Effect of Artificial Intelligence (AI) on GDP Growth of India in 2022: An Analysis
Bibliographic record
Abstract
In this present era, artificial intelligence (AI) is a buzz word. With the increase in hype for AI and its increasing rate of application worldwide, it has raised a debate on the impact of AI on increase in growth rate, productivity and unemployment. Having said that, studies have shown AI is still at an infant stage. With the increase in the ability to understand context and reply in human language, it has surely paved its way towards an impeccable journey of transforming an economy with the increase in productivity, gross domestic product (GDP) and economic growth. The advancement may cause some setbacks in terms of labour force participation with higher demand for labour with certain skill set to be able to work with AI and hence increasing the unemployment rate as well as creating a widening gap between developing and developed countries. In this chapter, we aim to determine the effect of AI on GDP growth of India for the year 2022 and have selected terms of trade (ToT), human development index (HDI) and per capita real GDP for the year 2022 to understand its overall impact using secondary data. The results of the analysis show that ToT, global artificial intelligence (GAI) index, and HDI as explanatory variables significantly explain the variation in per capita real GDP.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".