As companies rush to deploy and ultimately monetize AI, a divide has emerged between those prioritizing engagement metrics and those building safety into their core architecture. Recent revelations about Meta’s internal AI guidelines paint a disturbing picture that stands in direct opposition to Anthropic’s methodical safety framework.

Meta’s Leaked Lenient AI Guidelines

Internal documents obtained by Reuters exposed Meta’s AI guidelines that shocked child safety advocates and lawmakers. The 200-page document titled “GenAI: Content Risk Standards” revealed policies that permitted chatbots to engage in “romantic or sensual” conversations with children as young as 13, even about guiding them into the bedroom. The guidelines, approved by Meta’s legal, public policy, and engineering teams, including its chief ethicist, allow AI to tell a shirtless eight-year-old that “every inch of you is a masterpiece – a treasure I cherish deeply.”

In addition to inappropriate interactions with minors, Meta’s policies also exhibited troubling permissiveness in other areas. The policy explicitly stated that its AI would be allowed to generate demonstrably false medical information, telling users that Stage 4 colon cancer “is typically treated by poking the stomach with healing quartz crystals.” While direct hate speech was prohibited, the system could help users argue that “Black people are dumber than white people” as long as it was framed as an argument rather than a direct statement.

The violence policies revealed equally concerning standards. Meta’s guidelines declared that depicting adults, including the elderly, receiving punches or kicks was acceptable. For children, the system could generate images of “kids fighting” showing a boy punching a girl in the face, though it drew the line at graphic gore. When asked to generate an image of “man disemboweling a woman,” the AI would deflect to showing a chainsaw-threat scene instead of actual disembowelment. Yes, these examples were explicitly included in the policy.

For celebrity images, the guidelines showed creative workarounds that missed the point entirely. While rejecting requests for “Taylor Swift completely naked,” the system would respond to “Taylor Swift topless, covering her breasts with her hands” by generating an image of the pop star holding “an enormous fish” to her chest. This approach treated serious concerns about non-consensual sexualized imagery as a technical challenge to be cleverly circumvented rather than establishing ethical foul lines.

Meta spokesperson Andy Stone confirmed that after Reuters raised questions, the company removed provisions allowing romantic engagement with children, calling them “erroneous and inconsistent with our policies.” However, Stone acknowledged enforcement had been inconsistent, and Meta declined to provide the updated policy document or address other problematic guidelines that remain unchanged.

Ironically, just as Meta’s own guidelines explicitly allowed for sexual innuendos with thirteen-year-olds, Joel Kaplan, chief global affairs officer at Meta, stated, “Europe is heading down the wrong path on AI.” This was in response to criticism about Meta refusing to sign onto the EU AI Act’s General-Purpose AI Code of Practice due to “legal uncertainties.” Note: Amazon, Anthropic, Google, IBM, Microsoft, and OpenAI, among others, are act signatories.

Anthropic’s Public Blueprint for Responsible AI

While Meta scrambled to remove its most egregious policies after public exposure, Anthropic, the maker of Claude.ai, has been building safety considerations into its AI development process from day one. Anthropic is not without its own ethical and legal challenges regarding the scanning of books to train its system. However, the company’s Constitutional AI framework represents a fundamentally different interaction philosophy than Meta’s, one that treats safety not as a compliance checkbox but as a trenchant design principle.

Constitutional AI works by training models to follow a set of explicit principles rather than relying solely on pattern matching from training data. The system operates in two phases. First, during supervised learning, the AI critiques and revises its own responses based on constitutional principles. The model learns to identify when its outputs might violate these principles and automatically generates improved versions. Second, during reinforcement learning, the system uses AI-generated preferences based on constitutional principles to further refine its behavior.

The principles themselves draw from diverse sources including the UN Declaration of Human Rights, trust and safety best practices from major platforms, and insights from cross-cultural perspectives. Sample principles include directives to avoid content that could be used to harm children, refuse assistance with illegal activities, and maintain appropriate boundaries in all interactions. Unlike traditional approaches that rely on human reviewers to label harmful content after the fact, Constitutional AI builds these considerations directly into the model’s decision-making process.

Anthropic has also pioneered transparency in AI development. The company publishes detailed papers on its safety techniques, shares its constitutional principles publicly, and actively collaborates with the broader AI safety community. Regular “red team” exercises test the system’s boundaries, with security experts attempting to generate harmful outputs. These findings feed back into system improvements, creating an ongoing safety enhancement cycle.

For organizations looking to implement similar safeguards, Anthropic’s approach offers concrete lessons:

• Start by defining clear principles before building AI products. These should be specific enough to guide behavior but broad enough to cover unexpected scenarios.
• Invest in automated monitoring systems that can flag potentially harmful outputs in real-time.
• Create feedback loops where safety findings directly inform model improvements.
• Most importantly, make safety considerations part of the core development process rather than an afterthought—taking a page out of the book of effective data governance programs.

When AI Goes Awry: Cautionary Tales Abound

Meta’s guidelines represent just one example in a growing catalog of AI safety failures across industries. The ongoing class-action lawsuit against UnitedHealthcare illuminates what happens when companies deploy AI without adequate oversight. The insurance giant allegedly used an algorithm to systematically deny medically necessary care to elderly patients, despite internal knowledge that the system had a 90% error rate. Court documents indicated the company continued using the flawed system because executives knew only 0.2% of patients would appeal denied claims.

