Personal Data Sharing & Protection: Strategic relevance from India’s context
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India’s Investments in the digital financial infrastructure—known as “India Stack”—have sped up the large-scale digitization of people’s financial lives. As more and more people begin to conduct transactions online, questions have emerged about how to provide millions of customers adequate data protection and privacy while allowing their data to flow throughout the financial system. Data-sharing among financial services providers (FSPs) can enable providers to more efficiently offer a wider range of financial products better tailored to the needs of customers, including low-income customers.
However, it is important to ensure customers understand and consent to how their data are being used. India’s solution to this challenge is account aggregators (AAs). The Reserve Bank of India (RBI) created AAs in 2018 to simplify the consent process for customers. In most open banking regimes, financial information providers (FIPs) and financial information users (FIUs) directly exchange data. This direct model of data exchange—such as between a bank and a credit bureau—offers customers limited control and visibility into what data are being shared and to what end. AAs have been designed to sit between FIPs and FIUs to facilitate data exchange more transparently. Despite their name, AAs are barred from seeing, storing, analyzing, or using customer data. As trusted, impartial intermediaries, they simply manage consent and serve as the pipes through which data flow among FSPs. When a customer gives consent to a provider via the AA, the AA fetches the relevant information from the customer’s financial accounts and sends it via secure channels to the requesting institution. implementation of its policies for consensual data-sharing, including the establishment and operation of AAs. It provides a set of guiding design principles, outlines the technical format of data requests, and specifies the parameters governing the terms of use of requested data. It also specifies how to log consent and data flows.
There are several operational and coordination challenges across these three types of entities: FIPs, FIUs, and AAs. There are also questions around the data-sharing business model of AAs. Since AAs are additional players, they generate costs that must be offset by efficiency gains in the system to mitigate overall cost increases to customers. It remains an open question whether AAs will advance financial inclusion, how they will navigate issues around digital literacy and smartphone access, how the limits of a consent-based model of data protection and privacy play out, what capacity issues will be encountered among regulators and providers, and whether a competitive market of AAs will emerge given that regulations and interoperability arrangements largely define the business.
Account Aggregators (AA’s):
ACCOUNT AGGREGATORS (AAs) is one of the new categories of non banking financial companies (NBFCs) to figure into India Stack—India’s interconnected set of public and nonprofit infrastructure that supports financial services. India Stack has scaled considerably since its creation in 2009, marked by rapid digitization and parallel growth in mobile networks, reliable data connectivity, falling data costs, and continuously increasing smartphone use. Consequently, the creation, storage, use, and analyses of personal data have become increasingly relevant. Following an “open banking “approach, the Reserve Bank of India (RBI) licensed seven AAs in 2018 to address emerging questions around how data can be most effectively leveraged to benefit individuals while ensuring appropriate data protection and privacy, with consent being a key element in this. RBI created AAs to address the challenges posed by the proliferation of data by enabling data-sharing among financial institutions with customer consent. The intent is to provide a method through which customers can consent (or not) to a financial services provider accessing their personal data held by other entities. Providers are interested in these data, in part, because information shared by customers, such as bank statements, will allow providers to better understand customer risk profiles. The hypothesis is that consent-based data-sharing will help poorer customers qualify for a wider range of financial products—and receive financial products better tailored to their needs.
Data Sharing Model : The new perspective:
Paper based data collection is inconvenient , time consuming and costly for customers and providers. Where models for digital-sharing exist, they typically involve transferring data through intermediaries that are not always secure or through specialized agencies that offer little protection for customers. India’s consent-based data-sharing model provides a digital framework that enables individuals to give and withdraw consent on how and how much of their personal data are shared via secure and standardized channels. India’s guiding principles for sharing data with user consent—not only in the financial sector— are outlined in the National Data Sharing and Accessibility Policy (2012) and the Policy for Open Application Programming Interfaces for the Government of India. The Information Technology Act (2000) requires any entity that shares sensitive personal data to obtain consent from the user before the information is shared. The forthcoming Personal Data Protection Bill makes it illegal for institutions to share personal data without consent.
