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Insights > AI

ARTIFICIAL INTELLIGENCE 

TBA INSIGHTS

GLOBAL REGULATORY OVERVIEW

INDIA

INDIA

INDUSTRY OVERVIEW 

REGULATORY & LEGAL OVERVIEW

REGULATORY BODIES

APPLICABLE LAWS 

POLICY DOCUMENTS 

SELF REGULATORY FRAMWORK

TBA GUIDE TO LEGAL COMPLIANCE & SELF-REGULATION IN AI

Although there are no direct laws or regulations in India that regulate Artificial Intelligence, the existing laws  are sufficient for tackling the challenges of AI that directly impact society. They are described in the documents as "System Considerations" and that the existing laws require sector-specific modifications and alignments. However, the policy documents identify a different category of challenges which indirectly impact the society such as loss in jobs, deep fakes, pshychological profiling and macicious use. For challenges having indirect impact such as loss of jobs they suggests skilling, adapting legislations and regulations to harness new job opportunities. It is interesting to see that the recommendations on dealing with malicious use of AI for spreading hate or propoganda, is to use the technology for proactive adentificaton and flaging.

Policy documentation also identifies ethical challenges in AI based on their impact on the Indian society while recognizing the issues such as the 'Black Box Phenomenon', the issues of data collection without proper consent, the privacy of personal data, inherent selection bias, risk of profiling and discrimination, and non-transparent nature of certain AI solutions. They also recognize the reputational issues of public fear that companies are somehow harnessing huge consumer data and utilizing it inappropriately to gain consumer insight; and that the companies are developing large DATASETS and building unfair competitive advantage somehow.

Policy Documentation emphasizes conscious development of 'XAI' or explainable AI and concepts such as 'Differential Privacy' by implementing 'Federated Learning' wherein data trusts are developed for easy and secure sharing of data without compromising any sensitive personal data or information. The documentation also prescribes Technical best practices on three broader principles: Explainability using Pre hoc and Post hoc techniques; Privacy and data protection using federated learning, differential privacy, zero knowledge protocols or homomorphic encryption; and Emiminating bias and encouraging fairness using such as Tools such as IBM's 'AI Fairness 360', Google's 'What-If' Tool, Fairlearn and open source frameworks such as FairML.

- MARKET VALUATION

- AI market share and size in relation to the Types of Companies is the highest across the broad MNC IT / Technology / Electronic category

- MARKET SHARE OF DOMESTIC INDIAN FIRMS

- CAGR

- FDI

- SUB-SECTORS

- FUTURE GROWTH PROJECTIONS

- INDUSTRY APPLICATIONS/ USE -SECTOR WISE

- STATE WISE DATA AND LAWS

- WORKING PROFESSIONALS

UNITED STATES

USA
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