Trustworthy artificial intelligence: a review

D Kaur, S Uslu, KJ Rittichier, A Durresi - ACM computing surveys (CSUR …, 2022 - dl.acm.org
Artificial intelligence (AI) and algorithmic decision making are having a profound impact on
our daily lives. These systems are vastly used in different high-stakes applications like …

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

AB Arrieta, N Díaz-Rodríguez, J Del Ser, A Bennetot… - Information fusion, 2020 - Elsevier
In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if
harnessed appropriately, may deliver the best of expectations over many application sectors …

The ethics of algorithms: key problems and solutions

A Tsamados, N Aggarwal, J Cowls, J Morley… - Ethics, governance, and …, 2021 - Springer
Research on the ethics of algorithms has grown substantially over the past decade.
Alongside the exponential development and application of machine learning algorithms …

Machine learning testing: Survey, landscapes and horizons

JM Zhang, M Harman, L Ma… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
This paper provides a comprehensive survey of techniques for testing machine learning
systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing …

Towards fairness in visual recognition: Effective strategies for bias mitigation

Z Wang, K Qinami, IC Karakozis… - Proceedings of the …, 2020 - openaccess.thecvf.com
Computer vision models learn to perform a task by capturing relevant statistics from training
data. It has been shown that models learn spurious age, gender, and race correlations when …

[PDF][PDF] A framework for understanding unintended consequences of machine learning

H Suresh, JV Guttag - arXiv preprint arXiv:1901.10002, 2019 - courses.cs.duke.edu
As machine learning increasingly affects people and society, it is important that we strive for
a comprehensive and unified understanding of how and why unwanted consequences …

[HTML][HTML] A survey on bias in deep NLP

I Garrido-Muñoz, A Montejo-Ráez… - Applied Sciences, 2021 - mdpi.com
Deep neural networks are hegemonic approaches to many machine learning areas,
including natural language processing (NLP). Thanks to the availability of large corpora …

Using satellite imagery to understand and promote sustainable development

M Burke, A Driscoll, DB Lobell, S Ermon - Science, 2021 - science.org
BACKGROUND Accurate and comprehensive measurements of a range of sustainable
development outcomes are fundamental inputs into both research and policy. For instance …

Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literature

C Starke, J Baleis, B Keller… - Big Data & …, 2022 - journals.sagepub.com
Algorithmic decision-making increasingly shapes people's daily lives. Given that such
autonomous systems can cause severe harm to individuals and social groups, fairness …

Fairness in deep learning: A computational perspective

M Du, F Yang, N Zou, X Hu - IEEE Intelligent Systems, 2020 - ieeexplore.ieee.org
Fairness in deep learning has attracted tremendous attention recently, as deep learning is
increasingly being used in high-stake decision making applications that affect individual …