Understanding Bayesian Optimization with Shapley Values for Human-AI Collaboration

Khái niệm cốt lõi

Bayesian optimization with Shapley values enhances human-AI collaboration by providing interpretability and rationale behind optimization decisions.

Tóm tắt

Bayesian optimization (BO) with Gaussian processes is crucial for black box optimization problems. ShapleyBO framework interprets BO's proposals using game-theoretic Shapley values, aiding in human-machine interaction. The method disentangles contributions to exploration and exploitation, enhancing trust and efficiency in AI systems.

Thống kê

BO is optimized with confidence bound (CB) as an acquisition function.
ShapleyBO quantifies each parameter's contribution to the acquisition function.
Contributions are dissected into mean optimization and uncertainty reduction.
Epistemic and aleatoric uncertainties are disentangled using Shapley values.

Trích dẫn

Thông tin chi tiết chính được chắt lọc từ

Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration

by Juli... lúc arxiv.org 03-08-2024

https://arxiv.org/pdf/2403.04629.pdf

Yêu cầu sâu hơn

How can the use of Shapley values in Bayesian optimization impact real-world applications beyond robotics

The use of Shapley values in Bayesian optimization can have a significant impact on real-world applications beyond robotics. One key area where this approach can be beneficial is in personalized medicine. By applying Bayesian optimization with Shapley values, healthcare providers can optimize treatment plans for individual patients based on their unique characteristics and responses to different interventions. This personalized approach can lead to more effective treatments, reduced side effects, and better patient outcomes.
Another application could be in financial portfolio management. By using Bayesian optimization with Shapley values, investment firms can optimize their portfolios based on various factors such as risk tolerance, market conditions, and investment goals. This could result in improved returns for investors while managing risks effectively.
Furthermore, the use of Shapley values in Bayesian optimization can also enhance supply chain management by optimizing inventory levels, production schedules, and distribution strategies. This could lead to cost savings, improved efficiency, and better customer satisfaction.
Overall, the integration of Shapley values into Bayesian optimization opens up possibilities for enhancing decision-making processes across various industries by providing interpretable insights into the reasoning behind algorithmic proposals.

What counterarguments exist against the effectiveness of interpreting BO proposals through game-theoretic approaches like Shapley values

While interpreting BO proposals through game-theoretic approaches like Shapley values offers valuable insights into the contributions of parameters to the acquisition function's informativeness and exploration-exploitation trade-offs, there are some counterarguments against its effectiveness:

Computational Complexity: Calculating exact Shapley values for all possible coalitions of features or parameters may become computationally expensive as the number of features increases exponentially.

Assumption Limitations: The assumption that each feature contributes independently may not always hold true in complex systems where interactions between features play a crucial role.

Interpretability vs Performance Trade-off: While interpretability is essential for understanding model decisions and building trust with users or stakeholders, focusing too much on interpretability might compromise performance metrics such as accuracy or efficiency.

Subjectivity: Interpreting results based on subjective human knowledge or intervention criteria may introduce bias or inconsistencies that affect the reliability of using Shapley values for decision-making.

How can the concept of interpretability in AI systems be related to ethical considerations surrounding algorithmic decision-making

The concept of interpretability in AI systems is closely related to ethical considerations surrounding algorithmic decision-making due to several reasons:

Transparency: Interpretability allows stakeholders to understand how AI algorithms arrive at specific decisions or recommendations which promotes transparency and accountability.

Bias Detection: Interpretable models help identify biases within algorithms that could lead to discriminatory outcomes against certain groups or individuals.

Trustworthiness: Ethical concerns arise when black-box algorithms make critical decisions without clear explanations; interpretability helps build trust among users by providing understandable justifications for algorithmic outputs.

4Fairness: Understanding how AI systems work enables stakeholders to assess whether they are making fair decisions according to legal requirements and ethical standards.
5Human Oversight: Interpretability facilitates human oversight over automated systems ensuring that humans remain accountable for final decisions made by AI tools rather than relinquishing control entirely.