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.
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Artikel-artikel mengenai Algorithmic bias
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Undergraduate programs are springing up across the US to meet the burgeoning demand for workers trained in big data. Yet many of the programs lack training in the ethical use of data science.
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.
The Federal Trade Commission is rattling its saber at the technology industry over growing public concern about biased AI algorithms. Can the agency back up its threats?
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.
Some AI systems make faulty assumptions about women and nonwhite men, which can lead to misdiagnoses. Overcoming this bias takes legal, regulatory and technical fixes.
By letting machines recommend movies and decide whom to hire, humans are losing their unpredictable nature – and possibly the ability to make everyday judgments, as well.
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.
The departure of AI ethics researcher Timnit Gebru from Google highlights attempts to make algorithmic decision-making accountable.
The COVID-19 pandemic has led to calls for the collection of race-based data. But the risks of algorithmic discrimination must be addressed.
Handing management to algorithms creates ‘black-box bosses" whose decision-making is hard to understand or question.
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.
An AI trained to look at heart scans was able to successfully predict risk of death. But one expert cautions we still need to be careful about designing – and using – AI for medical diagnosis.
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.
Kontributor teratas
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Professor of Information Systems, Michigan State University
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Professor of English, Macalester College
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Professor of Information Science, University of Washington
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Professor of Informatics and Computer Science, Indiana University
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Associate Professor of Public Policy, Communication, and Information, UMass Amherst
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Lecturer in Criminology, The University of Queensland
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Professor of Information Technology and Organisation, University of Sydney
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Professor of Law, The University of Melbourne
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Sessional Lecturer, Australian Catholic University
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Senior Lecturer and DECRA Fellow, University of Sydney
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Senior Lecturer in Management Systems, University of Hull
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James P. Jimirro Professor of Media Effects, Co-Director, Media Effects Research Laboratory, & Director, Center for Socially Responsible AI, Penn State
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Head of Art and Design, University of Hertfordshire
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Director of Sydney Executive Plus, University of Sydney
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Professor, Computing and Information Systems, Pro Vice-Chancellor (Research Systems), and Pro Vice-Chancellor (Digital & Data), The University of Melbourne
