Disinformation, algorithms, big data, care work, climate change, and cultural knowledge can all be invisible. This exhibition brings them to the light.
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Articles on Algorithm bias
Displaying 1 - 20 of 25 articles
Inclusivity and diversity also need to be at the level of identifying values and defining frameworks of what counts as ethical AI in the first place.
A UK controversy about school leavers’ marks shows algorithms can get things wrong. To ensure algorithms are as fair as possible, how they work and the trade-offs involved must be made clear.
Plans have been made for the AI-based program to begin trials before the year ends. But it raises serious questions about the role of police in preventing domestic violence.
Politicians of all stripes, computer professionals and even big-tech executives are calling on government to hit the brakes on using these algorithms. The feds are hitting the gas.
A civil rights group is suing Facebook for its failure to stop the spread of anti-Muslim hate speech on the platform.
Music recommendation algorithms are more likely to suggest music by male than female artists.
Search engines, like social media algorithms, get you to click on links by learning what other people click on. Enticing misinformation often comes out on top.
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.
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.
Top contributors
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Professor and ARC Future Fellow, Education & Social Work, Education Futures Studio, University of Sydney
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Chief Scientist, Gradient Institute, University of Sydney
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Lecturer in Criminology, The University of Queensland
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Researcher, University of Sydney
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Professor of Law, The University of Melbourne
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Senior Lecturer and Director, Centre for Research on Education in a Digital Society, University of Technology 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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Professor of Law, Villanova School of Law
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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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Associate Professor in Data Science, University of Technology Sydney
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Assistant Professor of Computer Science and Engineering, University of South Florida
