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US Justice Department and TikTok reach $500 million children's privacy settlement. The Justice Department sued TikTok and ByteDance in 2024 for allegedly failing to protect children's privacy, and illegally collecting their information. Published Aug 22, 2026, 04:54 AM Updated Aug 22, 2026, 04:54 AM * The US Justice Department and TikTok have reached a $500 million settlement over allegations of violating children's online privacy. * TikTok and ByteDance were accused of illegally collecting data from users under 13 without parental consent, violating the Children's Online Privacy Protection Act. * The lawsuit is dismissed with prejudice, and ByteDance agreed to form a majority American-owned joint venture to secure US user data and avoid a ban. AI generated WASHINGTON - The US Department of Justice and TikTok have reached a US$400 million (S$500 million) settlement to resolve allegations the short-video app violated children's online privacy. The Justice Department sued TikTok and ByteDance in 2024 for allegedly failing to protect children's privacy, and illegally collecting their information. The defendants were accused of violating the Children's Online Privacy Protection Act, which requires services aimed at children to obtain parental consent to collect personal information from users under age 13. The settlement was announced by the Justice Department. A court filing shows that its lawsuit against TikTok and TikTok parent ByteDance is being dismissed with prejudice. In January, ByteDance agreed to establish a majority American-owned joint venture to secure US data and avoid a US ban on the app used by more than 200 million Americans. REUTERS More on this topic. Full individual access, on any device From morning headlines to evening reads, enjoy unlimited access One Digital Monthly Recurring Billed monthly. Cancel any time Why subscribe? * Access to all premium articles on straitstimes.com * ST app access on 1 mobile device * E-paper with expanded view and 2-week archive

TikTok agrees to pay $400 million to settle alleged violation of children's privacy law. Aug 21, 2026, 06:28pm EDT Topline. TikTok and its parent company ByteDance agreed Friday to settle allegations it violated a digital child privacy law, meaning the company will fork over $400 million in its latest in its largest settlement to date. Key facts. TikTok will immediately pay $300 million under the terms of the settlement, according to the Justice Department, which said the company will pay another $100 million to vacate an older Musical.ly consent judgement (Musical.ly was the predecessor app to TikTok). The DOJ sued TikTok in 2024 claiming it illegally collected and kept data from children without notifying parents or receiving consent. TikTok was also accused of knowingly letting children create regular TikTok accounts, which are for ages 13 and up, which allowed them to post and share videos and message with adults. The Justice Department called the settlement "one of the largest recoveries ever obtained" in a case related to COPPA, the Children's Online Privacy Protection Act that requires websites and apps to get parental consent before collecting personal data. The Justice Department noted TikTok "has undergone significant changes to its ownership, management, compliance functions, and privacy practices," referencing the ownership deal inked last year in which ByteDance sold a majority stake in its U.S. operations to a group of American and global investors. TikTok did not admit to wrongdoing in the case. Big number. Roughly $500 million. That is how much ByteDance has agreed to pay in U.S. settlements since 2019. Key background. TikTok also agreed to a $92 million settlement in 2022, resolving claims it collected and profited from private user information including facial and geolocation data. The app was barred from collecting biometric and GPS data unless doing so was explicitly disclosed in its privacy policy. Prior to divesting its U.S. operations to American companies like Oracle, TikTok was repeatedly criticized for alleged links to the Chinese Communist Party. Reporting by Forbes also found ByteDance employees improperly used IP addresses and user data to spy on journalists reporting on the company. ByteDance also mishandled data from American companies and top influencers, the latter of which had their sensitive financial information stored in China. MidFirst Bank BRANDVOICE Paid Program MidFirst Bank's long-term operating philosophy has made it the largest privately owned bank in the nation and one of the strongest and best capitalized. Brand Contributor

Eleven AI models in twenty days! Releases have outrun anyone's ability to test them. Published: Aug 20, 2026, 04:27 IST Updated: Aug 20, 2026, 04:27 IST Story highlights August has produced eleven new AI models from seven providers, including Gemini 3.7 Flash three weeks after its predecessor, Grok 4.6, Meta's Muse Spark 1.2, ByteDance's Seed 2.1 Turbo and Z.AI's GLM-5.3. The release cadence now moves faster than independent evaluation, which means most models are chosen on the vendor's own benchmark numbers. Eleven new AI models have shipped this month, from seven different providers. The list is worth reading in sequence, because the pace is the point. Meta released Muse Spark 1.2 on August 6. xAI released Grok 4.6 the same day, then Grok Imagine Image 2.0 on August 8. ByteDance released Seed 2.1 Turbo on August 10. Google released Gemini 3.7 Flash on August 13, three weeks after Gemini 3.6 Flash. Z.AI released GLM-5.3 on August 14. You May Like That is six in nine days, from four countries, spanning text, image and video generation. Late July had already produced Moonshot's Kimi K3, a 2.8-trillion-parameter open-weight model, and the month before carried GPT-5.6 and Claude Opus 5. The Problem This Creates Evaluating a frontier model properly takes weeks. Independent benchmarking organisations have to obtain access, run standardised suites, check for contamination, and publish. Red-teaming for safety-relevant behaviour takes longer, because the interesting failures are rare and have to be hunted. A three-week gap between a model and its successor is shorter than that process. Gemini 3.7 Flash arrived before most independent assessment of Gemini 3.6 Flash had been published - which means the comparison being drawn is between a new model's vendor-reported numbers and an old model's vendor-reported numbers. The result is a market where purchasing decisions are made on self-reported benchmarks, because nothing else is available in time. Why Self-Reported Numbers Are A Problem This is not an accusation of fabrication. The issue is more mundane and harder to fix. A laboratory choosing which benchmarks to publish is choosing the ones its model does well on. Every laboratory does this, and every laboratory is telling the truth about the numbers it prints. The distortion is in the selection, not the arithmetic. Benchmark contamination compounds it. Models trained on internet-scale data may have absorbed the test sets, and detecting that requires access the vendor does not always grant. Independent evaluation exists precisely to catch these effects, and independent evaluation is what the release schedule is outpacing. There is recent precedent for how badly this can go. Meta faced sustained criticism over benchmark presentation earlier this year, and the episode demonstrated that the gap between a published score and a user's experience can be substantial enough to become a corporate crisis. What The Cadence Signals The compression is not arbitrary. It reflects where the competition has moved. Gemini 3.7 Flash gained sixteen points on DeepSWE v1.1 in three weeks - a jump inconsistent with a new base model and entirely consistent with post-training refinement of an existing one. Much of what is being shipped as a new release is a new fine-tune, which is genuinely faster to produce and genuinely cheaper to serve. Meanwhile the differentiators being marketed have shifted from capability to delivery. Google led on output speed and introductory price. OpenAI answered on the same day with a faster serving tier. Moonshot competes on open weights and self-hosting. These are distribution arguments, not intelligence arguments. When competition moves to distribution, release frequency becomes a marketing instrument in itself. Shipping often signals momentum to customers and investors regardless of what each individual release contains. What To Do About It For anyone selecting a model, the practical response is unglamorous. Treat vendor benchmarks as a claim about the vendor's priorities rather than a measure of fitness. Where independent numbers exist, weight them more heavily even when they are a generation behind. Run an internal evaluation on the actual task, which is the only benchmark that reflects the workload in question and the only one that cannot be gamed by someone else. And note which vendors publish evaluation methodology alongside results, and which publish only results. That distinction has become more informative than the scores. Eleven models in twenty days is a remarkable engineering achievement across the industry. It is also more new software than any customer can responsibly assess, arriving faster than the institutions built to assess it can work - and that gap is being filled, at present, by the sellers.