Bias in data‐driven artificial intelligence systems—An introductory survey

Eirini Ntoutsi · Pavlos Fafalios · Ujwal Gadiraju · Vasileios Iosifidis · Wolfgang Nejdl · María-Esther Vidal +17 more

1,040 citations0/0 files storedWiley Interdisciplinary Reviews Data Mining and Knowledge Discovery

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Ethics and Social Impacts of AI Explainable Artificial Intelligence (XAI)Artificial Intelligence in Law

Abstract

Abstract Artificial Intelligence (AI)‐based systems are widely employed nowadays to make decisions that have far‐reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well‐grounded in a legal frame. In this survey, we focus on data‐driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth. This article is categorized under: Commercial, Legal, and Ethical Issues > Fairness in Data Mining Commercial, Legal, and Ethical Issues > Ethical Considerations Commercial, Legal, and Ethical Issues > Legal Issues

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 1,040 citations exist upstream.

References

2012CRITICAL QUESTIONS FOR BIG DATA 2018Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification 2012Fairness through awareness 2017Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy 2014Big Data's Disparate Impact 2016Equality of Opportunity in Supervised Learning 2016Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy 2018Fairness definitions explained

Cited by

2023Re-Thinking Data Strategy and Integration for Artificial Intelligence: Concepts, Opportunities, and Challenges 2021A Framework of Potential Sources of Harm Throughout the Machine Learning Life Cycle 2023Six Human-Centered Artificial Intelligence Grand Challenges 2023The Impact of Artificial Intelligence on Workers’ Skills: Upskilling and Reskilling in Organisations 2020Artificial Intelligence (AI) or Intelligence Augmentation (IA): What Is the Future?2021

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.