Promoting User Data Autonomy During the Dissolution of a Monopolistic Firm

• URL: http://arxiv.org/abs/2411.13546v1
• Date: Wed, 20 Nov 2024 18:55:51 GMT
• Title: Promoting User Data Autonomy During the Dissolution of a Monopolistic Firm
• Authors: Rushabh Solanki, Elliot Creager,
• Abstract summary: We show how the framework of Conscious Data Contribution can enable user autonomy during under dissolution. We explore how fine-tuning and the phenomenon of "catastrophic forgetting" could actually prove beneficial as a type of machine unlearning.
• Score: 5.864623711097197
• License:
• Abstract: The deployment of AI in consumer products is currently focused on the use of so-called foundation models, large neural networks pre-trained on massive corpora of digital records. This emphasis on scaling up datasets and pre-training computation raises the risk of further consolidating the industry, and enabling monopolistic (or oligopolistic) behavior. Judges and regulators seeking to improve market competition may employ various remedies. This paper explores dissolution -- the breaking up of a monopolistic entity into smaller firms -- as one such remedy, focusing in particular on the technical challenges and opportunities involved in the breaking up of large models and datasets. We show how the framework of Conscious Data Contribution can enable user autonomy during under dissolution. Through a simulation study, we explore how fine-tuning and the phenomenon of "catastrophic forgetting" could actually prove beneficial as a type of machine unlearning that allows users to specify which data they want used for what purposes.

Related papers

• Safety vs. Performance: How Multi-Objective Learning Reduces Barriers to Market Entry [86.79268605140251]
  We study whether there are insurmountable barriers to entry in emerging markets for large language models. We show that the required number of data points can be significantly smaller than the incumbent company's dataset size. Our results demonstrate how multi-objective considerations can fundamentally reduce barriers to entry.
  arXiv  Detail & Related papers  (2024-09-05T17:45:01Z)
• Consumer Transactions Simulation through Generative Adversarial Networks [0.07373617024876725]
  This paper presents an innovative application of Generative Adversarial Networks (GANs) to generate synthetic retail transaction data. We diverge from conventional methodologies by integrating SKU data into our GAN architecture and using more sophisticated embedding methods. Preliminary results demonstrate enhanced realism in simulated transactions measured by comparing generated items with real ones.
  arXiv  Detail & Related papers  (2024-08-07T09:45:24Z)
• Hybrid LLM/Rule-based Approaches to Business Insights Generation from Structured Data [0.0]
  The ability to extract actionable insights from vast and varied datasets is essential for informed decision-making and maintaining a competitive edge. Traditional rule-based systems, while reliable, often fall short when faced with the complexity and dynamism of modern business data. This paper explores the efficacy of hybrid approaches that integrate the robustness of rule-based systems with the adaptive power of Large Language Models.
  arXiv  Detail & Related papers  (2024-04-24T02:42:24Z)
• The Frontier of Data Erasure: Machine Unlearning for Large Language Models [56.26002631481726]
  Large Language Models (LLMs) are foundational to AI advancements. LLMs pose risks by potentially memorizing and disseminating sensitive, biased, or copyrighted information. Machine unlearning emerges as a cutting-edge solution to mitigate these concerns.
  arXiv  Detail & Related papers  (2024-03-23T09:26:15Z)
• Scaling Laws Do Not Scale [54.72120385955072]
  Recent work has argued that as the size of a dataset increases, the performance of a model trained on that dataset will increase. We argue that this scaling law relationship depends on metrics used to measure performance that may not correspond with how different groups of people perceive the quality of models' output. Different communities may also have values in tension with each other, leading to difficult, potentially irreconcilable choices about metrics used for model evaluations.
  arXiv  Detail & Related papers  (2023-07-05T15:32:21Z)
• Enabling Inter-organizational Analytics in Business Networks Through Meta Machine Learning [0.0]
  Fear of disclosing sensitive information as well as the sheer volume of the data that would need to be exchanged are key inhibitors for the creation of effective system-wide solutions. We propose a meta machine learning method that deals with these obstacles to enable comprehensive analyses within a business network.
  arXiv  Detail & Related papers  (2023-03-28T09:06:28Z)
• Semantic Information Marketing in The Metaverse: A Learning-Based Contract Theory Framework [68.8725783112254]
  We address the problem of designing incentive mechanisms by a virtual service provider (VSP) to hire sensing IoT devices to sell their sensing data. Due to the limited bandwidth, we propose to use semantic extraction algorithms to reduce the delivered data by the sensing IoT devices. We propose a novel iterative contract design and use a new variant of multi-agent reinforcement learning (MARL) to solve the modelled multi-dimensional contract problem.
  arXiv  Detail & Related papers  (2023-02-22T15:52:37Z)
• Federated Pruning: Improving Neural Network Efficiency with Federated Learning [24.36174705715827]
  We propose Federated Pruning to train a reduced model under the federated setting. We explore different pruning schemes and provide empirical evidence of the effectiveness of our methods.
  arXiv  Detail & Related papers  (2022-09-14T00:48:37Z)
• Mechanisms that Incentivize Data Sharing in Federated Learning [90.74337749137432]
  We show how a naive scheme leads to catastrophic levels of free-riding where the benefits of data sharing are completely eroded. We then introduce accuracy shaping based mechanisms to maximize the amount of data generated by each agent.
  arXiv  Detail & Related papers  (2022-07-10T22:36:52Z)
• Propensity-to-Pay: Machine Learning for Estimating Prediction Uncertainty [1.452875650827562]
  This study investigates machine learning models' ability to consider different contexts and estimate the uncertainty in the prediction. A novel concept of utilising a Baysian Neural Network to the binary classification problem of propensity-to-pay energy bills is proposed and explored for deployment.
  arXiv  Detail & Related papers  (2020-08-27T11:49:25Z)
• Super-App Behavioral Patterns in Credit Risk Models: Financial, Statistical and Regulatory Implications [110.54266632357673]
  We present the impact of alternative data that originates from an app-based marketplace, in contrast to traditional bureau data, upon credit scoring models. Our results, validated across two countries, show that these new sources of data are particularly useful for predicting financial behavior in low-wealth and young individuals.
  arXiv  Detail & Related papers  (2020-05-09T01:32:03Z)

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.