If the historical data used to train an AI system disadvantages certain minority groups, the system can be swayed to follow these patterns in its own decision-making process.
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Articles on Algorithm bias
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Problems in the underlying data mean we can't leave algorithms to decide things on their own.
Scientists are arguing over how YouTube might help turn people into extremists.
A report calls for banning the use of emotion recognition technology. An AI and computer vision researcher explains the potential and why there's growing concern.
A machine learning expert predicts a new balance between human and machine intelligence is on the horizon. For that to be good news, researchers need to figure out how to design algorithms that are fair.
The fundamental problem with AI is it is often riddled with society's existing biases and prejudices.
Technology firms should use more design fiction to explore and avoid potential negative consequences, such as AI bias.
Artificial intelligence is predicted to contribute some US$15.7 trillion to the global economy by 2030. A new report looks at issues specific to New Zealand.
Social biases in digital tech create racist face recognition software and sexist hiring tools, but more data collection isn't the answer.
When algorithms are at work, there should be a human safety net to prevent harming people. Artificial intelligence systems can be taught to ask for help.
An ethicist on why fixing algorithms may not be the best response to algorithmic bias.
What do the Carlos Ghosn scandal, the rising power of algorithms and the "gilets jaunes" have in common? The need to extend the spatial and temporal definitions of responsibility.
Some AI technologies aren't advanced enough to provide useful insights, but simpler tools can yield new opportunities to explore the humanities.
Expecting algorithms to perform perfectly might be asking too much of ourselves.
Artificial intelligence poses opportunities as well as dangers; understanding them – and regulating carefully – will help avoid harm to individuals and society as a whole.
A new tool called Biometric Mirror exposes the need for public debate about the ethics of AI.
Information on social media can be misleading because of biases in three places – the brain, society and algorithms. Scholars are developing ways to identify and display the effects of these biases.
Top contributors
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Chief Scientist, Gradient Institute, University of Sydney
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Senior Research Fellow, University of Oxford
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Assistant Professor of Integrative Biology & Computer Science and Engineering, Michigan State University
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Departmental Lecturer in International Political Economy, University of Oxford
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Professeur en management & théories des organisations, Université Paris Dauphine – PSL
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Assistant Professor of Law, Saint Louis University
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Professor of Informatics and Computer Science, Indiana University
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Head of Art and Design, University of Hertfordshire
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Assistant professor, department of Computer Science and Engineering, University of South Florida
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Professor of Demography, University of Waikato
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Head of Research and Emerging Practice, Research Fellow, The University of Melbourne
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Reader (Associate Professor) in Information Systems, School of Business and Management, Royal Holloway
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Assistant professor in organisation studies, Durham University
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Research Fellow in Behavioural Economics, International Institute for Management Development (IMD)
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Director Microsoft Research Centre for Social Natural User Interfaces, The University of Melbourne
