Decision-aid or Controller? Steering Human Decision Makers with Algorithms

• URL: http://arxiv.org/abs/2303.13712v1
• Date: Thu, 23 Mar 2023 23:24:26 GMT
• Title: Decision-aid or Controller? Steering Human Decision Makers with Algorithms
• Authors: Ruqing Xu, Sarah Dean
• Abstract summary: We study a decision-aid algorithm that learns about the human decision maker and provides ''personalized recommendations'' to influence final decisions. We discuss the potential applications of such algorithms and their social implications.
• Score: 5.449173263947196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Algorithms are used to aid human decision makers by making predictions and recommending decisions. Currently, these algorithms are trained to optimize prediction accuracy. What if they were optimized to control final decisions? In this paper, we study a decision-aid algorithm that learns about the human decision maker and provides ''personalized recommendations'' to influence final decisions. We first consider fixed human decision functions which map observable features and the algorithm's recommendations to final decisions. We characterize the conditions under which perfect control over final decisions is attainable. Under fairly general assumptions, the parameters of the human decision function can be identified from past interactions between the algorithm and the human decision maker, even when the algorithm was constrained to make truthful recommendations. We then consider a decision maker who is aware of the algorithm's manipulation and responds strategically. By posing the setting as a variation of the cheap talk game [Crawford and Sobel, 1982], we show that all equilibria are partition equilibria where only coarse information is shared: the algorithm recommends an interval containing the ideal decision. We discuss the potential applications of such algorithms and their social implications.

Related papers

• Designing Algorithmic Recommendations to Achieve Human-AI Complementarity [2.4247752614854203]
  We formalize the design of recommendation algorithms that assist human decision-makers. We use a potential-outcomes framework to model the effect of recommendations on a human decision-maker's binary treatment choice. We derive minimax optimal recommendation algorithms that can be implemented with machine learning.
  arXiv  Detail & Related papers  (2024-05-02T17:15:30Z)
• Persuasion, Delegation, and Private Information in Algorithm-Assisted Decisions [0.0]
  A principal designs an algorithm that generates a publicly observable prediction of a binary state. She must decide whether to act directly based on the prediction or to delegate the decision to an agent with private information but potential misalignment. We study the optimal design of the prediction algorithm and the delegation rule in such environments.
  arXiv  Detail & Related papers  (2024-02-14T18:32:30Z)
• Online Decision Mediation [72.80902932543474]
  Consider learning a decision support assistant to serve as an intermediary between (oracle) expert behavior and (imperfect) human behavior. In clinical diagnosis, fully-autonomous machine behavior is often beyond ethical affordances.
  arXiv  Detail & Related papers  (2023-10-28T05:59:43Z)
• Explainable Data-Driven Optimization: From Context to Decision and Back Again [76.84947521482631]
  Data-driven optimization uses contextual information and machine learning algorithms to find solutions to decision problems with uncertain parameters. We introduce a counterfactual explanation methodology tailored to explain solutions to data-driven problems. We demonstrate our approach by explaining key problems in operations management such as inventory management and routing.
  arXiv  Detail & Related papers  (2023-01-24T15:25:16Z)
• Algorithmic Decision-Making Safeguarded by Human Knowledge [8.482569811904028]
  We study the augmentation of algorithmic decisions with human knowledge. We show that when the algorithmic decision is optimal with large data, the non-data-driven human guardrail usually provides no benefit. In these cases, even with sufficient data, the augmentation from human knowledge can still improve the performance of the algorithmic decision.
  arXiv  Detail & Related papers  (2022-11-20T17:13:32Z)
• Algorithmic Assistance with Recommendation-Dependent Preferences [2.864550757598007]
  We consider the effect and design of algorithmic recommendations when they affect choices. We show that recommendation-dependent preferences create inefficiencies where the decision-maker is overly responsive to the recommendation.
  arXiv  Detail & Related papers  (2022-08-16T09:24:47Z)
• From Explanation to Recommendation: Ethical Standards for Algorithmic Recourse [0.0]
  We argue that recourse should be viewed as a recommendation problem, not an explanation problem. We illustrate by considering the case of diversity constraints on algorithmic recourse.
  arXiv  Detail & Related papers  (2022-05-30T20:09:42Z)
• Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizability [59.81339109121384]
  We study the $K$ contextual dueling bandit problem, a sequential decision making setting in which the learner uses contextual information to make two decisions, but only observes emphpreference-based feedback suggesting that one decision was better than the other. We provide a new algorithm that achieves the optimal regret rate for a new notion of best response regret, which is a strictly stronger performance measure than those considered in prior works.
  arXiv  Detail & Related papers  (2021-11-24T07:14:57Z)
• Double Coverage with Machine-Learned Advice [100.23487145400833]
  We study the fundamental online $k$-server problem in a learning-augmented setting. We show that our algorithm achieves for any k an almost optimal consistency-robustness tradeoff.
  arXiv  Detail & Related papers  (2021-03-02T11:04:33Z)
• Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis [75.64261155172856]
  survival analysis (SA) naturally supports censored data and offers appropriate ways to use such data for learning distributional models of algorithm runtime. We leverage such models as a basis of a sophisticated decision-theoretic approach to algorithm selection, which we dub Run2Survive. In an extensive experimental study with the standard benchmark ASlib, our approach is shown to be highly competitive and in many cases even superior to state-of-the-art AS approaches.
  arXiv  Detail & Related papers  (2020-07-06T15:20:17Z)
• A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores [85.12096045419686]
  We study the adoption of an algorithmic tool used to assist child maltreatment hotline screening decisions. We first show that humans do alter their behavior when the tool is deployed. We show that humans are less likely to adhere to the machine's recommendation when the score displayed is an incorrect estimate of risk.
  arXiv  Detail & Related papers  (2020-02-19T07:27:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.