THE ROLE OF ARTIFICIAL INTELLIGENCE (AI) IN CONSUMER PROTECTION ACT IN PRESENT PROSPECTIVE
Bibliographic record
Abstract
Artificial intelligence is revolutionizing our daily lives in health care, shopping, and travel, but it is as we become increasingly aware also introducing serious risks for consumers. In this AI driven and rapid growth of technology there is a necessity to protect consumers in India. Today this study aims at drawing ideas that how AI can help in better governance to protect the consumer-based economy. Not only this most transaction is taking place on digital platform, but judicial procedure also seeing rapid changing in AI, there is need of an urgent law to protect Indian consumers. It presents detailed ideas for how AI should be governed in the consumer sector. It recommends creating a special law about AI, setting clear policies, procedures and rights, supported by a risk-based grouping method much like the one in the EU AI Act. The Consumer Protection Act,2019 could be improved to address AI- based products and services, paying attention to product liability and unfair trade practices, It is recommended that a government agency oversee AI rules, monitor use and assure transparency, together with making it mandatory for algorithms used in consumer services to be accountable. Building institutional capacity would involve giving AI training to consumer court staff and the regulatory personnel involved. Arranging public awareness campaigns will help people know their rights concerning AI. At the same time, organizations should be motivated to create ethical AI, but with strict monitoring from authorities.in this regard it is suggested that Canada works together with OECD, WTO and the UN to follow global best practices. Regular assessments are required to maintain regulations that are appropriate and focus on consumers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".