandemic.

4.Birth, death registrations up in 2019

14 States/Union Territories achieved 100% level of birth registrations: report

The level of registration of births and deaths in the country improved in 2019, according to the “Vital Statistics of India Based on The Civil Registration System” report.

Some States and Union Territories were, however, lagging behind.

The report states that the level of birth registration increased from 87.8% in 2018 to 92.7% in 2019; and death registrations went up from 84.6% to 92% during the period.

While 14 States/Union Territories achieved 100% level of birth registrations, 19 States/Union Territories achieved the same level in cases of death.

Sex ratio at birth

Based on the information received from 32 States/Union Territories, the share of institutional births in the total registered births was 81.2%. The number of registered births increased to 2.48 crore in 2019 from 2.33 crore in 2018. The share of male and female was 52.1% and 47.9%.

In the case of registration of births within the prescribed period of 21 days, 15 States/Union Territories achieved more than 90% registration.

The highest sex ratio at birth (SRB) based on registered events was reported by Arunachal Pradesh (1,024), followed by Nagaland (1,001) Mizoram (975) and Andaman & Nicobar Islands (965). The lowest SRB was reported by Gujarat (901), Assam (903) and Madhya Pradesh (905), followed by Jammu & Kashmir (909).

The number of registered deaths increased from 69.5 lakh in 2018 to 76.4 lakh in 2019. The share of male and female was 59.6% and 40.4%.

Based on the information received from 31 States/Union Territories, the share of institutional deaths in total registered deaths was 32.1%.

Infant deaths

Eleven States/Union Territories achieved more than 90% registration of deaths within the prescribed period of 21 days.

In the case of registration of infant deaths, the share of urban area was 75.5% compared to 24.5% in rural areas.

In the north-east, Arunachal Pradesh reported 100% registration of births, but only 38.6% of deaths. Nagaland also registered 100% births, but just 30% deaths, while Manipur recorded 67.7% births and only 21.4% deaths. In Sikkim, there was 100% registration of deaths, but 61.2% registration of births.

Full registration

However, Mizoram and Tripura reported 100% registration of both births and deaths. Meghalaya had 100% registration of births and 97.6% registration of deaths, while Assam reported 100% registration of births and 74% registration of deaths.

In Bihar and Jharkhand, the levels of registration of births were 89.3% and 84.3% and the levels of registration of deaths were 51.6% and 58.8%. In Daman & Diu, the figures were 50.7% and 61%.

Listing the limitations, the report said the level of registration of States/Union Territories and India level presented in the report was arrived at using the mid-year projected population of the respective States/Union Territories of 2011-2019 based on 2011 census (Report of the Technical Group on Population Projections, July 2020, National Commission on Population, Ministry of Health & Family Welfare) and, therefore, was not comparable with rates presented in previous reports.

Incomplete data

The level of registration was arrived at using Sample Registration System Rates for 2018 as the survey for 2019, which was scheduled for 2020, could not be completed due to the COVID-19 pandemic. Besides, some States/Union Territories submitted incomplete or partial data, which was not included.