Rethinking machine unlearning for large language models
We explore machine unlearning in the domain of large language models (LLMs), referred to
as LLM unlearning. This initiative aims to eliminate undesirable data influence (for example …
as LLM unlearning. This initiative aims to eliminate undesirable data influence (for example …
Federated learning for medical applications: A taxonomy, current trends, challenges, and future research directions
With the advent of the Internet of Things (IoT), artificial intelligence (AI), machine learning
(ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications …
(ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications …
Model sparsity can simplify machine unlearning
In response to recent data regulation requirements, machine unlearning (MU) has emerged
as a critical process to remove the influence of specific examples from a given model …
as a critical process to remove the influence of specific examples from a given model …
Explainability of artificial intelligence methods, applications and challenges: A comprehensive survey
W Ding, M Abdel-Basset, H Hawash, AM Ali - Information Sciences, 2022 - Elsevier
The continuous advancement of Artificial Intelligence (AI) has been revolutionizing the
strategy of decision-making in different life domains. Regardless of this achievement, AI …
strategy of decision-making in different life domains. Regardless of this achievement, AI …
Salun: Empowering machine unlearning via gradient-based weight saliency in both image classification and generation
With evolving data regulations, machine unlearning (MU) has become an important tool for
fostering trust and safety in today's AI models. However, existing MU methods focusing on …
fostering trust and safety in today's AI models. However, existing MU methods focusing on …
[PDF][PDF] Decentralized finance
ABSTRACT DeFi ('decentralized finance') has joined FinTech ('financial technology'),
RegTech ('regulatory technology'), cryptocurrencies, and digital assets as one of the most …
RegTech ('regulatory technology'), cryptocurrencies, and digital assets as one of the most …
AI bias: exploring discriminatory algorithmic decision-making models and the application of possible machine-centric solutions adapted from the pharmaceutical …
L Belenguer - AI and Ethics, 2022 - Springer
A new and unorthodox approach to deal with discriminatory bias in Artificial Intelligence is
needed. As it is explored in detail, the current literature is a dichotomy with studies …
needed. As it is explored in detail, the current literature is a dichotomy with studies …
Ethics of artificial intelligence in education: Student privacy and data protection
L Huang - Science Insights Education Frontiers, 2023 - bonoi.org
Rapid advances in artificial intelligence (AI) technology are profoundly altering human
societies and lifestyles. Individuals face a variety of information security threats while …
societies and lifestyles. Individuals face a variety of information security threats while …
Generating synthetic data in finance: opportunities, challenges and pitfalls
Financial services generate a huge volume of data that is extremely complex and varied.
These datasets are often stored in silos within organisations for various reasons, including …
These datasets are often stored in silos within organisations for various reasons, including …
Model merging in llms, mllms, and beyond: Methods, theories, applications and opportunities
Model merging is an efficient empowerment technique in the machine learning community
that does not require the collection of raw training data and does not require expensive …
that does not require the collection of raw training data and does not require expensive …