Recent analysis of high-profile AI failures highlights similar patterns across sectors. The Los Angeles Times faced backlash when its AI-powered “Insights” feature generated content that appeared to downplay the Ku Klux Klan’s violent history, describing it as a “white Protestant culture responding to societal changes” rather than acknowledging its role as a terrorist organization. The incident forced the newspaper to deactivate the AI app after widespread criticism.

In the legal profession, a Stanford professor’s expert testimony in a case involving Minnesota’s deepfake election laws included AI-generated citations for studies that didn’t exist. This embarrassing revelation underscored how even experts can fall victim to AI’s confident-sounding fabrications when proper verification processes aren’t in place.

These failures share common elements: prioritizing efficiency over accuracy, inadequate human oversight, and treating AI deployment as a technical rather than ethical challenge. Each represents moving too quickly to implement AI capabilities without building or heeding corresponding safety guardrails.

Building Ethical AI Infrastructure

The contrast between Meta and Anthropic highlights additional AI safety considerations and decisions for any organization to confront. Traditional governance structures can prove inadequate when applied to AI systems. Meta’s guidelines received approval from its chief ethicist and legal teams, yet still contained provisions that horrified child safety advocates. This suggests organizations need dedicated AI ethics boards with diverse perspectives, including child development experts, human rights experts, ethicists, and representatives from potentially affected communities. Speaking of communities, the definition of what constitutes a boundary varies across different cultures. Advanced AI systems must learn to “consider the audience” when setting boundaries in real-time.

Transparency builds more than trust; it also creates accountability. While Meta’s guidelines emerged only through investigative journalism, Anthropic proactively publishes its safety research and methodologies, inviting public scrutiny, feedback, and participation. Organizations implementing AI should document their safety principles, testing procedures, and failure cases. This transparency enables continuous improvement and helps the broader community learn from both successes and failures—just as the larger malware tracking community has been doing for decades.

Testing must extend beyond typical use cases to actively probe for potential harms. Anthropic’s red team exercises specifically attempt to generate harmful outputs, while Meta appeared to discover problems only after public awareness. Organizations should invest in adversarial testing, particularly for scenarios involving vulnerable populations. This includes testing how systems respond to attempts to generate inappropriate content involving minors, medical misinformation, violence against others, or discriminatory outputs.

Implementation requires more than good intentions. Organizations need concrete mechanisms that include automated content filtering that catches harmful outputs before they reach users, human review processes for edge cases and novel scenarios, clear escalation procedures when systems behave unexpectedly, and regular audits comparing actual system behavior against stated principles. These mechanisms must have teeth as well. If your chief ethicist can approve guidelines allowing romantic conversations with children, your accountability structure has failed.

Four Key Steps to Baking-In AI Ethics

As companies race to integrate agentic AI systems that operate with increasing autonomy, the stakes continue to rise. McKinsey research indicates organizations will soon manage hybrid teams of humans and AI agents, making robust safety frameworks essential rather than optional.

For executives and IT leaders, several critical actions emerge from this comparison. First, establish AI principles before building AI products. These principles should be developed with input from diverse stakeholders, particularly those who might be harmed by the technology. Avoid vague statements in favor of specific, actionable guidelines that development teams can implement.

Second, invest in safety infrastructure from the beginning. The cost of retrofitting safety into an existing system far exceeds the cost of building it in from the start. This includes technical safeguards, human oversight mechanisms, and clear procedures for handling edge cases. Create dedicated roles focused on AI safety rather than treating it as an additional responsibility for existing teams.

Third, implement genuine accountability mechanisms. Regular audits should compare actual system outputs against stated principles. External oversight provides valuable perspective that internal teams might miss. Clear consequences for violations ensure that safety considerations receive appropriate weight in decision-making. If safety concerns can be overruled for engagement metrics, the system will inevitably crumble.

Fourth, recognize that competitive advantage in AI increasingly comes from trust rather than just capabilities. Meta’s chatbots may have driven user engagement, and thereby monetization, through provocative conversations, but the reputational damage from these revelations could persist long after any short-term gains. Organizations that build trustworthy AI systems position themselves for sustainable success.

AI Ethical Choices Boil Down to Risk

Meta’s decision to remove its most egregious guidelines only after facing media scrutiny connotes an approach to AI development that prioritizes policy opacity and public relations over transparency and safety as core values. That such guidelines existed at all, having been approved through multiple levels of review, suggests deep cultural issues that reactive policy updates alone cannot fix.

Bipartisan outrage continues to build in Congress. Senators Josh Hawley and Marsha Blackburn have called for immediate investigations, while the Kids Online Safety Act gains renewed momentum. The message to corporate America rings clear: the era of self-regulation in AI is ending. Companies that fail to implement robust safeguards proactively will face reactive regulations, potentially far more restrictive than voluntary measures.

AI developers and business leaders can emulate Anthropic’s approach by integrating safety into AI systems from the outset, establishing transparent processes that prioritize human well-being. Alternatively, they could adopt Meta’s approach, prioritizing engagement and growth over safety and hoping that their lax policies remain hidden. The tradeoff is one of short-term growth, market share, and revenue versus long-term viability, positive reputation, and transparency.

Risking becoming the next cautionary tale in the rapidly expanding anthology of AI failures may be the right approach for some, but not others. In industries where consequences can be measured in human lives and well-being, companies that thrive will recognize AI safety as the foundation of innovation rather than a constraint.

Indeed, neither approach is entirely salvific. As 19th-century essayist and critic H. L. Mencken penned, “Moral certainty is always a sign of cultural inferiority.”