Q: What are the most common AI transcription risks for social workers? A: Common AI transcription risks for social workers include hallucinations that insert details no one said (for example falsely indicating suicidal ideation), misheard phrases that become “gibberish”, and summaries that add judgments or labels. These errors can lead to incorrect decisions about care, missed risks, and professional consequences if outputs are used without proper human review. Q: How widespread are errors in AI transcriptions used by councils? A: An eight-month study by the Ada Lovelace Institute across 17 English and Scottish councils highlighted AI transcription risks for social workers, finding potentially harmful misrepresentations appearing in official care records. Social workers in multiple councils reported gibberish, mislabeling and false flags while the research also noted observable time savings. Q: Do AI note-takers save time for social workers despite the risks? A: Yes, studies found AI transcription delivered observable time savings and helped social workers focus more on relationships during visits. However, those time gains require consistent checking because unchecked outputs have produced errors that can affect care and lead to disciplinary action. Q: What simple safeguards reduce AI transcription risks for social workers? A: To address AI transcription risks for social workers, the essential safeguards are strong human review, clear rules, and better training; adopt a human-in-the-loop standard, require a minimum verification step, and use structured review checklists. Improve audio quality, use prompts that ban added content, and log who checked and corrected transcripts. Q: How should social workers check and verify AI-generated transcripts? A: Use a two-pass check: first confirm factual details (names, dates, timelines and speaker attributions) and scan for added claims such as self-harm that were not spoken, then review tone, consent and safety decisions. Compare key quotes to the audio before finalising and write the analysis section yourself to preserve professional judgment. Q: What kind of training helps staff manage AI transcription tools safely? A: Training should focus on judgment rather than just tool mechanics, offering short practical workshops with real case audio and examples of hallucinations to teach detection. Staff should also learn when to avoid AI — for sensitive disclosures or poor audio — and refresh data protection and consent rules. Q: What policies should leaders and regulators require for AI use in social work? A: Leaders should publish short policies defining allowed use, required checks, and red lines, require explicit consent where needed, and prohibit copying AI drafts directly into final records without review. Regulators should issue clear guidance on how and when AI tools should be used and organisations should run spot audits and measure error and correction rates. Q: What improvements should vendors make to make AI transcription safer for social work? A: Vendors should build “no invention” modes for sensitive topics, tune models on local accents, add low-confidence flags and provide side-by-side audio and text for quick verification. They should also supply audit logs and edit trails and share bias and hallucination evaluations with clients to support accountability.