AI 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.
Opportunities and Applications:
• The AI has the ability to overcome some of the computationally intensive, intellectual and perhaps creative limitations of humans. Therefore, it 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.
• AI can impact education in three ways:
o AI enabled hyper-personalization helps in developing student-specific learning profile and in developing customized learning environments based on ability, preferred mode of learning and experience.
o The use of smart assistants (Amazon Alexa, Google Home etc.) and associated
technologies offer significant potential to help students.
o AI systems can assist educators with secondary tasks such as grading activities, providing personalized response to students etc.
• AI technology can be used within several other sectors for enhancing both efficiency and effectiveness.
AI and SDG:
• AI-based systems can help in achieving UN Sustainable Development Goals. It can be utilized for conducting remote diagnosis supporting doctors to help improve health service delivery.
• It 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 timely manner to mitigate the risk of catastrophic agricultural events.
• Similarly, AI 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:
A. In Health
• India has 0.8 per thousand doctor-to-patient ratio (UK – 2.8, Australia – 5, China – Approx. 4). 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.
B. In Agriculture
• 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 significant loss of productivity due to pests and diseases.
• AI-based agricultural pest and disease identification system are helping farmers in identifying the disease and advising the remedial measures.
C. In Education
• India has about 50% less teachers per thousand students when compared with developed countries (India 2.4/thousand vs. UK 6.3/thousand). In this scenario, AI can help in providing education in remote areas.
• India has 1.18 billion mobile phone users with 600 million internet users and 374 million smartphone users.
• It has one of the cheapest data rates in the world ($0.24/GB) and an average data speed of 6 MBPS.
• These factors open up huge potential for adoption of AI technology in India.
Challenges and Shortcomings:
• Lack of explainability – 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.
• Lack of contextual awareness and inability to learn – AI based systems have major limitations in terms of making decisions where context plays a critical role.
• Lack of Standardization – AI based systems are increasingly being embedded in variety of products and services. 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?
• Organizations 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 emphasizes the need for strategic management of AI transition requiring organizations to carefully consider a number of challenges: how to select tasks for automation; how to select the level of automation for each task; how to manage the impact of AI-enabled automation on human performance and how to manage AI-enabled automation errors.
• Lack of competency and need for re-skilling and up-skilling workers.
• Lack of trusts and resistance to change – Due to above mentioned issues and negative media coverage on the consequences of AI, people are generally apprehensive about its implementation.
Key Public Policy Challenges of AI:
I. Ethics – There are two dimensions of ethics in AI:
a. Privacy and Data Protection: It is the top most concern while using AI systems.
b. Human and Environmental Values – Any AI system has to conform to human value system and the policymakers need to ask: Has the AI system been sensitized to human values such as kindness, equity, dignity etc.? An important aspect which needs to be built into AI systems is the overall cost of their decisions on the society.
II. Transparency and Audit – The technology providers must explain the decision-making process to the user so that the AI system doesn’t remain a black box. These AI systems must provide an audit trail of decisions made not only to meet the legal needs but also for us to learn and make improvements over past decisions.
III. 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.
IV. Fairness and Equity – AI can disrupt social order which could damage the social fabric exposing people lower in bargaining hierarchy with a real threat of exploitation and unfair treatment. An AI system designed with equity as a priority would ensure that no one gets left behind in this world. Also, the AI system must exhibit 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).
V. Accountability and Legal Issues – 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.
VI. Misuse Protection – This possibly is the toughest of all. How do we insulate every new technology to prevent it from being twisted for achieving destructive goals?
• States like Tamil Nadu have already started deploying AI systems at scale for addressing some of the key challenges in health, education and agriculture sectors.
• 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.