Research on the application risks and countermeasures of ChatGPT generative artificial intelligence in social work
Bibliographic record
Abstract
Since its debut on November 30, 2022, OpenAI's ChatGPT has rapidly transformed natural language processing (NLP) and artificial intelligence-generated content (AIGC). Leveraging Generative Pre-trained Transformer (GPT) technology, ChatGPT offers cost-effective, efficient, and diverse content creation. Despite advancements like GPT-4, concerns about data security, algorithmic bias, and ethical issues persist, especially in social work. AI integration in social work presents challenges such as potential data breaches and weakened interpersonal connections, necessitating robust regulations and ethical guidelines. Addressing these issues requires strict data protection, diverse AI training datasets, and transparency in AI-driven decisions. By balancing technological integration with humanistic values, AI can enhance social work efficiency while maintaining essential human care and support. This ensures AI serves as a beneficial tool rather than replacing human contributions.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".