India’s Ministry of Electronics and Information Technology (MeitY) has issued an Electronic Consent Framework to define a comprehensive mechanism to implement policies for consensual data-sharing. It provides a set of guiding design principles, outlines the technical format of the data request, and specifies the parameters governing the terms of use of the data requested. It also specifies how to log both consent and data flows. This “consent artifact” was adopted by RBI, SEBI, IRDAI, and PFRDA. Components of the consent artifact structure include :
- Identifier : Specifies entities involved in the transaction: who is requesting the data, who is granting permission, who is providing the data, and who is recording consent.
- Data : Describes the type of data being accessed and the permissions for use of the data. Three types of permissions are available: view (read only), store, and query (request for specific data). The artifact structure also specifies the data that are being shared, date range for which they are being requested, duration of storage by the consumer, and frequency of access.
- Purpose : Describes end use, for example, to compute a loan offer.
- Log : Contains logs of who asked for consent, whether it was granted or not, and data flows.
- Digital signature : Identifies the digital signature and digital ID user certificate used by the provider to verify the digital signature. This allows providers to share information in encrypted form
The Approach :
THE AA consent based data sharing model mediates the flow of data between producers and users of data, ensuring that sharing data is subject to granular customer consent. AAs manage only the consent and data flow for the benefit of the consumer, mitigating the risk of an FIU pressuring consumers to consent to access to their data in exchange for a product or service. However, AAs, as entities that sit in the middle of this ecosystem, come with additional costs that will affect the viability of the business model and the cost of servicing consumers. FIUs most likely will urge consumers to go directly to an AA to receive fast, efficient, and low-cost services. However, AAs ultimately must market their services directly to the consumer. While AA services are not an easy sell, the rising levels of awareness among Indian consumers that their data are being sold without their consent or knowledge may give rise to the initial wave of adopters. While the AA model is promising, it remains to be seen how and when it will have a direct impact on the financial lives of consumers.
Differences between Personal Data Protection & GDPR ?
There are some major differences between the two.
First, the bill gives India’s central government the power to exempt any government agency from the bill’s requirements. This exemption can be given on grounds related to national security, national sovereignty, and public order.
While the GDPR offers EU member states similar escape clauses, they are tightly regulated by other EU directives. Without these safeguards, India’s bill potentially gives India’s central government the power to access individual data over and above existing Indian laws such as the Information Technology Act of 2000, which dealt with cyber crime and e-commerce.
Second, unlike the GDPR, India’s bill allows the government to order firms to share any of the non personal data they collect with the government. The bill says this is to improve the delivery of government services. But it does not explain how this data will be used, whether it will be shared with other private businesses, or whether any compensation will be paid for the use of this data.
Third, the GDPR does not require businesses to keep EU data within the EU. They can transfer it overseas, so long as they meet conditions such as standard contractual clauses on data protection, codes of conduct, or certification systems that are approved before the transfer.
The Indian bill allows the transfer of some personal data, but sensitive personal data can only be transferred outside India if it meets requirements that are similar to those of the GDPR. What’s more, this data can only be sent outside India to be processed; it cannot be stored outside India. This will create technical issues in delineating between categories of data that have to meet this requirement, and add to businesses’ compliance costs.
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AIQRATE in Virtual Round-table organized by IET in partnership with British High Commission
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Institute of Engineers & Technology (IET) India with the British High Commission’s UK India Tech Partnership to led a joint study on AI skilling landscape in India. The study uncovered key pressing challenges around AI skilling including curriculum, content, access, availability as well as perceived end results of gaining AI skills.
To understand the entire gamut of AI skilling space further, IET brought together global experts for a virtual round table on Friday, July 3rd 2020 to dive into the depths of ‘AI Skilling in India: Opportunities, challenges and road ahead’. The session was inaugurated by Jeremy Pilmore, British Deputy High Commissioner, British High Commission, Bangalore, India. Sameer Dhanrajani, CEO & Co-founder, AIQRATE was part of the virtual round table which discussed about bridging AI skills gap in India.
