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(GIST OF YOJANA) Artificial Intelligence: Challenges and Opportunities for India [FEBRUARY-2020]

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(GIST OF YOJANA) Artificial Intelligence: Challenges and Opportunities for India [FEBRUARY-2020]

(GIST OF YOJANA) Artificial Intelligence: Challenges and Opportunities for India  [FEBRUARY-2020] Artificial Intelligence: Challenges and Opportunities for India Introduction: Artificial Intelligence can be described as a system’s ability to learn and interpret external data via software/algorithms or machines/devices for problem solving by performing specific roles and tasks currently executed by humans. The term AI has…

(GIST OF YOJANA) Artificial Intelligence: Challenges and
Opportunities for India

 [FEBRUARY-2020]

Artificial Intelligence: Challenges and
Opportunities for India

Introduction:

  • Artificial Intelligence can be described as a system’s ability to learn
    and interpret external data via software/algorithms or machines/devices for
    problem solving by performing specific roles and tasks currently executed by
    humans.
  • The term AI has been used interchangeably with other closely related
    terms such as expert systems, decision-support system, knowledge-based
    systems, machine learning, natural language processing, neural networks,
    pattern recognition, recommender systems and text mining.

Background:

  • Although the origin of the term AI can be traced back to early 1950s,
    the relatively recent advancement in information technology (such as big
    data, improved computing, storage capability and super-fast speed of data
    processing machines) and robotics has enabled AI to gain significant
    momentum in terms of its development, application and use within public and
    private sector organizations.
  • The recent developments in AI offer the potential for significant
    opportunities for industry, governments and society, but there are many
    challenges and subsequent risks as Al-based systems are adopted for an ever
    increasing range of tasks and duties. In this article, we aim to briefly
    outline the opportunities and challenges, particularly focusing on elements
    of policy that could act as a major roadblock for development and further
    diffusion of AI-based systems.

Opportunities and Applications:

  • A multitude of opportunities have been presented for the application and
    use of Al-based systems in various domains particularly to assist where
    structured decision making is needed.
  • The ability of AI to the computationally intensive, intellectual and
    perhaps creative limitations of humans opens up new application domains
    within manufacturing, law, medicine, healthcare, education, government,
    agriculture, marketing, sales, finance, operations and supply chain
    management, public service delivery and cyber security.
  • Within the education sector, AI can be deployed to improve teacher
    effectiveness and student engagement by offering capabilities such as
    intelligent game-based learning environments, tutoring systems and
    intelligent narrative technologies. Schmelzer suggested that AI can impact
    education in three ways.
  • Firstly, Al-enabled hyper-personalisation helps in developing student
    specific learning profiles and in developing customised learning
    environments based on ability, preferred mode of learning and experience.
  • Secondly, the use of smart assistants (Amazon Alexa, Google Home, Apple
    Siri, and Microsoft Cortana) and associated technologies offer significant
    potential to help students. Universities are already using voice assistants
    to help answer common questions about campus, student schedules and courses.
  • Thirdly, AI systems can assist educators with secondary tasks such as
    grading activities, providing personalised responses to students, handling
    routine and repetitive paperwork and dealing with logistics-related matters.
    Al-based analytics can help with academic research within various
    disciplines and potentially transform library processes and staffing
    requirements with aim to provide a richer user experience.
  • AI technology can be used within several other sectors for enhancing
    both efficiency and effectiveness. Specifically, AI can help in achieving
    good health and well-being goals within rural and remote areas in developing
    countries where access to medical care is limited. In such scenarios,
    Al-based systems can be utilised for conducting remote diagnosis supporting
    doctors to help improve health service delivery.
  • Al-based systems can also help achieve the “Zero Poverty and Zero
    Hunger” (SDG 2) by assisting in resource allocation for predicting adverse
    environmental conditions, diagnose crop diseases and identify pests in a
    timely manner to mitigate the risk of catastrophic agricultural events.
    Similarly, Al-based systems can be used to predict energy and utility demand
    to help in achieving SDGs such as “Clean water, sanitation” and “Affordable
    clean energy”.

