Resources that were produced by upcoming or rarely-recognized authors.
Author: Nina Alag Suri; Publisher: LinkedIn; Publication Year: 2021. The following article describes how artificial intelligence (AI) plays an important role in talent acquisition and can make more precise predictions of candidate success. In order to ethically use AI, companies should utilize ethical frameworks and an ethical code of conduct. Some notable topics include: candidates should be informed of AI usage and…
Author: Mikael Anneroth; Publisher: Ericsson; Publication Year: 2021. The following article begins with descriptions of examples of artificial intelligence (AI) bias and the poses the question: without ethical use of AI, just how at stake are our human rights? The article then describes characteristics of frameworks for ethical AI which is then followed up by examples in practice. The first tenant of ethical AI is more regulation…
Author: Michael Schur; Publisher: TED; Publication Year: 2022. The following resource takes a step back and looks at ethics broadly, and how ethics can be important in any situation, any industry that you are in. The speakers says “Understanding ethical theories is how we increase our chances of success at simply being human beings who have to negotiate with other human beings.” This quote is especially…
Author: Mia Shah-Dand; Publisher: AI Ethics Diaries: Keeping it Real Podcast; Publication Year: 2022. The following podcast episode features Milena Pribic, a senior artificial intelligence (AI) designer at IBM, who gives advice on building ethical AI. Her team digs into team practices and uses an ethical approach to understand the team as she wants her team to understand the context and repercussions of what they are building. Additionally, Pribic…
Author: Loren Larson; Publisher: HireVue; Publication Year: 2018. The following article discusses how HireVue is committed to good science that creates a level playing field for all candidates. Without deliberately working to reduce bias that may reside in an algorithm’s training data or its data scientist creators, algorithms are absolutely at risk of inheriting the biases of humans. If Hirevue finds a feature that indirectly…
Author: N/A; Publisher: Linear Digressions; Publication Year: 2020. The following podcast episode discusses how the quality of data around policing is hit-and-miss based on region. Reporters of data are those being scrutinized, so there is some self-reporting bias. Human biases are reflected in the data, so it is important to draw conclusions carefully…
Author: Kriti Sharma; Publisher: TED; Publication Year: N/A. The following video features Kriti Sharma who talks about how human-programmed artificial intelligence (AI) can have the biases of humans encoded into them and how this can negatively affect real people. Even in everyday technology, having female voice assistants, such as Siri or Alexa, perpetuates sexism. AI is another piece of technology…
Author: Joy Buolamwini; Publisher: Time Magazine; Publication Year: 2019. In the following article, notable data ethics advocate Joy Buolamwini shares her journey combating gender and racial bias in artificial intelligence (AI). After encountering biased facial analysis software that could not recognize dark-skinned faces, the author was motivated to seek similar examples of discriminatory AI in the industry. After recognizing the…
Author: Jabril Ashe; Publisher: CrashCourse; Publication Year: 2019. The following video discusses how bias is a natural outcome as systems such as the human brain or a computer algorithm try to take shortcuts in order to increase efficiency. It becomes a problem when we do not acknowledge it, we fail to recognize exceptions to the pattern, or when it results in incorrect or skewed results (including discrimination). Some…
Author: Harini Suresh; Publisher: Medium; Publication Year: 2019. In the following article, the author points out that language matters and that the right terminology forms a mental framework. The author also provides a framework for identifying the sources of harm within a machine learning pipeline. By identifying at what stage the bias is in the pipeline, better communication can ensue and the right resolutions to…