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Strategic perspectives for India to attain AI supremacy
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The strategic perspectives provided herein will provide you crucial overview of the AI’s increasing prevalence amongst Indian industry, government and peripheral ecosystem and the significant impact AI will generate for India in the coming years and the possible strategic considerations that India needs to initiate to attain AI supremacy. The ensuing details also highlights the relative comparison amongst India, China and USA on the steady progress being done in AI adoption. VC firms, PE funds and investors attempting to understand where to target investment, what offerings and capabilities would lead to better performance and gains, and how to capitalize on AI opportunities, it’s crucial for them to understand the International economic potential of AI for now and projections in the coming years. Cutting across all these strategic considerations is how to build responsible AI operating models and keep it transparent enough to maintain the confidence of customers and wider stakeholders.
International AI Capitalization Report – China & NA Leads, India hot in the heels
Without doubt, AI is going to be a big game changer in the international setting. A previous set of reports from multiple analysts concluded that AI could contribute up to $15.7 trillion to the global economy in 2030, more than the current output of China and India combined. Of this, $6.6 trillion is likely to come from increased productivity and $9.1 trillion is likely to come from consumption-side effects. Global GDP will be up to 14% higher in 2030 as a result of the accelerating development and take-up of AI from the standpoint of direct economic impact of AI, China and USA will have greatest gains in GDP. Even though USA will reach its peak of AI led growth faster due to huge opportunities in parallel technologies implementations and advanced customer readiness for AI.
China, on the other hand will have a slower but stable rise in GDP gains, post COVID 19 because a large portion of Chinese GDP comes from manufacturing, a sector which is highly susceptible to AI disruption in its operation, and also a higher rate of capital re-investment within Chinese economy compared to EU and USA. As productivity in China eventually catches up with USA , USA will focus more on importing AI-enabled products from China due to economically cheap alternative China provides. Hence by 2030, China will see much larger impact in its GDP.
Is the Differential for Developing countries like India too steep in catching up with AI? – AI is still at its early stages, which means that irrespective of the fact that the exponential technology landscape is skewed towards the developed economies as compared to developing, the developing economies and their markets could still lead the developed markets from AI standpoint. This makes countries like India, with a strong focus in Technology sector, a strong contender.
The economic impact of AI in GDP for India ,will be driven by:
- Productivity gains from businesses automating processes (including use of robots and autonomous vehicles).
- Productivity gains from businesses augmenting their existing labor force with AI technologies (assisted and augmented intelligence).
- Increased consumer demand resulting from the availability of personalized and/or higher-quality AI-enhanced products and services.
The consumer revolution set off by AI opens the way for massive disruption as both established businesses and new entrants drive innovation and develop new business models. A key part of the impact of AI will come from its ability to make the most of parallel developments such as 5G connectivity.
India’s Macroeconomic Landscape of AI
India is already way ahead of many other countries in implementing artificial intelligence (AI). More than 40% of the enterprises are going beyond pilot and test projects and adopting the technology at a larger scale coupled with 1400+ global capability centers that have become frontiers in pushing AI led innovation and transformation for their parent organizations. The Indian government’s Digital India initiative, too, has created a favorable regulatory environment for increased use of AI.
Recipe for AI Success in India – Digital Deluge & Data Detonation
As India undergoes rapid digital transformation, data centers and the intelligence behind the data collected will enable the government and industry to make effective decisions based on algorithms. This means increasing opportunities for adoption (and investing over) AI in the country.
Intel is betting on Artificial Intelligence (AI) to drive demand for its electronic chips, for which it is aiming to train 15,000 scientists, developers, engineers and students on AI in India over the next one year. The company will host 60 courses under its ‘AI Developer Education Program’. These will train people on ways they can adopt AI for better research, testing or even building of products. Intel is looking at India due to the country’s large base of technical talent. The country is the third largest global site for AI companies. As India’s largest e-commerce marketplace Flip kart is looking to put in use its mammoth pile of data to predict sales of products months in advance. The company is working on an artificial intelligence (AI) solution that will give it an edge over rivals by helping it make smarter decisions in ordering, distribution and pricing products on its platform. Ultimately, the AI system will allow Flip kart to boost efficiency and reduce the cost of products for customers. While rival Amazon, which has around a 10-year head start over Flip kart, is known to have some of the most advanced sales prediction engines, the Indian company has the advantage of having a bigger data set of the country’s online consumer market.
