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The Real Reason to be Afraid of Artificial Intelligence
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Author: Peter Haas; Publisher: TEDx Talks; Publication Year: 2017. The following article features Peter Haas, who actually works in robotics at Brown University, who is afraid of robots. The example of how to train a model to classify wolf and Husky illustrates that there exists bias in the data set that was fed to the…
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Big Data Phenotyping in Rare Diseases: Some Ethical Issues
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Author: Nina Hallowell, Michael Parker, Christoffer NellÃ¥ker; Publisher: Genetics in Medicine; Publication Year: 2018. The following article discusses how computational phenotyping (using machine learning algorithms [MLAs] to analyze photographic images) has improved healthcare experience for rare disease patients and facilitated the research for clinical geneticists. Although there are many benefits and beneficiaries of computational phenotyping,…
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Artificial Intelligence in Healthcare: Providing Ease or Ethical Dilemmas?
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Author: Nils Aoun, Chloé Currie, Itai Epstein, and Chaaron Nahar; Publisher: Maiei; Publication Year: 2022. The following article covers how research in medicine by people is at a plateau, many of the recent advancements are coming through artificial intelligence (AI). AI has primarily increased the efficiency of the diagnosis process. Showing great accuracy in classifying…
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How AI and Neuroscience Drive Each Other Forwards
Author: Neil Savage; Publisher: Nature; Publication Year: 2019. The following article explores how artificial intelligence (AI) and neuroscience enhance each other. AI, with its ability to identify patterns in large, complex datasets, has seen remarkable successes in the past decade, in part by emulating how the brain performs certain computations. Cognitive science is beginning to…
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SAM: The Sensitivity of Attribution Methods to Hyperparameters
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Author: Naman Bansal, Chirag Agarwal, Anh Nguyen; Publisher: N/A; Publication Year: 2020. The following article provides examples and an accompanying talk that illustrates how in many classification machine learning systems, even more explainable models, not black box, are highly susceptible to manipulation and silly mistakes. These models, which seek to show how they classify or…
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Can Machine Learning be Moral?
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Author: Miguel Sicart, Irina Shklovski, Mirabelle Jones; Publisher: N/A; Publication Year: 2021. The following paper attempts to answer a critical question that has vexed many debates: what constitutes an ethically accountable machine learning system? The authors of this paper investigate the ethical evaluation of machine learning methodologies. The authors examine machine learning techniques, methods, and…
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Ethical Algorithm Design Should Guide Technology Regulation
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Author: Michael Kearns, Aaron Roth; Publisher: Brookings; Publication Year: 2020. The following article is focused on frameworks for thinking about algorithmic bias and how to address the “unanticipated consequence of following the standard methodology of machine learning: specifying some objective (usually a proxy for accuracy or profit) and algorithmically searching for the model that maximizes…
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Responsible AI Dashboard: A One-Stop Shop for Operationalizing Responsible AI in Practice
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Author: Mehrnoosh Sameki; Publisher: Microsoft; Publication Year: 2021. The following dashboard can be used to make sure data scientists ethically and responsibly take the correct approach towards artificial intelligence or machine learning. “A single pane of glass bringing together several mature responsible AI tools in the areas of machine learning interpretability, unfairness assessment and mitigation,…
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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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The Invisible Workers of the AI Era
Author: Maximilian GAHNTZ; Publisher: Medium; Publication Year: 2018. The following article discusses how a lot of automated programs require a large of amount of workers doing blue collar work such as data entry in order to function properly. In the past, underpaid women worked as “computers” doing calculations. In order for algorithms such as machine…
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