Accountability

Teaching Data Ethics: We’re Going to Ethics the Heck Out of This

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Author: Tristan Henderson; Publisher: Association for Computing (ACM) Machinery Digital Library; Publication Year: 2019. The following paper outlines a new Data Ethics & Privacy module that was introduced to computer science students in 2018. The module aims to raise student awareness of current debates in computer science such as bias in artificial intelligence, algorithmic accountability, filter bubbles and data protection, and practical mechanisms for…

AI has a Dangerous Bias Problem — Here’s How to Manage It

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Author: Thomas Macaulay, Alejandro Saucedo; Publisher: The Next Web; Publication Year: 2022. The following article discusses how almost all artificial intelligence (AI) algorithms are inherently biased as they do exactly what us the programmers tell them to do and we are all inherently biased ourselves. This resource is an interview with Alejandro Saucedo, Chief Scientist at The Institute for Ethical AI, who discusses his main points of what needs to be…

Data Ethics Framework

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Author: N/A; Publisher: The United Kingdom’s Department for Digital, Culture, Media & Sport; Publication Year: 2020. The following framework is based on 3 principles: transparency, accountability, and fairness. These principles, supported by 5 specific actions, guide organizations through different stages of the project and provide practical considerations. The 5 actions are: 1). Define the goal or benefit, 2). Use diverse teams to minimize bias (evaluators may be part-…

Australia’s Artificial Intelligence Ethics Framework

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Author: N/A; Publisher: Australian Government; Publication Year: 2022. The following guidelines provided by the Australian Government has a list of 8 items that make up their data ethics framework. These include: 1). Human, societal and environmental wellbeing, 2). Human-centered values, 3). Fairness , 4). Privacy protection and security , 5). Reliability and safety , 6). Transparency and explainability , 7). Contestability, and…

Algorithmic Accountability

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Author: Hetan Shah; Publisher: National Library of Medicine; Publication Year: 2017. The following article reviews ways in which researchers can improve trustworthiness. The authors argue that transparency alone cannot lead to accountability. Additionally, legislation for equitable machine learning algorithms may be useless because of the rise of social media networks collecting data. Certain countries are guilty of “digital…

Locating ethics in data science: responsibility and accountability in global and distributed knowledge production systems

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Author: Sabina Leonelli; Publisher: The Royal Society; Publication Year: 2016. The following article talks about the challenges produced in data science around the responsibilities and accountabilities of individuals in the industry. The author advocates a flexible, participative management of data practices. Regulatory agencies should be responsible for encouraging data scientists to examine ethical implications of their work…

A Guide for Ethical Data Science

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Author: Institute and Faculty of Actuaries; Publisher: Royal Statistical Society; Publication Year: 2019. The following article is a very well designed code of ethics that revolves around its defined 5 major themes of data ethics: 1). Seek to enhance the value of data science for society; 2). Avoid harm; 3). Apply and maintain professional competence; 4). Seek to preserve or increase trustworthiness; 5). Maintain accountability and oversight. The paper goes into…

Legal and Human Rights Issues of AI: Gaps, Challenges and Vulnerabilities

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Author: Rowena Rodrigues; Publisher: Journal of Responsible Technology; Publication Year: 2020. The following article looks at how ethical issues such as algorithmic transparency, cybersecurity vulnerabilities, unfairness, bias and discrimination, lack of contestability, legal personhood issues, intellectual property issues, adverse effects on workers, privacy and data protection issues may result in serious liability for damage and lack of accountability as…

10 Big Data Analytics Privacy Problems

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Author: Rebecca Herold; Publisher: SecureWorld; Publication Year: 2014. The following article discusses how 10 of the most significant privacy risks are: 1). Privacy breaches and embarrassments; 2). Anonymization become impossible; 3). Insufficient data masking; 4). Unethical misinterpretation; 5). Inaccurate data or flawed models; 6). Discrimination; 7). Few legal protection; 8). Forever-existing data; 9). Concerns for e-…

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 algorithm. This example proved the importance of researchers/developers who working on artificial…