AI Inroads in the Private Sector
AI has now a significant impact in the day to day lives of the regular mass of the country. Now that the Indian IT sector has reached a certain intermediary peak of digitization, the focus, now , is more on automating the repetitive problems and finding more optimized, efficient or refined methods of performing the same tasks, with less time duration and lesser manpower. The result is the standardization of some very critical app based services like virtual assistants, cab aggregators, shopping recommendations etc. This will eventually lead to AI solutions to real world problems.
The AI Startups Sphere of India- Startups are clearly playing a major role in innovating faster than enterprises, which has led to several partnerships. SAP India has invested in Niki.ai, a bot that improves the ordering experience. Then there’s Ractrack.AI, where a bot improves customer engagement and provides insights; it functions as a virtual communications assistant to convert the customer into a client. Racetrack is helping companies turn leads into meaningful engagements by using AI. Another startup, LUCEP, converts all potential queries into leads with their AI engine. The objective is to generate insights from data and simplify customer interaction with a business and also convert them into leads. Indian startups saw $ 10 billion in risk capital being deployed across 1,540 angel and VC/PE deals between January and December 2019. VC/PE firms predict that AI would be key themes to invest in for next few years.
AI in Public Sector– Ripe for Digital Revamp and AI Adoption
A Blue Ocean for AI Investment due to Digital India Initiatives – Though both corporates and startups are making significant inroads in instituting AI in their service architecture and product offerings, and sometimes as part of their core business strategy itself, the challenges in the public sector in instituting AI can be quickly overcome due to huge Digital Movements instituted by the Indian Govt. like Digital India, Skill India and Make in India. This will create a solid bedrock of Data and Digital Footprint which will act as a foundational infrastructure to base AI implementation on, opening a huge blue ocean in public sector, rich for AI investment.
A New Workaround for Regulatory Challenges in Public Sector AI Implementation – One of the peculiar problems the public sector faces in mainstream implementation of AI is the fact that since AI is a continuously self-learning system, capable of analytical or creative decision making and autonomous implementation of actions, who will then be accountable in taking responsibility for its actions, should they turn out to be not so favorable. This is because of the fact that since AI has a degree of autonomous decision making, it makes it difficult to pre-meditate its consequence. The AI systems are meant to augment and enrich the life of the consumers. In such a situation, deciding liability of AI system’s actions will be difficult. Therefore, a lot of deliberation will be required to carefully come to a precise conclusion surrounding implementing these systems with ethical foundation and propriety.
Although many countries like US and some European countries are in the verge of implementing regulations and laws surrounding concepts like driver less vehicles, India still don’t have the regulations sanctioned. This, but need not be a bad news. India is cut to establish a completely revamped legal infrastructure, thereby completely circumventing the need for continuous regulatory intervention. Also, there is a favorable atmosphere in India as far as AI is concerned which will foster a spike in activities in that avenue.
Indian Governance Initiatives – Huge Scope for Investment of AI – As India emerges as a premier destination for AI, scope for investment opens in the governance aspect, in several ways. Governance schemes have a unique trait of the baggage of large volume and large scale implementation need, which can be tackled with Deep learning. For example, in Swachh Bharat Initiative, specifically construction of toilets in rural India, public servants are tasked with uploading images of these toilet constructions to a central server for assessment. Image recognition can be used to target unfinished toilets. It can also be used to identify whether the same official appears in multiple images or if photos were uploaded from a different location other than the intended place. Other initiatives such as the Make in India, Digital India & Skill India can be augmented with AI to deal with scale. The range of application for AI techniques could range from crop insurance schemes, tax fraud detection, and detecting subsidy leakage and defense and security strategy.
An AI system can improve and enrich the agriculture of India by enhancing the bodies like The Department of Agriculture Cooperation and Farmers Welfare, Ministry of Agriculture runs the Kisan Call Centers across the country etc. It can help assist the call center by linking various available information like soil reports from government agencies and link them to the environmental conditions. It will then provide advice on the optimal crop that can be sown in that land pocket. As the need for large scale implementation and monitoring of governance initiative becomes more pronounced, the need for AI becomes absolute and it will open doors to considerable AI investment in the future of India.
