TNB Tech Minute: Reddit Sues Anthropic, Alleging Unauthorized Data Use - Tech News Briefing
This transcript was prepared by a transcription service. This version may not be in its final form and may be updated.
Julie Chang: Here's your TNB Tech minute for Wednesday, June 4th. I'm Julie Chang for the Wall Street Journal. Reddit is suing Anthropic, saying The AI startup used the site's data for commercial purposes without a licensing agreement violating user data policy. The lawsuit references a 2021 Anthropic research paper that details the usefulness of Reddit data in AI model training. Reddit has licensing deals with OpenAI and Google, but said it had tried and failed to reach an agreement with Anthropic. A spokeswoman for Anthropic said the company disagrees with Reddit's claims and will defend itself vigorously. Meta is in talks with Disney, A24, and other smaller production companies to feature exclusive content on its new premium virtual reality headset to be launched next year. Thus, according to people familiar with the matter, the new device known internally as Loma is set to rival Apple's Vision Pro. Meta is offering millions of dollars for episodic and standalone immersive video based on well-known intellectual property. A Meta spokesman referred to comments by its CTO about the company working on many prototypes, not all of which go into production. CrowdStrike says its cooperating with federal authorities in connection with an incident last July, when a bug in the company's software knocked millions of computers offline. The outage delayed thousands of flights, broke backend systems, and rendered laptops temporarily unusable. According to a filing with the Securities and Exchange Commission, the cybersecurity firms at the Justice Department and the SEC have requested information related to the incident and other matters. Shares of CrowdStrike fell more than 5% today. For a deeper dive into what's happening in tech, check out Thursday's Tech News Briefing podcast.
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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.'
