Category: Diversity, Equity & Inclusion
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5 Questions to Ask to Ensure AI Ethics
Author: Dan Power; Publisher: Evanta; Publication Year: 2022. The following article lists questions that need to be asked to check if any ethics problems exist or if anything has been missed. Large companies such as…
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Algorithmic Bias: Why Bother?
Author: Damini Gupta, T. S. Krishnan; Publisher: California Review Management; Publication Year: 2020. The following article highlights the importance and the need to reduce algorithmic bias. The article starts by recognizing that human bias will…
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Managing Data Ethics: A Process-Based Approach for CDOs
Author: Christopher Wilson; Publisher: Deloitte. Insights; Publication Year: 2019. The following article discusses how businesses are sometimes so concerned about solving issues with data that they ignore the new issues that arise from even using…
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Principles for the Safe and Effective use of Data and Analytics
Author: N/A; Publisher: New Zealand Government; Publication Year: 2018. The following document contains a list of principles developed for the safe and effective use of data and analytics. The first principle states that any organization…
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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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Data Feminism
Binaries, Context, Data Collection, Hierarchies, Indigenous Peoples, Intersectionality, Power Distribution, Power StructuresAuthor: Catherine D’Ignazio and Lauren Klein; Publisher: MIT Press; Publication Year: 2020. In the following book D’Ignazio and Klein present a new lens for thinking about data science and ethics. Their ideas are based on…