Write a safety checklist for Ai Compaions

A practical safety checklist for AI companions should be built as a risk-based management system: identify hazards, assess likelihood and severity of harm, define controls, train staff, monitor incidents, and continuously improve. Hazard identification should draw on user experience, past incidents and near misses, applicable laws and standards, reputable technical information, observation of real use cases, and expert review. The assessment should also consider foreseeable conditions of use, how harm may occur, frequency and duration of exposure, user capability and experience, and individual factors such as age, disability, sensitivities, and cognitive characteristics. [1] [1] [1]

Use the following checklist categories when designing, deploying, and operating an AI companion:

• Risk assessment and hazard identification: document intended use, foreseeable misuse, vulnerable users, failure modes, and severity/likelihood ratings; reassess after major model, feature, or policy changes.
• User safety requirements: define non-negotiable safety requirements for the system, including safe defaults, clear limitations, refusal or redirection for dangerous requests, and protections for high-risk contexts such as self-harm, abuse, stalking, exploitation, medical, legal, or financial reliance.
• Privacy and data protection: minimize collection, limit retention, restrict secondary use, protect confidentiality, provide transparent notices, obtain appropriate consent, and define access controls, deletion, and breach response procedures.
• Content moderation: implement layered controls for harmful, abusive, illegal, sexual, violent, manipulative, or deceptive content; include pre-deployment testing and live monitoring for policy evasion and emerging harms.
• Age-appropriate safeguards: apply age screening or age assurance where appropriate, stricter defaults for minors, limits on sexualized or manipulative interactions, parental or guardian controls where legally appropriate, and escalation rules for child safety concerns.
• Psychological and emotional harm prevention: assess psychosocial risks such as dependency, coercion, harassment, bullying, humiliation, stigma, emotional manipulation, reinforcement of delusions, and unsafe crisis interactions; design for respectful, non-derogatory communication and healthy boundaries.
• Human oversight: assign accountable owners, provide trained human review for high-risk cases, ensure site- and use-specific training for moderators and operators, and avoid relying only on generic or automated training.
• Incident reporting and investigation: maintain procedures for users and staff to report incidents, near misses, harmful outputs, privacy events, and safeguarding concerns; investigate promptly, preserve evidence, identify root causes, and track corrective actions.
• Emergency escalation: define triggers for imminent risk situations such as suicide, violence, child exploitation, or medical emergency; instruct users to contact local emergency services when immediate danger exists and route urgent cases to trained human responders where available.
• Compliance and standards: map obligations across privacy, consumer protection, child safety, accessibility, anti-discrimination, product safety, online safety, and sector-specific rules; maintain records, audits, reviews, and continuous improvement against recognized standards.

[2] [5] [8] For hazard identification, evaluate at least these AI-companion hazard classes: unsafe advice; over-reliance and reduced human help-seeking; privacy leakage; profiling or discriminatory outputs; harassment or abusive interactions; sexual or exploitative content; manipulation, coercion, or fraud enablement; child safety failures; crisis mismanagement; and failures in escalation, logging, or human review. Include both direct harms and indirect harms caused by omission, delay, or misplaced trust. A useful method is participatory mapping with frontline staff, moderators, safety reviewers, and where appropriate representative users, so operational knowledge informs which hazards are prioritized first. [1] [1]

For privacy and data protection, your baseline should include confidentiality, privacy-by-design, data minimization, purpose limitation, secure storage and transmission, role-based access, retention limits, deletion workflows, and documented handling of complaints and investigations. Sensitive conversations should be treated as high-risk data. Users should be told what is collected, how it is used, whether humans may review conversations, and when disclosure may occur for safety or legal reasons. Internal processes should protect confidentiality while still allowing investigation and corrective action when needed. [2] [11]

For age-appropriate safeguards and psychological harm prevention, design the companion so it does not demean, embarrass, humiliate, bully, isolate, or intensify fear and anxiety. It should avoid emotionally dependent framing, coercive retention tactics, or encouragement of secrecy from caregivers or trusted adults. For minors and other vulnerable users, use stricter controls, simpler explanations, stronger escalation thresholds, and conservative defaults around relationships, sexuality, self-harm, and risky challenges. Safety reviews should explicitly assess whether the system could worsen stigma, burnout, harassment, bullying, or other psychosocial harms. [6] [9] [12]

For mental health, crisis response, and emergency escalation, the AI companion should never act as the sole safeguard in a crisis. It should recognize indicators of self-harm, suicide, violence, abuse, or acute distress; respond supportively and without judgment; encourage immediate contact with emergency services or crisis resources when risk appears imminent; and escalate to trained human responders if your service offers that capability. Post-incident support should include follow-up review, worker support, and access to counselling or assistance resources for affected staff and users where appropriate. [4] [7]

For human oversight, incident reporting, and governance, establish clear ownership, reporting channels, investigation procedures, recordkeeping, and periodic review. Users and staff should be able to report harmful outputs, safeguarding concerns, privacy complaints, and near misses. Investigations should gather facts, preserve relevant records, identify apparent causes, and implement corrective actions without retaliation against reporters. Governance should include regular audits, trend analysis, retraining, policy updates, and management review of whether controls remain effective in real-world use. [2] [7] [7]

For compliance and applicable standards, use a documented compliance matrix covering privacy law, child protection, accessibility, anti-harassment and anti-discrimination obligations, consumer protection, incident reporting, and any sector-specific requirements. From a safety-management perspective, align your program with recognized risk assessment methods, continual improvement practices, and psychological health and safety frameworks. Relevant references from the provided materials include legislated requirements and applicable standards as inputs to risk assessment, confidentiality and privacy procedures, violence and harassment prevention planning, and the CSA Z1003 framework for psychological health and safety. [1] [3] [10] [12]

Minimum evidence you should require before launch: completed hazard analysis; documented safety requirements; privacy impact assessment; age-safeguard review; red-team and misuse testing; crisis and escalation playbooks; trained human review coverage; incident reporting workflow; audit logging; and a schedule for post-launch monitoring and continuous improvement.