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Ethics in The Metaverse: Why Data Concerns Needs to be Resolved Now, Not Later
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Author: Merav Ozair; Publisher: Nasdaq; Publication Year: 2022. The following article talks about the importance of data ethics in the metaverse. Although the metaverse is not a concrete reality yet, its underlying technologies (including artificial intelligence, virtual reality, and 5G networks) all rely heavily on large amounts of data. In light of many companies implementing/developing…
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Data Bias: Why It Matters, and How to Avoid It
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Author: Megan Wells; Publisher: Scuba; Publication Year: N/A. The following article describes how with the rise of artificial intelligence (AI) and machine learning (ML), the use of data has multiplied over the years. The problem is that data is subject to biases that can alter the valuable purpose of a model built on the data.…
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AI Trained on 4Chan Becomes ‘Hate Speech Machine’
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Author: Matthew Gault; Publisher: Vice; Publication Year: 2022. The following article serves as an extreme example of unethical data practices. There are 2 important takeaways from this article. First, artificial intelligence (AI) professionals must be careful to ensure that the data their models are trained on have as little bias as possible. Kilcher’s model was…
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How Bad Data Keeps Us from Good AI
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Author: Mainak Mazumdar; Publisher: TED; Publication Year: N/A. In the following talk, Mainak argues that the biased data is what makes unethical artificial intelligence (AI), not the algorithms. Instead of spending time and money on enhancing algorithms, focus should be on obtaining unbiased data that makes ethical AI possible. He proposes 3 areas of improvement…
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Racist Data? Human Bias is Infecting AI Development
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Author: John Murray; Publisher: The Guardian; Publication Year: 2019. The following article gives several real-life examples of times when unchecked bias had serious consequences. For example, unchecked bias in the COMPAS program caused Black defendants to be incorrectly flagged as probable reoffenders almost twice as often as white defendants. People do not usually intend to…
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8 Types of Data Bias That Can Wreck Your Machine Learning Models
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Author: Joanna Kaminska; Publisher: Statice; Publication Year: 2022. The following article discusses how it is important to know that while working with data there is a possibility that the data is biased; this is a big problem due to the fact that if this biased data is used to create an machine learning (ML) model,…
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Human Bias in Machine Learning: How Well Do You Really Know Your Model?
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Author: Jim Box, Elena Snavely, and Hiwot Tesfaye; Publisher: SAS Global Forum; Publication Year: 2022. The following paper discusses how it is a common consensus that artificial intelligence (AI) and machine learning (ML) are going to solve problems by letting an objective algorithm make decisions. The issue is that the algorithms are not always objective.…
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The Three Big Ethical Concerns with Artificial Intelligence
Author: Frank Rudzicz; Publisher: MaRS Discovery District; Publication Year: 2019. In the following video, Frank Rudzicz, an artificial intelligence (AI) researcher, lays out the 3 big ethical concerns in AI. The first one is what we actually use AI for, when we develop models we create it for a distinct purpose, however that can get…
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Ethics, Data and Insurance: 4 Developments Worth Watching
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Author: Duncan Minty; Publisher: Scandinavian Insurance Quarterly (SIQ); Publication Year: 2017. The following article discusses how the insurance industry has long been using historical data to underwrite, price, and manage risks. The advanced algorithms that insurance firms rely on to profile customers underwrite and price premiums are built upon such data, and recent studies revealed…
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Data Brew
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Author: Denny Lee, Brooke Wenig, Diana Pfeil; Publisher: Data Brew; Publication Year: N/A. The following video series discusses 3 large topics – transparency, privacy, and bias. First was the idea of transparency in data science – one example is that of a credit score. If you are rejected for a loan, you have a right…
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