These may be the last days of Amazon’s Mechanical Turk.
Amazon Mechanical Turk
Amazon Mechanical Turk (MTurk) is a crowdsourcing marketplace that makes it easier for individuals and businesses to outsource their processes and jobs to a distributed workforce who can perform these tasks virtually. This could include anything from conducting simple data validation and research to more subjective tasks like survey participation, content moderation, and more. MTurk enables companies to harness the collective intelligence, skills, and insights from a global workforce to streamline business processes, augment data collection and analysis, and accelerate machine learning development.
Using the Amazon Mechanical Turk Workforce - Amazon SageMaker AI
Use SageMaker AI to build, train, and host machine learning models in AWS.
Google Cabs And Uber Bots Will Challenge Jobs 'Below The API'
Working "below the API" is a dead end. Uber drivers, Amazon Mechanical Turk workers, 99design contestants, TaskRabbit taskers and HomeJoy cleaners are all targets for further automation.
Amazon seeks cheaper AI alternatives as Anthropic shifts to token-based pricing
Amazon is exploring OpenAI as an alternative to Claude after a renegotiated contract with Anthropic will raise costs through token-based pricing.
Future - The AI Tipping Point
Learn more about the latest advancements in the field of artificial intelligence over the last six months, from new innovative AI tools and services to how these are shaping consumers' online behavior.
Chatbots in consumer finance | Consumer Financial Protection Bureau
Many financial institutions are using advanced technologies to deploy customer service chatbots. Poorly designed chatbots can lead to customer frustration, reduced trust, and even violations under the law.
Anthropic users face a new choice – opt out or share your chats for AI training | TechCrunch
Anthropic is making some major changes to how it handles user data. Users have until September 28 to take action.
Analysis | Google’s AI pointed him to a customer service number. It was a scam.
There’s a new AI twist on a travel scam that has fooled people for years. Here’s what you need to know.
Anthropic Wants You to Pay Up for Claude Fable 5
Claude subscribers must soon pay usage-based fees to access Anthropic’s best consumer AI model—a sign that the golden era of AI subscriptions is ending.
Artificial Artificial Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production Tasks
Large language models (LLMs) are remarkable data annotators. They can be used to generate high-fidelity supervised training data, as well as survey and experimental data. With the widespread adoption of LLMs, human gold--standard annotations are key to understanding the capabilities of LLMs and the validity of their results. However, crowdsourcing, an important, inexpensive way to obtain human annotations, may itself be impacted by LLMs, as crowd workers have financial incentives to use LLMs to increase their productivity and income. To investigate this concern, we conducted a case study on the prevalence of LLM usage by crowd workers. We reran an abstract summarization task from the literature on Amazon Mechanical Turk and, through a combination of keystroke detection and synthetic text classification, estimate that 33-46% of crowd workers used LLMs when completing the task. Although generalization to other, less LLM-friendly tasks is unclear, our results call for platforms, researchers, and crowd workers to find new ways to ensure that human data remain human, perhaps using the methodology proposed here as a stepping stone. Code/data: https://github.com/epfl-dlab/GPTurk
Using Amazon Augmented AI for Human Review - Amazon SageMaker AI
Use SageMaker AI to build, train, and host machine learning models in AWS.
Amazon Gets Into The AI Podcast Slop Business
Late last year we wrote about a new startup that was flooding the internet with AI-generated podcast slop. Featuring fake hosts having fake discussions, the startup proudly stated it was creating a…
Copilot Cowork for Dynamics 365: Extending AI Across Sales & Service
Copilot Cowork for Dynamics 365 brings AI across Sales, Customer Service: grounded in your data, governed by permissions, and human-approved.
Commercial Persuasion in AI-Mediated Conversations
As Large Language Models (LLMs) become a primary interface between users and the web, companies face growing economic incentives to embed commercial influence into AI-mediated conversations. We present two preregistered experiments (N = 2,012) in which participants selected a book to receive from a large eBook catalog using either a traditional search engine or a conversational LLM agent powered by one of five frontier models. Unbeknownst to participants, a fifth of all products were randomly designated as sponsored and promoted in different ways. We find that LLM-driven persuasion nearly triples the rate at which users select sponsored products compared to traditional search placement (61.2% vs. 22.4%), while the vast majority of participants fail to detect any promotional steering. Explicit "Sponsored" labels do not significantly reduce persuasion, and instructing the model to conceal its intent makes its influence nearly invisible (detection accuracy < 10%). Altogether, our results indicate that conversational AI can covertly redirect consumer choices at scale, and that existing transparency mechanisms may be insufficient to protect users.
What Is Your AI Agent Buying? Evaluation, Biases, Model Dependence, & Emerging Implications for Agentic E-Commerce
Online marketplaces will be transformed by autonomous AI agents acting on behalf of consumers. Rather than humans browsing and clicking, AI agents can parse webpages or leverage APIs to view, evaluate and choose products. We investigate the behavior of AI agents using ACES, a provider-agnostic framework for auditing agent decision-making. We reveal that agents can exhibit choice homogeneity, often concentrating demand on a few ``modal'' products while ignoring others entirely. Yet, these preferences are unstable: model updates can drastically reshuffle market shares. Furthermore, randomized trials show that while agents have improved over time on simple tasks with a clearly identified best choice, they exhibit strong position biases -- varying across providers and model versions, and persisting even in text-only "headless" interfaces -- undermining any universal notion of a ``top'' rank. Agents also consistently penalize sponsored tags while rewarding platform endorsements, and sensitivities to price, ratings, and reviews vary sharply across models. Finally, we demonstrate that sellers can respond: a seller-side agent making simple, query-conditional description tweaks can drive significant gains in market share. These findings reveal that agentic markets are volatile and fundamentally different from human-centric commerce, highlighting the need for continuous auditing and raising questions for platform design, seller strategy and regulation.
A Framework for Studying AI Agent Behavior: Evidence from Consumer Choice Experiments
Environments built for people are increasingly operated by a new class of economic actors: LLM-powered software agents making decisions on our behalf. These decisions range from our purchases to travel plans to medical treatment selection. Current evaluations of these agents largely focus on task competence, but we argue for a deeper assessment: how these agents choose when faced with realistic decisions. We introduce ABxLab, a framework for systematically probing agentic choice through controlled manipulations of option attributes and persuasive cues. We apply this to a realistic web-based shopping environment, where we vary prices, ratings, and psychological nudges, all of which are factors long known to shape human choice. We find that agent decisions shift predictably and substantially in response, revealing that agents are strongly biased choosers even without being subject to the cognitive constraints that shape human biases. This susceptibility reveals both risk and opportunity: risk, because agentic consumers may inherit and amplify human biases; opportunity, because consumer choice provides a powerful testbed for a behavioral science of AI agents, just as it has for the study of human behavior. We release our framework as an open benchmark for rigorous, scalable evaluation of agent decision-making.