Application of AI in India:

  • Within the Indian context, a number of key indicators from health,
    education and agriculture sectors are important to highlight as AI is
    further adopted. India has 0.8 per thousand doctor-to-patient ratio (UK:
    2.8, Australia: 5, China: approximately 4). This low ratio implies a heavy
    workload on Indian doctors. In India, doctors spend just 2 minutes per
    patient, whereas in the US it is close to 20 minutes. AI could be a valuable
    assistive tool for doctors in helping reduce their workload and assisting in
    diagnosis.
  • Al-assisted diagnostics can provide access to quality healthcare for
    people in remote areas. The per hectare cereal productivity in India is
    almost half that of China and UK (3000 kg/ha vs. over 6000 kg/ha). There is
    a significant loss of productivity due to pests and diseases.
  • The Tamil Nadu e-Governance Agency has partnered with Anna University to
    launch a Tamil smart assistant called `Anil”. This NLP-based smart assistant
    provides a step-by-step guide to people in helping them apply online for
    scores of critical government services. The Tamil Nadu Government has been
    one of the pioneers in using AI for public service delivery.
  • The agency has recently launched an Al-based agricultural pest and
    disease identification system and made it available to over half a million
    farmer families through a mobile app. The farmer clicks an image of diseased
    crop or a pest and the system processes the image through an AI algorithm to
    identify the pest or disease and sends a message to the farmer advising the
    remedial measure. This system is gaining a good field response in which
    nearly 400 fanners are posting identification requests and getting answers
    every day.
  • The Tamil Nadu Government is implementing an innovative use of AI
    through face recognition for recording attendance. The system is saving more
    than 45 minutes per day and is freeing up extra time for core educational
    activities in schools. Within healthcare, AI solutions such as radiographic
    diagnostics like “detection of internal bleeding in the brain from CT scans”
    are being tried to assist doctors and increase their reach to serve remote
    areas of India.

Challenges and Shortcomings:

  • There exists a number of challenges and limitations of successfully
    implementing and utilising AI in both public and private sector
    organisations. Some of the key challenges are briefly outlined here.

Lack of explain ability:

  • Generally AI operates effectively as a black-box-based system that does
    not transparently provide the reasoning behind a particular decision,
    classification or forecast made by the systems. This is a major limitation
    of this technology as it has direct impact on transparency, hence trust and
    confidence of using decisions made.

Lack of contextual awareness and inability to learn:

  • Al-based systems are good at performing with given parameters and rules.
    However, they still have major limitations in terms of making decisions
    where context plays a critical role. Unlike humans, Al-based systems cannot
    learn from their environment. This limits the application of AI to specific
    types of domains.

Lack of standardization:

  • Al-based systems that may have utilised different types of
    technologies/techniques are increasingly being embedded in a variety of
    products and services (for example, smart assistants, modules for enterprise
    products, widely available cloud libraries and bespoke data science-driven
    applications).
  • This poses a critical question: how can the inferences delivered by
    different AI components be integrated coherently when they may be based on
    different data and subject to different ecosystem conventions (and the
    associated quality differences)? Furthermore, organisations face challenges
    on how to ensure AI and human work together successfully.

Job losses:

  • Increasing automation will lead to significant job losses particularly
    at operational and lower skill levels for repetitive tasks. This critical
    consequence of AI Use will continue to impact all sectors and countries
    across the world but particularly developing economies where employment
    opportunities are already limited.
  • This emphasizes the need for strategic management of AI transition
    requiring organizations’ to carefully consider a number of major challenges:
    how to select tasks for automation; how to select the level of automation
    for each task; how to manage the impact of Al-enabled automation on human
    performance and how to manage Al-enabled automation errors.

Lack of competency and need for re-skilling and up-skilling workers:

  • A large number of organisations still lack in-house competency to
    successfully develop and implement Al-based systems. In such a scenario,
    organisations utilise specialised consultancy firms which can be very
    resource intensive.
  • But this restricts organisations having limited resources in using such
    systems. Similarly, using or working with Al-based systems requires workers
    to be equipped with a new and advanced set of skills, which is a challenge
    for government, organisations and individuals.

Lack of trust and resistance to change:

  • Due to the above mentioned issues and negative media coverage on the
    consequences of AI, people are generally apprehensive about its
    implementation. This poses a major challenge on how to establish trust among
    workers and stakeholders in the management of resistance to change in
    adopting AI systems.
  • Public policy is facing unprecedented uncertainty and challenges in this
    dynamic world of AI. The velocity and scale of impact of AI is so high that
    it creates an interesting dynamics in terms of the need to predict its
    impact and inability to draw boundaries. We have identified six key public
    policy challenges of AI.