Finally, Looking Ahead – A Collaborative Innovation led ecosystem
AI innovations which fall under assisted, augmented and autonomous intelligence will help users understand and decide which level of intelligence is helpful and required in their context, thereby making AI Acceptance easier for the people. At the same time, this AI continuum can be used to understand economic ramifications, usage complexity and decision-making implications. While academia and the private sector conduct research on various AI problems with diverse implications in mind, the public sector with its various digital initiatives (Digital India, Make in India, etc.) can identify areas where parts of the AI continuum can be utilized to increase reach, effectiveness and efficiency, thereby giving direction to AI Innovative Research. A collaborative innovation environment between academia and the private and public sectors will help provide holistic and proactive advisory delivery to the population, for example through public call centers, linking information from various government sources. At the same time, the rich data generated from these interactions can be used to draw deep conclusions. Collaboration between the three pillars could further help get a comprehensive view of problems and find intelligent and innovative ways to increase the efficiency and effectiveness of services delivered to society. India is at a cusp of taking a upward trajectory on establishing AI supremacy ; a strategic roadmap across public, private , SMB’s , Academic and startup sectors will accelerate the path to AI adoption and unleashing new sources of economic output for the country . The journey to attain AI supremacy has begun ……
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REPORT: Reimagine The Future of Work with New Age Opportunities
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The management of talent has always been and continues to be a major challenge for most industries. This is particularly true for knowledge based industries like information technology. The dramatically changing dynamics of the Indian Technology industry compound the challenges and opportunities faced by the industry.
Never since the advent of mass production has an industry seen such dramatic volatility in such short period of time. The revolution before primarily added to the productivity of the labor and moved across the globe. The current revolution is not merely transcending national borders – it is redefining jobs, eliminating others and creating new opportunities.
Please fill in the below details to download the complete report.
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How India can Emerge as a Premier Destination for AI; Watch out China, USA…
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This detailed primer will provide you crucial overview of the AI’s increasing prevalence amongst Indian industry, government and peripheral ecosystem and the significant impact AI will have in your organizations to remodel strategic and business models accordingly. The ensuing details also highlights the relative comparison amongst India, China and USA on the steady progress being done in AI adoption.
VC’s, PE funds and investors attempting to understand where to target investment, what offerings and capabilities would lead to better performance and gains, and how to capitalize on AI opportunities, it’s crucial for them to understand the International economic potential of AI for now and projections in the coming years.
Cutting across all these considerations is how to build responsible AI operating models and keep it transparent enough to maintain the confidence of customers and wider stakeholders.
International AI Capitalization Report – China & NA Leads, India hot in the heels
Without doubt, AI is going to be a big game changer in the international setting. A recent PwC report concludes that AI could contribute up to $15.7 trillion to the global economy in 2030, more than the current output of China and India combined. Of this, $6.6 trillion is likely to come from increased productivity and $9.1 trillion is likely to come from consumption-side effects.
Global GDP will be up to 14% higher in 2030 as a result of the accelerating development and take-up of AI
from the standpoint of direct economic impact of AI, China and North America will have greatest gains in GDP. Even though NA will reach its peak of AI led growth faster due to huge opportunities in parallel technologies implementations and advanced customer readiness for AI. NA is supposed to reach the peak of macroeconomic gains by 2020, following which there would be a relative slowdown in the growth.
Figure 1: Souce – PwC Analysis
China, on the other hand will have a slower but stable rise in GDP gains, even post mid 2020s because a large portion of Chinese GDP comes from manufacturing, a sector which is highly susceptible to AI disruption in its operation, and also a higher rate of capital re-investment within Chinese economy compared to EU and NA. As productivity in China eventually catches up with North America, NA will focus more on importing AI-enabled products from China due to economically cheap alternative China provides. Hence by 2030, China will see much larger impact in its GDP.
Sector-wise AI Consumption Impact Index – The sector-wise impact of AI and its constituent offerings space will give crucial overview for investors to get a clear understanding of opportunities and threats in AI investment space. AI is set to be the key source of transformation, disruption and competitive advantage in today’s fast changing economy. Drawing on the findings of our AI Impact Index, we look at how quickly change is coming and where your business can expect the greatest return.
Figure 2: AI Consumption Impact (Src: PwC Analysis)
Deriving from the detailed PwC analysis report, which includes an AI impact index rating which gives an indication of enhancing quality and personalization for consumers and freeing up their time to invest their preferences in other endeavours.
