Trustworthy AI
- URL: http://arxiv.org/abs/2011.02272v1
- Date: Mon, 2 Nov 2020 20:04:18 GMT
- Title: Trustworthy AI
- Authors: Richa Singh, Mayank Vatsa, Nalini Ratha
- Abstract summary: Brittleness to minor adversarial changes in the input data, ability to explain the decisions, address the bias in their training data, are some of the most prominent limitations.
We propose the tutorial on Trustworthy AI to address six critical issues in enhancing user and public trust in AI systems.
- Score: 75.99046162669997
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Modern AI systems are reaping the advantage of novel learning methods. With
their increasing usage, we are realizing the limitations and shortfalls of
these systems. Brittleness to minor adversarial changes in the input data,
ability to explain the decisions, address the bias in their training data, high
opacity in terms of revealing the lineage of the system, how they were trained
and tested, and under which parameters and conditions they can reliably
guarantee a certain level of performance, are some of the most prominent
limitations. Ensuring the privacy and security of the data, assigning
appropriate credits to data sources, and delivering decent outputs are also
required features of an AI system. We propose the tutorial on Trustworthy AI to
address six critical issues in enhancing user and public trust in AI systems,
namely: (i) bias and fairness, (ii) explainability, (iii) robust mitigation of
adversarial attacks, (iv) improved privacy and security in model building, (v)
being decent, and (vi) model attribution, including the right level of credit
assignment to the data sources, model architectures, and transparency in
lineage.
Related papers
- Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems [88.80306881112313]
We will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI.
The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees.
We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them.
arXiv Detail & Related papers (2024-05-10T17:38:32Z) - Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance [14.941040909919327]
Distributed AI systems are revolutionizing big data computing and data processing capabilities with growing economic and societal impact.
Recent studies have identified new attack surfaces and risks caused by security, privacy, and fairness issues in AI systems.
We review representative techniques, algorithms, and theoretical foundations for trustworthy distributed AI.
arXiv Detail & Related papers (2024-02-02T01:58:58Z) - Building Safe and Reliable AI systems for Safety Critical Tasks with
Vision-Language Processing [1.2183405753834557]
Current AI algorithms are unable to identify common causes for failure detection.
Additional techniques are required to quantify the quality of predictions.
This thesis will focus on vision-language data processing for tasks like classification, image captioning, and vision question answering.
arXiv Detail & Related papers (2023-08-06T18:05:59Z) - VerifAI: Verified Generative AI [22.14231506649365]
Generative AI has made significant strides, yet concerns about its accuracy and reliability continue to grow.
We propose that verifying the outputs of generative AI from a data management perspective is an emerging issue for generative AI.
Our vision is to promote the development of verifiable generative AI and contribute to a more trustworthy and responsible use of AI.
arXiv Detail & Related papers (2023-07-06T06:11:51Z) - Auditing and Generating Synthetic Data with Controllable Trust Trade-offs [54.262044436203965]
We introduce a holistic auditing framework that comprehensively evaluates synthetic datasets and AI models.
It focuses on preventing bias and discrimination, ensures fidelity to the source data, assesses utility, robustness, and privacy preservation.
We demonstrate the framework's effectiveness by auditing various generative models across diverse use cases.
arXiv Detail & Related papers (2023-04-21T09:03:18Z) - Human-Centric Multimodal Machine Learning: Recent Advances and Testbed
on AI-based Recruitment [66.91538273487379]
There is a certain consensus about the need to develop AI applications with a Human-Centric approach.
Human-Centric Machine Learning needs to be developed based on four main requirements: (i) utility and social good; (ii) privacy and data ownership; (iii) transparency and accountability; and (iv) fairness in AI-driven decision-making processes.
We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data.
arXiv Detail & Related papers (2023-02-13T16:44:44Z) - Towards a Responsible AI Development Lifecycle: Lessons From Information
Security [0.0]
We propose a framework for responsibly developing artificial intelligence systems.
In particular, we propose leveraging the concepts of threat modeling, design review, penetration testing, and incident response.
arXiv Detail & Related papers (2022-03-06T13:03:58Z) - Statistical Perspectives on Reliability of Artificial Intelligence
Systems [6.284088451820049]
We provide statistical perspectives on the reliability of AI systems.
We introduce a so-called SMART statistical framework for AI reliability research.
We discuss recent developments in modeling and analysis of AI reliability.
arXiv Detail & Related papers (2021-11-09T20:00:14Z) - Privacy and Robustness in Federated Learning: Attacks and Defenses [74.62641494122988]
We conduct the first comprehensive survey on this topic.
Through a concise introduction to the concept of FL, and a unique taxonomy covering: 1) threat models; 2) poisoning attacks and defenses against robustness; 3) inference attacks and defenses against privacy, we provide an accessible review of this important topic.
arXiv Detail & Related papers (2020-12-07T12:11:45Z) - Uncertainty as a Form of Transparency: Measuring, Communicating, and
Using Uncertainty [66.17147341354577]
We argue for considering a complementary form of transparency by estimating and communicating the uncertainty associated with model predictions.
We describe how uncertainty can be used to mitigate model unfairness, augment decision-making, and build trustworthy systems.
This work constitutes an interdisciplinary review drawn from literature spanning machine learning, visualization/HCI, design, decision-making, and fairness.
arXiv Detail & Related papers (2020-11-15T17:26:14Z)
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.