Prediction-sharing Contracts in Machine Learning

Core Concepts

Firms benefit from sharing predictions strategically during training and inference.

Abstract

Two firms engage in competitive prediction tasks with shared data.
Study focuses on data-sharing contracts for prediction models.
Analysis conducted on three levels: Bayesian framework, natural settings, and simulation.
Different contracts explored: no-sharing, train-sharing, infer-sharing, full-sharing.
Results show unique optimal contracts based on parameters and utility models.
Practical implementation using loan data simulation.

Stats

"We take a random subset of 25% of the features and assign it to Firm 1."
"Each neural net was trained for 20 epochs on a 8-GB RAM M1 MacBook Pro."
"Depending on the contract, the firms choose their equilibrium strategies based on the performance in the test data."

Quotes

"In many cases, one firm’s data and prediction capabilities are complemented by those available to a competing firm."
"The distributed nature of firms’ capabilities introduces a major obstacle: Why, and under what conditions, would firms willingly share their predictions with competitors?"
"Our main innovation in this paper is the observation that this obstacle actually consists of two separate questions, corresponding respectively to the training and inference phases."

Key Insights Distilled From

Prediction-sharing During Training and Inference

by Yotam Gafni,... at arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17515.pdf

Deeper Inquiries

How do differing beliefs between firms impact the emergence of optimal contracts

Differing beliefs between firms can significantly impact the emergence of optimal contracts in the context of data sharing. When firms have divergent beliefs about the underlying data or the correlation between their prediction models, it can lead to disagreements on the value of sharing information. This can result in challenges in reaching consensus on the most beneficial contract for both parties.
If firms have different beliefs about the accuracy of their prediction models or the correlation between them, it can lead to uncertainty and potential mistrust in the data-sharing process. This uncertainty may hinder the willingness of firms to share their data or predictions, as they may fear that the other party has an advantage or is not acting in good faith.
In such cases, the emergence of optimal contracts may be more difficult to achieve, as the firms may struggle to find common ground or agree on the terms of data sharing. Negotiations and communication become crucial in resolving these differences and reaching mutually beneficial agreements.

What are the implications of introducing monetary compensation for data sharing in this context

Introducing monetary compensation for data sharing in this context can have significant implications for the dynamics of the data-sharing agreements. The introduction of monetary incentives can alter the motivations and behaviors of the firms involved, potentially leading to different outcomes in terms of optimal contracts.
Monetary compensation can provide a clear incentive for firms to share their data or predictions, as they stand to gain financially from the exchange. This can overcome some of the barriers related to trust and uncertainty that may arise in non-monetary exchanges. Firms may be more willing to share their data if they see a direct financial benefit from doing so.
However, the introduction of monetary compensation can also introduce new challenges. It may lead to issues of fairness and equity in the data-sharing process, as firms may prioritize financial gain over the quality or accuracy of the shared data. Additionally, determining the appropriate monetary value for the data shared can be complex and may require careful negotiation and agreement between the parties involved.
Overall, introducing monetary compensation can change the dynamics of data sharing, potentially incentivizing firms to share more data but also raising new considerations and challenges in the process.

How can the findings of this study be applied to other industries beyond machine learning

The findings of this study can be applied to other industries beyond machine learning that involve competitive prediction tasks and data sharing.

Finance and Banking: In the financial sector, firms can benefit from sharing data on credit risk assessment or fraud detection. By understanding the optimal contracts for sharing prediction models or inference-time predictions, financial institutions can improve their decision-making processes and risk management strategies.

Healthcare: Healthcare organizations can leverage the insights from this study to optimize data sharing for diagnostic purposes or treatment recommendations. Sharing prediction models or inference-time predictions can enhance the accuracy of medical diagnoses and improve patient outcomes.

Marketing and Advertising: Companies in the marketing and advertising industry can use the principles of optimal data sharing contracts to improve targeted advertising and customer segmentation. By sharing prediction models or inference-time predictions, firms can enhance their marketing strategies and campaign effectiveness.

By applying the concepts of strategic data sharing and optimal contract design across various industries, organizations can enhance their predictive capabilities, improve decision-making processes, and drive better business outcomes.