The areas with the biggest potential representation will help your business target investment in the short to medium term. Some aspects could be even more disruptive or even revolutionary, such as robotic doctors, but they would be further off in the timeline of mainstream implementation.
Is the Differential for Developing countries like India too steep in catching up with AI? – AI is still at its early stages, which means that irrespective of the fact that the technology landscape is skewed towards the developed economies as compared to developing, the developing economies and their markets could still lead the developed markets from AI standpoint. This makes countries like India, the second largest economy with a strong focus in IT sector, a strong contender.
The economic impact of AI in GDP for developing countries, will be driven by:
- Productivity gains from businesses automating processes (including use of robots and autonomous vehicles).
- Productivity gains from businesses augmenting their existing labour force with AI technologies (assisted and augmented intelligence).
- Increased consumer demand resulting from the availability of personalised and/or higher-quality AI-enhanced products and services.
The consumer revolution set off by AI opens the way for massive disruption as both established businesses and new entrants drive innovation and develop new business models. A key part of the impact of AI will come from its ability to make the most of parallel developments such as IoT connectivity.
India’s Macroeconomic Landscape of AI
India is already way ahead of many other countries in implementing artificial intelligence (AI). More than half of the companies are going beyond pilot and test projects and adopting the technology at a larger scale. This statistic is largely driven by American firms such as Accenture, Microsoft, the successful implementers’ toolkit, said. Last year, India was the second-largest global site for new centres, after the US.
Well over 58% of the companies that are using AI in India are working with the technology at significant scale
The Indian government’s Digital India initiative, too, has created a favourable regulatory environment for increased use of AI.
Recipe for AI Success in India – Digital & Data Bedrock
As India undergoes rapid digital transformation, data centres and the intelligence behind the data collected will enable the government and industry to make effective decisions based on algorithms. This means increasing opportunities for using (and investing over) AI in the country.
For example, Intel is betting on Artificial Intelligence (AI) to drive demand for its electronic chips, for which it is aiming to train 15,000 scientists, developers, engineers and students on AI in India over the next one year. The company will host 60 courses under its ‘AI Developer Education Program’. These will train people on ways they can adopt AI for better research, testing or even building of products. Intel is looking at India due to the country’s large base of technical talent. The country is the third largest global site for AI companies.
As India’s largest e-commerce marketplace Flipkart closes in on completing a decade in the business, it is looking to put in use its mammoth pile of data to predict sales of products months in advance. The company is working on an artificial intelligence (AI) solution that will give it an edge over rivals by helping it make smarter decisions in ordering, distribution and pricing products on its platform. Ultimately, the AI system will allow Flipkart to boost efficiency and reduce the cost of products for customers.
While rival Amazon, which has around a 10-year headstart over Flipkart, is known to have some of the most advanced sales prediction engines, the Indian company has the advantage of having a bigger data set of the country’s online consumer market.
AI Inroads in the Private Sector
AI has now a significant impact in the day to day lives of the regular mass of the country. Now that the Indian IT sector has reached a certain intermediary peak of digitization, the focus, now , is more on automating the repetitive problems and finding more optimized, efficient or refined methods of performing the same tasks, with less time duration and lesser manpower. The result is the standardization of some very critical app based services like virtual assistants, cab aggregators, shopping recommendations etc. This will eventually lead to AI solutions to real world problems.
The AI Startups Sphere of India- Startups are clearly playing a major role in innovating faster than corporates, which has led to several curious partnerships. SAP India has invested in Niki.ai, a bot that improves the ordering experience. Then there’s Ractrack.AI, where a bot improves customer engagement and provides insights; it functions as a virtual communications assistant to convert the customer into a client. Racetrack is helping companies turn leads into meaningful engagements by using AI. Another startup, LUCEP, converts all potential queries into leads with their AI engine.
The objective is to generate insights from data and simplify customer interaction with a business and also convert them into leads.
Indian startups saw $4 billion in risk capital being deployed across 1,040 angel and VC/PE deals between January and December 2016.