Ethics:

  • Ethics for machines has been an area of immense interest for the
    researchers. However, defining has proven to be problematic and difficult to
    make it computable.
  • To tackle this, we need to deal with ethics purely from an AI
    perspective. There are two dimensions of ethics in AI:
  1. Privacy and data protection and
  2. Human and environmental values.

(i) Privacy and Data Protection:

  • Privacy is possibly the top-most concern while using AI systems. Users’
    sensitive and highly granular data is likely to be stored and shared across
    the AI network (for example, a person’s location for the day based on face
    recognition and CCTV feeds, food habits, shopping preferences, movies, music
    etc.).

(ii) Human and Environmental Values:

  • Any AI system has to conform to the human value system and the
    policymakers need to ask: Has the AI system been sensitised to human values
    such as respect, dignity, kindness, compassion, equity or not? Does the
    system know that it has a preferential duty towards children, elderly,
    pregnant women, sick and the vulnerable? An important aspect which needs to
    be built into AI systems is the overall cost of their decisions on the
    society.

Transparency and Audit:

  • In the future, many of the Al-based systems could be interacting with
    humans in fields such as finance, education, healthcare, transportation and
    elderly care. The technology providers must explain the decision-making
    process to the user so that the AI system doesn’t remain a black box.
  • There exists a legal need to explain the decision taken by such systems
    in case of litigation. These AI systems must provide an audit trail of
    decisions made not only to meet the legal needs but also for us to leam and
    make improvements over past decisions.

Digital Divide and Data Deficit:

  • Since the entire AI revolution has data at its foundation, there is a
    real danger of societies being left behind. Countries and governments having
    good quality granular data are likely to derive maximum benefit out of this
    disruption. Countries where the data is of poor quality or of poor
    granularity would be left behind in harnessing the power of AI to improve
    lives of its citizens adversely affecting low-resource communities.
  • AD can disrupt social order and hierarchy creating new social paradigms,
    which could damage the social fabric exposing people lower in the bargaining
    hierarchy with a real threat of exploitation and unfair treatment.
  • This could lead to commoditization of human labour and chip away human
    dignity. An AI system designed with equity as a priority would ensure that
    no one gets left behind in this world. Another key need for autonomous
    systems is fairness. They must not exhibit any gender or racial bias and
    they must be designed to stay away from ‘social profiling’ (especially in
    law enforcement, fraud detection and crime prevention areas). The recent
    reports questioning the neutrality of AI systems used by police to identify
    crime-prone individuals has brought this issue out in sharp focus.

Accountability and Legal Issues:

  • Without AI, any system designed by a human is only a machine under the
    control of the operator. Therefore, accountability has not been an issue.
  • Almost all civil and criminal liability laws of the world fairly
    unanimously attribute accountability to the operator, owner and manufacturer
    of the machine in varying degrees depending upon the facts of the ease.
    However, once machines are equipped with AI and take autonomous decisions,
    the question of accountability becomes very hard to answer, more so when the
    algorithms are unknown to the designer.

Misuse Protection:

  • This possibly is the toughest of all six questions. How do we insulate
    every new technology to prevent it from being twisted for achieving
    destructive goals?
  • An ease in point – how internet proliferated across the globe
    benefitting billions but also carried along with it a wave of cybercrime,
    malware, viruses and violent online games which resulted in loss of innocent
    lives of teens around the world. Autonomous AI systems must be designed for
    misuse protection. It cannot be an afterthought.

Conclusion:

  • AI as a technology holds tremendous potential for a country like India,
    which is data rich and has the requisite technological capability to create
    AI solutions for many of its problems. States like Tamil Nadu have already
    started deploying AI systems at scale for addressing sonic of the key
    challenges in health, education and agriculture sectors.
  • Public roll-out of AI systems needs to address issues of ethics,
    transparency, audit, fairness, equity, accountability and misuse prevention.
    An effective public policy framework for AI along with a practical scorecard
    would be needed to make this AI revolution work towards an equitable
    prosperity.

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