Check out this answer from Consensus:
While algorithms have the potential to outperform humans in various decision-making tasks, their effectiveness is highly context-dependent. The integration of AI into human decision processes faces several challenges, including cognitive biases, trust issues, and the need for explainable and ethical AI. Future research should focus on addressing these challenges to harness the full potential of human-AI collaboration.
The advent of artificial intelligence (AI) has revolutionized various domains, from healthcare to finance, by providing sophisticated decision-making tools. However, the question remains: do algorithms truly outperform humans in decision-making tasks? This article explores the current research on human-AI collaboration, the effectiveness of AI in decision-making, and the challenges faced in integrating AI into human decision processes.
Human-AI Collaboration
Research indicates that human-AI teams can sometimes outperform either humans or AI alone, but this is not always guaranteed. One study highlights that the typical experimental setups often limit the potential of human-AI teams, especially when dealing with out-of-distribution examples. The study also found mixed results regarding interactive explanations, which can improve human perception of AI assistance but may also reinforce human biases, leading to limited performance improvement1.
The Role of Explainable AI
Explainable AI (XAI) has been a focal point in enhancing human decision-making. A meta-analysis of XAI studies found a statistically positive impact of XAI on user performance, particularly in tasks involving text data. However, the study also noted that explanations did not significantly improve performance compared to sole AI predictions, suggesting that the utility of XAI might be context-dependent2.
Government Decision-Making
In the context of government decision-making, algorithms have shown promise in aiding decision-makers. An experimental study comparing human decisions with and without algorithmic support found that algorithms help in making more correct decisions. However, even experienced decision-makers struggled to identify all mistakes made by the algorithms, indicating that understanding and traceability are crucial for effective AI adoption3.
Sequential Decision-Making
Machine learning algorithms can significantly improve human performance in sequential decision-making tasks. For instance, a novel algorithm designed to provide interpretable “tips” to users was shown to enhance performance in a virtual kitchen management task. Participants did not blindly follow the tips but combined them with their own experience to discover additional strategies, highlighting the potential for AI to augment human decision-making4 6.
Cognitive Challenges
Despite the potential benefits, cognitive challenges persist in human-AI collaboration. Research shows that humans often fail to delegate tasks effectively to AI, primarily due to a lack of metaknowledge about their own capabilities. This poor delegation performance is not due to algorithm aversion but rather an unconscious trait that limits effective collaboration8.
Algorithm Aversion and Trust
People’s trust in algorithms varies depending on the uncertainty of the decision domain. Studies have found that people are less likely to use algorithms in unpredictable domains, even if the algorithms outperform human judgment. This preference for human decision-making methods, despite their higher variance in performance, suggests a need for better understanding and communication of algorithmic capabilities7.
Ethical and Responsible AI
The principles of ethical and responsible AI are crucial for effective human-AI collaboration. Research has shown that people struggle to evaluate the accuracy of both their own and the AI’s predictions, leading to biased interactions and suboptimal decision-making. This underscores the need for comprehensive studies on the sociotechnical contexts in which people and algorithms interact9.
The Illusion of Understanding
A paradox exists in the perception of human versus algorithmic decision-making. While both are often black-box processes, people tend to believe they understand human decision-making better. This illusion of understanding can hinder the acceptance and effective use of algorithms, as people project their intuitive understanding more onto humans than machines10.
Do algorithms outperform us at decision making?
Scott E. Fahlman has answered Likely
An expert from Carnegie Mellon University in Artificial Intelligence
In many domains, yes. For example, chess is a decision-making process, and AI-based systems can now beat the world champion. Current AI systems cannot yet match human abilities where broad, common-sense knowledge is required, or when dealing with unpredictable, complicated humans.
Do algorithms outperform us at decision making?
David Tuffley has answered Likely
An expert from Griffith University in Artificial Intelligence, Software Science
A well written algorithm does have the potential to outperform the notoriously quirky and inconsistent decision making that many humans perform. But it really depends on how well it was written by a human in the first place. They just do what they are told to do.
Do algorithms outperform us at decision making?
Zdenka Kuncic has answered Likely
An expert from University of Sydney in Artificial Intelligence, Astrophysics
Yes, because our decisions are influenced by emotions.
Do algorithms outperform us at decision making?
Kay Kirkpatrick has answered Uncertain
An expert from University of Illinois at Urbana-Champaign in Mathematics, Artificial Intelligence
Sometimes yes, sometimes no. It’s recently becoming well understood in cognitive science that emotions can help humans make decisions well and quickly, and there’s nothing comparable to this in algorithms so far.
Do algorithms outperform us at decision making?
Mark Lee has answered Near Certain
An expert from Aberystwyth University in Computer Science
Yes, they certainly can! Consider programs that play games like chess or Go. These AI systems have now reached grandmaster level and have beaten the world’s best human players. The search algorithms used in chess can analyse millions of possible moves in seconds and this allows them to see much further ahead in the game than humans can manage. This produces excellent decisions and many game playing programs can now learn to play a game from scratch and quickly become champion players. However, games are a very constrained kind of problem. In a board game everything is well defined and consistent – nothing else happens other than pieces are moved! In the real world, in our human environment, there are all kinds of problems for algorithms to overcome. Just deciding where to take a holiday, for example, can be influenced by weather forecasts, geographic choices, travel convenience, personal preferences, interactions with family and others, calculations of acceptable budgets of time and cost, etc. These are not straightforward yes or no issues, unlike making a move in chess, but involve uncertainty, missing information, and value judgements. Algorithms that attempt to replace general human decision making have not been successful. AI research is working in many areas that help us to make decisions, perhaps the most useful is in providing relevant information, but the subtly of the human mind still beats machines in many ways. So, yes, algorithms can massively outperform humans, but only in certain limited contexts and restricted applications.