{"id":316514,"date":"2025-11-24T21:42:51","date_gmt":"2025-11-24T21:42:51","guid":{"rendered":"https:\/\/peraltafinancing.com\/analytics\/ai-horizons-safety-ethics-and-data-rights-in-the-age-of-ai\/"},"modified":"2025-11-24T21:42:51","modified_gmt":"2025-11-24T21:42:51","slug":"ai-horizons-safety-ethics-and-data-rights-in-the-age-of-ai","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=316514","title":{"rendered":"AI Horizons: Safety, Ethics, and Data Rights in the Age of AI"},"content":{"rendered":"


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AI systems are built on data. Every Large Language Model (LLM), from chatbots to medical tools, learns from vast collections of text, images, and domain-specific material. They depend on high-quality, often human-labeled datasets to stay accurate and adaptable. Without them, models risk becoming brittle, biased, and subject to the familiar rule: \u201cgarbage in, garbage out.\u201d<\/p>\n

At Wharton Human-AI Research\u2019s (WHAIR)<\/a> third annual Business and Generative AI Conference<\/a> in San Francisco, Luyang Zhang<\/a>, PhD student at Carnegie Mellon University,\u00a0presented his paper \u201cFair Share Data Pricing: Data Valuation for Large Language Models,\u201d which directly addresses this topic. Beibei Li<\/a>, Professor of IT and Management at Carnegie Mellon University, and the paper\u2019s co-author, joined us to kick off our first episode of this year\u2019s AI Horizons webinar series.<\/p>\n

In conversation with Lynn Wu, Associate Professor of Operations, Information and Decisions at the Wharton School, Li \u2013 whose work in this area bridges data science, AI, and human behavior, and has been recognized by organizations like Google, Adobe, and the Marketing Science Institute \u2013 explained how data, especially human-labeled data, drives AI progress. She also details why today\u2019s data markets are both unfair and unsustainable. Here are the key lessons for business leaders.<\/p>\n

Human-labeled data is still essential<\/strong><\/h3>\n

Generative AI can create synthetic data, but it can\u2019t fully replace human input. Without real examples created by people, LLMs struggle with specialized tasks like medical diagnosis or legal advice. As Li put it, \u201cIf you keep feeding a model with synthetic data, it can go into a bubble.\u201d Businesses need to plan for the continued importance and cost of quality human-labeled datasets.<\/p>\n

Today\u2019s data markets underpay workers and hurt companies<\/strong><\/h3>\n

Many data labelers earn about $2 an hour, even for complex tasks. This \u201cgig work\u201d system saves money in the short run but drives away skilled workers, lowering data quality. This, in turn, results in weaker AI models. \u201cEven if you\u2019re well-funded, you can\u2019t find high-quality supplies,\u201d Li warned.<\/p>\n

Data should be priced by its true value<\/strong><\/h3>\n

Li\u2019s team tested ways to measure how much a dataset improves model performance. If adding a dataset boosts accuracy significantly, it should be priced higher than one that has little impact. Their simulations showed this approach raises pay for workers while giving buyers better data over time.<\/p>\n

Transparency helps smaller players compete<\/strong><\/h3>\n

Big tech companies can afford to buy up all kinds of data \u2013 even low-quality sets. Startups can\u2019t. Transparent pricing helps smaller firms target the highest-value data and stretch their budgets further. Li called this \u201cdemocratizing access,\u201d since it prevents innovation from being concentrated in just a few large players.<\/p>\n

Fairness and sustainability are on the line<\/strong><\/h3>\n

Many data workers live in low-income countries and rarely see the value of their contributions. Fair pricing makes their work visible and combats what Li calls \u201cdata colonialism,\u201d where richer nations take advantage of poorer regions. For policymakers, fairer pricing isn\u2019t just about ethics, it\u2019s about ensuring a steady supply of quality data to keep AI progress moving.<\/p>\n

Both policymakers and companies have a role<\/strong><\/h3>\n

Governments could adopt fair data pricing in public-sector projects like healthcare, while companies could test royalty-style models where labelers share in future economic value. Together, these steps could build a healthier data market.<\/p>\n

Why It Matters<\/strong><\/h3>\n

AI systems are only as good as the data they\u2019re trained on. Companies that treat data labeling as cheap gig work risk undermining their own products. By paying fairly and transparently, organizations can get better-quality data, strengthen their AI models, and support a more sustainable and ethical AI ecosystem. In Li\u2019s words, this isn\u2019t just a fairness issue \u2013\u00a0it\u2019s an \u201cinnovation issue.\u201d<\/p>\n<\/p><\/div>\n\n","protected":false},"excerpt":{"rendered":"

AI systems are built on data. Every Large Language Model (LLM), from chatbots to medical tools, learns from vast collections of text, images, and domain-specific material. They depend on high-quality, often human-labeled datasets to stay accurate and adaptable. Without them, models risk becoming brittle, biased, and subject to the familiar rule: \u201cgarbage in, garbage out.\u201d […]<\/p>\n","protected":false},"author":1,"featured_media":316515,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12033],"tags":[1082,11603,16127,19333,13922,417],"dealstore":[],"offerexpiration":[],"class_list":["post-316514","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","tag-age","tag-data","tag-ethics","tag-horizons","tag-rights","tag-safety"],"yoast_head":"\nAI Horizons: Safety, Ethics, and Data Rights in the Age of AI - Som2ny Network<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fivemor.com\/?p=316514\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI Horizons: Safety, Ethics, and Data Rights in the Age of AI - Som2ny Network\" \/>\n<meta property=\"og:description\" content=\"AI systems are built on data. 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