ibl.ai Agentic AI Blog

Insights on building and deploying agentic AI systems. Our blog covers AI agent architectures, LLM infrastructure, MCP servers, enterprise deployment strategies, and real-world implementation guides. Whether you are a developer building AI agents, a CTO evaluating agentic platforms, or a technical leader driving AI adoption, you will find practical guidance here.

Topics We Cover

Featured Research and Reports

We analyze key research from leading institutions and labs including Google DeepMind, Anthropic, OpenAI, Meta AI, McKinsey, and the World Economic Forum. Our content includes detailed analysis of reports on AI agents, foundation models, and enterprise AI strategy.

For Technical Leaders

CTOs, engineering leads, and AI architects turn to our blog for guidance on agent orchestration, model evaluation, infrastructure planning, and building production-ready AI systems. We provide frameworks for responsible AI deployment that balance capability with safety and reliability.

Back to Blog

University College London: How Human-AI Feedback Loops Alter Human Perceptual, Emotional and Social Judgements

Jeremy WeaverFebruary 18, 2025
Premium

This study finds that AI systems can amplify human biases when trained on slightly skewed data. Interactions with biased AI can further increase human bias, particularly when users view AI as more authoritative. However, accurate AI systems have the potential to improve human judgment.

University College London: How Human-AI Feedback Loops Alter Human Perceptual, Emotional and Social Judgements



Summary of Read Full Report

This research investigates how interactions between humans and AI can create feedback loops that amplify biases.The study reveals that AI algorithms, trained on slightly biased human data, not only adopt these biases but also magnify them.

When humans then interact with these biased AI systems, their own biases increase, demonstrating a concerning feedback mechanism. The researchers found this effect to be stronger in human-AI interactions than in human-human interactions, and that humans often underestimate the influence of AI on their judgments.

The study demonstrated that using an AI system like Stable Diffusion can increase social bias. Critically, the study shows that accurate AI can improve judgement, while flawed AI amplifies human biases.

Here are five key takeaways from the provided study on human-AI interaction:

  • AI systems can amplify biases present in human data. When AI algorithms are trained on data that contains even slight human biases, the algorithms not only adopt these biases but often amplify them.
  • Human interaction with biased AI increases human bias. Repeated interaction with biased AI systems leads humans to internalize and adopt the AI's biases, potentially creating a feedback loop where human judgment becomes increasingly skewed. This effect is stronger in human-AI interactions than in human-human interactions.
  • The perception of AI influences its impact. Humans may be more susceptible to bias from AI systems if they perceive the AI as superior or authoritative. The study showed that even when interacting with an AI, if participants believed they were interacting with a human, the bias learned was less than if they knew it was an AI.
  • Humans underestimate AI's biasing influence. People are often unaware of the extent to which AI systems affect their judgments, which can make them more vulnerable to adopting AI-driven biases.
  • Accurate AI improves human judgment. The study also demonstrated that interaction with accurate AI systems can improve human decision-making, suggesting that reducing algorithmic bias has the potential to enhance the quality of human judgment.

Why does owning the AI stack matter?

ibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing β€” so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.

  • You own all the code and the data

    Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform β€” the stack itself is yours.

  • Model-agnostic

    Run any LLM β€” Claude, GPT, Gemini, Llama, Command, or your own fine-tune β€” and switch providers without rewriting the platform.

  • No per-seat pricing

    Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ.

  • Deploy anywhere

    Your cloud, your VPC, on-premise, GovCloud, or a fully air-gapped network with no outbound connectivity.

1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.

ibl.ai is family-owned and operated from New York, NY β€” a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.

See the ibl.ai AI Operating System in Action

Discover how leading universities and organizations are transforming education with the ibl.ai AI Operating System. Explore real-world implementations from Harvard, MIT, Stanford, and users from 400+ institutions worldwide.

View Case Studies
Work with our team

Pilots, deployment, and full ownership

Most enterprise engagements are one-time, not subscriptions. You integrate ibl.ai with your own data, deploy it on your own infrastructure, and the engineering hours scale with the work β€” so the price tracks the scope, not your headcount.

Start here

Pilot

from $15K

fixed scope Β· fixed timeline

A time-boxed proof of value on your real data β€” not a slide deck.

Best for: Teams that want to see ibl.ai working before committing.

  • Deployed on your infrastructure or our cloud
  • 1–2 production agents wired to a slice of your data
  • One integration (LMS / SIS / SSO / data source)
  • Weekly working sessions with our engineers
  • Pilot fee credits toward a full engagement
Scope a pilot
Most common

Integration & Deployment

$25K – $80K

one-time Β· not a subscription

Full deployment integrated with your data and systems. Engineering hours scale with scope.

Best for: Organizations rolling ibl.ai out across a department, campus, or business unit.

  • Platform deployed in your VPC, on-prem, or air-gapped
  • Integrated with your data + identity (SSO / SAML)
  • Multiple custom agents built to your workflows
  • Engineering hours proportional to scope
  • You own the data Β· run any LLM you choose
Plan a deployment
Full ownership

Codebase Transfer + Custom AI Engineering

Six figures

perpetual license Β· you own the stack

We transfer the full source code. You own and self-host the entire platform β€” outright.

Best for: Government, defense, and enterprises that require perpetual ownership and sovereignty.

  • Complete source-code transfer + perpetual license
  • Dedicated AI engineering team on your roadmap
  • Custom agents, models, and integrations to spec
  • Air-gapped capable Β· zero vendor lock-in
  • Family-owned, New York–based long-term partner
Talk about ownership
You own the code and data Run any LLM β€” Claude, GPT, Gemini, Llama Family-owned & operated from New York, NY