Human brain intertwining with computer circuitry, representing human-AI collaboration.

AI Decision-Making: Can Algorithms and Humans Truly Collaborate?

Nico Varela in Tech & Innovation December 2025 • 4 min read.

"Explore the pitfalls and potential of combining AI predictions with human judgment for better decisions."

In today's world, decisions are more complex than ever. To help, algorithms are being used in important areas like criminal justice, healthcare, and lending. These algorithms analyze data and make predictions, which are then used to make decisions either automatically or with the help of human agents.

While these human agents may have valuable knowledge, they can also have biases or limitations that don't line up with the algorithm. Take, for instance, a child protection service using an algorithm to assess risk. A social worker might have useful intuition, but also personal biases that affect their judgment. This raises the question: How should we design these algorithms to work effectively with human decision-makers?

New research offers insights into this challenge, revealing how to design algorithms and delegate decisions for better outcomes. The findings suggest that simply providing more information isn't always the best approach, and that policies like always having a 'human-in-the-loop' can sometimes make things worse. By understanding these dynamics, we can create more effective collaborations between humans and AI.

The Delicate Balance: When Should AI Decisions Be Handled by Humans?

Human brain intertwining with computer circuitry, representing human-AI collaboration.

The study models a scenario where a principal (like a manager or policymaker) designs an algorithm to predict a binary outcome (like whether a loan will be repaid or a patient will respond to treatment). The principal must then decide whether to act directly on the algorithm's prediction or delegate the decision to an agent who has private information but may be misaligned with the principal's goals.

The research reveals that delegation is only beneficial if the principal would make the same decision as the agent if they had access to the agent's private information. If the principal's and agent's interests aren't aligned, the principal might prefer to keep control of the decision. This highlights the importance of understanding the incentives and potential biases of human decision-makers when designing AI systems.

Delegation Improves Outcomes If: The principal would make the same decision as the agent with the agent's information.
Misalignment Risks: Delegation can be counterproductive if the agent's interests diverge from the principal's.
Understanding Human Factors: Incentives and potential biases of human decision-makers are crucial when designing AI systems.

Interestingly, the study also finds that providing the most comprehensive algorithm isn't always the optimal strategy, even if the principal can directly act on the algorithm's prediction. Instead, the ideal algorithm might focus on providing more information about one specific outcome while limiting information about others. This suggests that strategic information design is key to effective AI-assisted decision-making.

Avoiding the Pitfalls: Toward Better Human-AI Collaboration

The study cautions that well-intentioned policies, such as always including a human in the decision-making loop or aiming for maximum prediction accuracy, can sometimes worsen the quality of decisions. This is particularly true when there's a misalignment between the algorithm's goals and the human agent's incentives. The findings offer a possible explanation for why human-machine collaborations often underperform in real-world scenarios. By understanding these potential pitfalls and carefully designing algorithms and delegation strategies, we can harness the power of AI to make better, more informed decisions.

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.2402.09384,

Title: Persuasion, Delegation, And Private Information In Algorithm-Assisted Decisions

Subject: econ.th cs.ai cs.cy cs.gt cs.hc

Authors: Ruqing Xu

Published: 14-02-2024

Everything You Need To Know

What are the potential drawbacks of always including a human in the decision-making process when using AI?

According to the study, policies like always having a 'human-in-the-loop' can sometimes worsen decision quality. This can happen when there's a misalignment between the algorithm's goals and the human agent's incentives. For instance, a social worker's personal biases could influence their judgment when using an algorithm in a child protection service, leading to less effective decision-making. The study suggests that the effectiveness of human-AI collaboration depends on carefully designed algorithms and delegation strategies to avoid such pitfalls.

How does delegation to a human agent affect decision outcomes in AI-assisted systems, and under what conditions does it improve or worsen results?

Delegation to a human agent is only beneficial if the principal, such as a manager or policymaker, would make the same decision as the agent if they had access to the agent's private information. If the principal's and the agent's interests are not aligned, delegation can be counterproductive. This highlights the importance of understanding the incentives and potential biases of human decision-makers when designing AI systems. For example, if a loan officer has different incentives than the lending institution, delegation of decisions based on an AI's predictions might lead to undesirable outcomes.

What is the role of 'strategic information design' in AI-assisted decision-making, and why is it important?

Strategic information design involves tailoring the information provided by the algorithm to optimize decision-making. The study indicates that providing the most comprehensive algorithm isn't always the optimal strategy. Instead, the ideal algorithm might focus on providing more information about one specific outcome while limiting information about others. This approach recognizes that the way information is presented can significantly influence how humans interact with and utilize the AI's predictions, thereby affecting the overall quality of decisions.

What are the practical implications of the research findings for professionals in fields like criminal justice, healthcare, and lending where AI is used?

Professionals in these fields should carefully consider the incentives and biases of human agents and the alignment of goals between the algorithm and the human decision-maker. The research suggests that simply providing more information might not be the best approach. Instead, they should focus on designing algorithms and delegation strategies that account for potential misalignments. Understanding that policies like 'human-in-the-loop' are not universally beneficial is crucial, and they should evaluate each situation based on its specific dynamics to achieve effective human-AI collaboration and better decision outcomes.

How can we create more effective collaborations between humans and AI in decision-making, based on the insights provided?

To create more effective collaborations, it is crucial to understand the incentives and potential biases of human decision-makers. The ideal algorithm might focus on providing more information about one specific outcome while limiting information about others. The research also suggests that strategic information design is key to effective AI-assisted decision-making. This means carefully designing algorithms and delegation strategies, considering potential pitfalls, and ensuring that the human and AI are aligned in their goals to make better, more informed decisions.