# Learning When to Advise Human Decision Makers

arXiv: https://arxiv.org/abs/2209.13578 · HTML: https://mlsanity.com/paper/2209.13578/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 103 references that are in the map.
- First posted on arXiv 27 Sep 2022.

## Builds on (most cited first, 29 of 29)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2001.02114/) (2020)
- [Consistent Estimators for Learning to Defer to an Expert](https://mlsanity.com/paper/2006.01862/) (2020)
- [Strategic Classification](https://mlsanity.com/paper/1506.06980/) (2015)
- [Manipulating and Measuring Model Interpretability](https://mlsanity.com/paper/1802.07810/) (2018)
- [Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer](https://mlsanity.com/paper/1711.06664/) (2017)
- [Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models](https://mlsanity.com/paper/1708.08296/) (2017)
- [Predictive Multiplicity in Classification](https://mlsanity.com/paper/1909.06677/) (2019)
- [On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection](https://mlsanity.com/paper/1811.07901/) (2018)
- [Strategic Classification from Revealed Preferences](https://mlsanity.com/paper/1710.07887/) (2017)
- [Learning to Complement Humans](https://mlsanity.com/paper/2005.00582/) (2020)
- [Explainable deep learning models in medical image analysis](https://mlsanity.com/paper/2005.13799/) (2020)
- [Machine Learning with a Reject Option: A survey](https://mlsanity.com/paper/2107.11277/) (2021)
- [Explainability in Deep Reinforcement Learning](https://mlsanity.com/paper/2008.06693/) (2020)
- ["Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans](https://mlsanity.com/paper/2001.05871/) (2020)
- [Strategic Classification in the Dark](https://mlsanity.com/paper/2102.11592/) (2021)
- [Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Making](https://mlsanity.com/paper/2101.05303/) (2021)
- [Deepfake Detection by Human Crowds, Machines, and Machine-informed Crowds](https://mlsanity.com/paper/2105.06496/) (2021)
- [The Flaws of Policies Requiring Human Oversight of Government Algorithms](https://mlsanity.com/paper/2109.05067/) (2021)
- [Optimum Statistical Estimation with Strategic Data Sources](https://mlsanity.com/paper/1408.2539/) (2014)
- [Auctions Between Regret-Minimizing Agents](https://mlsanity.com/paper/2110.11855/) (2021)
- [How and Why to Manipulate Your Own Agent: On the Incentives of Users of Learning Agents](https://mlsanity.com/paper/2112.07640/) (2021)
- [Bid Prediction in Repeated Auctions with Learning](https://mlsanity.com/paper/2007.13193/) (2020)
- [Explainable Reinforcement Learning via Model Transforms](https://mlsanity.com/paper/2209.12006/) (2022)
- [Behavior-Based Machine-Learning: A Hybrid Approach for Predicting Human Decision Making](https://mlsanity.com/paper/1611.10228/) (2016)
- [Neural Networks for Predicting Human Interactions in Repeated Games](https://mlsanity.com/paper/1911.03233/) (2019)
- [Finding Safe Zones of policies Markov Decision Processes](https://mlsanity.com/paper/2202.11593/) (2022)

## Built on by (most cited first, 18 of 18)

- [When combinations of humans and AI are useful: A systematic review and meta-analysis](https://mlsanity.com/paper/2405.06087/) (2024)
- [Accuracy-Time Tradeoffs in AI-Assisted Decision Making under Time Pressure](https://mlsanity.com/paper/2306.07458/) (2023)
- [Learning Personalized Decision Support Policies](https://mlsanity.com/paper/2304.06701/) (2023)
- [Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skills](https://mlsanity.com/paper/2410.04253/) (2024)
- [Towards Optimizing Human-Centric Objectives in AI-Assisted Decision-Making With Offline Reinforcement Learning](https://mlsanity.com/paper/2403.05911/) (2024)
- [AI-Assisted Decision Making with Human Learning](https://mlsanity.com/paper/2502.13062/) (2025)
- [Moral Change or Noise? On Problems of Aligning AI With Temporally Unstable Human Feedback](https://mlsanity.com/paper/2511.10032/) (2025)
- [Designing Algorithmic Recommendations to Achieve Human-AI Complementarity](https://mlsanity.com/paper/2405.01484/) (2024)
- [Human Misperception of Generative-AI Alignment: A Laboratory Experiment](https://mlsanity.com/paper/2502.14708/) (2025)
- [Decongestion by Representation: Learning to Improve Economic Welfare in Marketplaces](https://mlsanity.com/paper/2306.10606/) (2023)
- [User Friendly and Adaptable Discriminative AI: Using the Lessons from the Success of LLMs and Image Generation Models](https://mlsanity.com/paper/2312.06826/) (2023)
- [Bayesian Inference for Correlated Human Experts and Classifiers](https://mlsanity.com/paper/2506.05636/) (2025)
- [Collaborating with GenAI: Incentives and Replacements](https://mlsanity.com/paper/2508.20213/) (2025)
- [Learning to Decide with AI Assistance under Human-Alignment](https://mlsanity.com/paper/2605.12646/) (2026)
- [The Value of AI Advice: Personalized and Value-Maximizing AI Advisors Are Necessary to Reliably Benefit Experts and Organizations](https://mlsanity.com/paper/2412.19530/) (2024)
- [Dynamic Trust Calibration Using Contextual Bandits](https://mlsanity.com/paper/2509.23497/) (2025)
- [Human Decision-Making with AI Assistance under Correlated Features](https://mlsanity.com/paper/2606.20628/) (2026)
- [Should I stay or should I show? Learning to selectively disclose information](https://mlsanity.com/paper/2609.39818/) (2026)

## Abstract (arXiv)

Artificial intelligence (AI) systems are increasingly used for providing advice to facilitate human decision making in a wide range of domains, such as healthcare, criminal justice, and finance. Motivated by limitations of the current practice where algorithmic advice is provided to human users as a constant element in the decision-making pipeline, in this paper we raise the question of when should algorithms provide advice? We propose a novel design of AI systems in which the algorithm interacts with the human user in a two-sided manner and aims to provide advice only when it is likely to be beneficial for the user in making their decision. The results of a large-scale experiment show that our advising approach manages to provide advice at times of need and to significantly improve human decision making compared to fixed, non-interactive, advising approaches. This approach has additional advantages in facilitating human learning, preserving complementary strengths of human decision makers, and leading to more positive responsiveness to the advice.