A human brain intertwined with algorithmic code, symbolizing human-AI collaboration.

Human or Algorithm? How to Design AI That Works With You, Not Against You

Theo Raines in Tech & Innovation December 2025 • 3 min read.

"Unlock the secret to successful AI integration: Designing algorithms that complement human skills for better decisions."

In today's fast-paced world, algorithms are increasingly used to assist human decision-makers. However, the focus is often on simply predicting outcomes, overlooking the crucial aspect of how these algorithms affect human choices. This oversight can lead to AI systems that hinder rather than help.

Imagine a hiring manager using an AI tool to screen candidates. If the AI only focuses on predicting job performance based on past data, it might miss the nuances of human potential and fit. The manager, influenced by the AI's recommendations, might make worse decisions than if they relied solely on their judgment.

The key to successful AI integration lies in designing algorithms that complement human skills. By understanding how people respond to recommendations and incorporating this knowledge into the design process, we can create AI systems that enhance decision-making and lead to better outcomes.

The Framework for Human-AI Harmony: Understanding Potential Outcomes

A human brain intertwined with algorithmic code, symbolizing human-AI collaboration.

To design effective AI recommendation systems, we need a framework that models the impact of recommendations on human decisions. This framework leverages the potential-outcomes approach from causal inference, which allows us to analyze how different recommendations influence a decision-maker's binary choice (e.g., hire/not hire).

At the heart of this framework is the concept of "potential decisions." This refers to the decisions a person would make when receiving different recommendations from the algorithm. Instead of assuming that people blindly follow AI advice, we acknowledge that they may comply, ignore, or even defy recommendations based on their own information and preferences.

Compliance: The decision-maker follows the algorithm's recommendation.
Ignore: The decision-maker disregards the algorithm's recommendation and relies on their own judgment.
Defy: The decision-maker actively goes against the algorithm's recommendation.
Change: The decision-maker alters their decision in response to the algorithm's input.

By classifying human responses in this way, we can design algorithms that account for how people are likely to react to different recommendations. This leads to AI systems that are more effective at guiding decisions and improving outcomes.

Embracing the Future of Human-AI Collaboration

Designing AI systems that complement human skills is not just a theoretical exercise; it's a practical necessity. By understanding how people respond to recommendations and incorporating this knowledge into the design process, we can create AI tools that empower humans to make better decisions and achieve more successful outcomes. The future of AI lies not in replacing humans, but in working alongside them to unlock our full potential.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2405.01484,

Title: Designing Algorithmic Recommendations To Achieve Human-Ai Complementarity

Subject: cs.hc cs.lg econ.em stat.ml

Authors: Bryce Mclaughlin, Jann Spiess

Published: 02-05-2024

Everything You Need To Know

What is the core concept behind designing AI that works with humans, and why is it important?

The core concept is designing algorithms that complement human skills. This is crucial because AI systems often focus solely on predicting outcomes without considering how they affect human choices. If AI recommendations are not designed with human responses in mind, they can hinder decision-making. A hiring manager, for example, might make worse decisions by blindly following an AI's recommendations that doesn't account for the nuances of human potential. By understanding how people respond to recommendations, we can create AI systems that enhance decision-making.

How does the potential-outcomes approach contribute to the design of effective AI recommendation systems?

The potential-outcomes approach from causal inference allows us to analyze how different recommendations influence a decision-maker's binary choice. This framework is essential because it models the impact of recommendations on human decisions. It helps move beyond the assumption that people blindly follow AI advice, by acknowledging that people may react differently. By understanding how people respond to AI recommendations, such as through 'Compliance', 'Ignore', 'Defy', or 'Change', we can design algorithms that account for these varied responses.

Can you explain the different ways a human can respond to an AI's recommendation, according to the provided framework?

The framework outlines four ways a decision-maker can respond: 'Compliance' where the decision-maker follows the algorithm's recommendation; 'Ignore' where the decision-maker disregards the recommendation and uses their own judgment; 'Defy' where the decision-maker goes against the recommendation; and 'Change' where the decision-maker alters their decision in response to the AI's input. Understanding these responses allows for more effective AI design.

Why is it important to move beyond AI that simply predicts outcomes?

It's crucial to move beyond AI that simply predicts outcomes because such AI systems often overlook the crucial aspect of how these algorithms affect human choices. Focusing solely on prediction can lead to AI systems that hinder rather than help human decision-making. An example is when a hiring manager relies solely on AI recommendations without accounting for human potential, leading to possibly worse decisions. Designing AI with an understanding of human responses to recommendations leads to better outcomes.

What is the ultimate goal of designing AI systems that work with humans, and what are the implications of not doing so?

The ultimate goal is to empower humans to make better decisions and achieve more successful outcomes by creating AI tools that complement human skills. The implications of not designing AI in this way are significant. If AI systems are not designed with human responses in mind, they can hinder decision-making. Ignoring human factors can lead to worse decisions, missed opportunities, and ultimately, AI systems that fail to achieve their intended purpose. The future of AI lies in collaboration, not replacement, unlocking our full potential by working alongside humans.