Disclosed funding announcements have shown a decreased value of 55 percent from the same period last year (2015) and a decrease of 20 percent from 2014. About $9 billion in VC/PE capital had been invested in 2015. The number of deals in 2016, however, has increased by 3 percent over the last year. On an average, four startup deals were announced every weekday throughout 2016. VCs predict that going forward machine learning and AI would be key themes to invest in.
AI in Public Sector– Ripe for Digital Revamp and AI Adoption
A Blue Ocean for AI Investment due to Digital India Initiatives – Though both corporates and startups are making significant inroads in instituting AI in their service architecture and product offerings, and sometimes as part of their core business strategy itself, the challenges in the public sector in instituting AI can be quickly overcome due to huge Digital Movements instituted by the Indian Govt. like Digital India, Skill India and Make in India. This will create a solid bedrock of Data and Digital Footprint which will act as a foundational infrastructure to base AI implementation on, opening a huge blue ocean in public sector, rich for AI investment.
A New Workaround for Regulatory Challenges in Public Sector AI Implementation – One of the peculiar problems the public sector faces in mainstream implementation of AI is the fact that since AI is a continuously self-learning system, capable of analytical or creative decision making and autonomous implementation of actions, who will then be accountable in taking responsibility for its actions, should they turn out to be not so favourable. This is because of the fact that since AI has a degree of autonomous decision making, it makes it difficult to pre-meditate its consequence. The AI systems are meant to augment and enrich the life of the consumers. In such a situation, deciding liability of AI system’s actions will be difficult. Therefore, a lot of deliberation will be required to carefully come to a precise conclusion surrounding implementing these systems with ethical foundation and propriety.
Although many countries like US and some European countries are in the verge of implementing regulations and laws surrounding concepts like driverless vehicles, India still don’t have the regulations sanctioned. This, but need not be a bad news. India is cut to establish a completely revamped legal infrastructure, thereby completely circumventing the need for continuous regulatory intervention. Also, there is a favourable atmosphere in India as far as AI is concerned which will foster a spike in activities in that avenue.
Indian Governance Initiatives – Huge Scope for Investment of AI – As India emerges as a premier destination for AI, scope for investment opens in the governance aspect, in several ways. Governance schemes have a unique trait of the baggage of large volume and large scale implementation need, which can be tackled with Deep learning. For example, in Swachh Bharat Initiative, specifically construction of toilets in rural India, public servants are tasked with uploading images of these toilet constructions to a central server for assessment. Image recognition can be used to target unfinished toilets. It can also be used to identify whether the same official appears in multiple images or if photos were uploaded from a different location other than the intended place.
Other initiatives such as the Make in India, Digital India & Skill India can be augmented with AI to deal with scale. The range of application for AI techniques could range from crop insurance schemes, tax fraud detection, and detecting subsidy leakage and defence and security strategy.
An AI system can improve and enrich the agriculture of India by enhancing the bodies like The Department of Agriculture Cooperation and Farmers Welfare, Ministry of Agriculture runs the Kisan Call Centers across the country etc. It can help assist the call centre by linking various available information like soil reports from government agencies and link them to the environmental conditions. It will then provide advice on the optimal crop that can be sown in that land pocket.
As the need for large scale implementation and monitoring of governance initiative becomes more pronounced, the need for AI becomes absolute and it will open doors to considerable AI investment in the future of India.
Finally, Looking Ahead – A Collaborative Innovation Environment due to AI
AI innovations which fall under assisted, augmented and autonomous intelligence will help users understand and decide which level of intelligence is helpful and required in their context, thereby making AI Acceptance easier for the people. At the same time, this AI continuum can be used to understand economic ramifications, usage complexity and decision-making implications. While academia and the private sector conduct research on various AI problems with diverse implications in mind, the public sector with its various digital initiatives (Digital India, Make in India, etc.) can identify areas where parts of the AI continuum can be utilised to increase reach, effectiveness and efficiency, thereby giving direction to AI Innovative Research.
A collaborative innovation environment between academia and the private and public sectors will help provide holistic and proactive advisory delivery to the population, for eg. through public call centres, linking information from various government sources. At the same time, the rich data generated from these interactions can be used to draw deep conclusions. Collaboration between the three pillars could further help get a comprehensive view of problems and find intelligent and innovative ways to increase the efficiency and effectiveness of services delivered to society.