Tag: Representation in Training Datasets
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A Beauty Contest was Judged by AI and the Robots Didn’t like Dark Skin
Artificial Intelligence, Beauty.AI, Human Beauty, Microsoft, Racial Bias, Representation in Training Datasets, Training Data, Youth LaboratoriesAuthor: Sam Levin; Publisher: The Guardian; Publication Year: 2016. The following article discusses how in 2016, the first artificial intelligence (AI) judged beauty contest was conducted. The objective factors included facial symmetry and wrinkles. Beauty.AI,…
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How I’m Fighting Bias in Algorithms
Algorithmic Bias, Coded Gaze, Discriminatory Practices, Facial Recognition Systems, Fairness, Machine Learning, Representation in Training DatasetsAuthor: Joy Buolamwini; Publisher: TED; Publication Year: N/A. The following speech discusses how algorithmic bias, or the “coded gaze,” can lead to discriminatory practices. Machine learning is being used for facial recognition but a lack…
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Human Bias in Machine Learning: How Well Do You Really Know Your Model?
Artificial Intelligence, Biased Data, Biased Results, Local Interpretable Model-Agnostic Explanations, Machine Learning, Objective Algorithm, Representation in Training Datasets, Training DataAuthor: 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…
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4 Principles of Responsible AI and Best Practices to Adopt Them
Data Access, Data Classification, Data Encryption, Fairness, Representation in Training Datasets, Responsible Artificial Intelligence, Synthetic Data, Training Data, Transparency, Usage RestrictionsAuthor: Cem Dilmegani; Publisher: AI Multiple; Publication Year: 2022. The following article explores 4 principles for responsible AI design and recommends best practices. The 4 principles, along with best practices, include: 1). achieving fairness by…
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Bias in AI: What it is, Types, Examples & 6 Ways to Fix It in 2022
Artificial Intelligence, Automation, Debiasing, Decision-Making, Diversify, Human-Driven Processes, Multidisciplinary, Representation in Training Datasets, Third-Party, Training Data, TransparencyAuthor: Cem Dilmegani; Publisher: AI Multiple; Publication Year: 2022. The following article describes how in the imperfect world, AI can’t be expected to be completely unbiased. However, there are various ways to minimize bias by…
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Inequality in the Data Science Industry
Algorithms, Automation, Bias Avoidance, Gender Differences, Hard-Coded Racism, Malicious Actors, Recidivism Rates, Representation in Training Datasets, Weapons of Math DestructionAuthor: Aisulu Omar; Publisher: Towards Data Science; Publication Year: 2022. The following article discusses how data scientists should be more cognizant of the role they play in organizations. Their skill set as a tool can…