Author: N/A; Publisher: The Wise Marketer; Publication Year: 2022. The following article talks about how businesses could benefit from customer data and pointed out the 4 main ethical issues (ownership, transparency, consent, and equitable value exchange) that need to be aware of when processing customer data. The goal is to ensure that customers understand, agree, and benefit from the process…
Author: Jessica Fjeld, Adam Nagy; Publisher: Berkman Klein Center; Publication Year: 2020. The following paper describes and explains 9 principles of ethical data practices: Informed consent of data subjects, security of data, anonymization, transparency, diversity, bias, prominence and communication. The principles listed in this framework cover 4 essential values of ethical data practices: Fairness, benefit, openness and reliability…
Author: N/A; Publisher: The University of Queensland; Publication Year: N/A. The following article is written for the University of Queensland in Australia, but the ethical principles outlined in the document are best practice for anyone who collects, maintains, and utilizes data. The first principle states that data usage must be defined, and a cost-benefit analysis must be done for individuals affected by the data. Likewise…
Author: Will Horvath; Publisher: Data Science Ethics; Publication Year: 2019. The following article discusses how with countries fighting against the growing threat of privacy invasion (and many losing), Europe implemented in 2018 the General Data Protection Regulation (GDPR) that provides the ‘most sweeping protection of user privacy rights yet passed in the world.’ France’s CNIL used the GDPR to fine Google for $57 million…
Author: N/A; Publisher: We All Count; Publication Year: N/A. The following article discusses how anytime data is involved, decisions are being made and those decisions have consequences. Equity needs to be at the forefront, and this 7-step framework is a systematic approach to doing so. Each step has equity-impacting decision points that must be evaluated. These steps are 1). Funding (this is the stage that…
Author: N/A; Publisher: U.K. Statistics Authority; Publication Year: 2020. The following visual provides a resource that outlines data ethics into 6 different categories. These categories are public good, data security, methods, legal compliance, public views/engagement, and transparency. A key takeaway from this resource is public good being at the top of the visual which emphasizes the fact that public good is crucial to…
Author: Ton Reijns, Richard Weurding, Jos Schaffers; Publisher: KPMG International; Publication Year: 2021. The following article discusses how the European Commission and corresponding European Union General Data Protection Regulation (GDPR) laws have established a framework for ethical artificial intelligence (AI) usage, specifically within the insurance industry. The Dutch have adopted and mandated their own set of standards based off these…
Author: Todd Lohr, Tracy Gusher; Publisher: KPMG International; Publication Year: 2019. The following report contains policies and actions that can be implemented to operate an ethical artificial intelligence (AI). The 5 pillars are: 1). Prepare employees now, 2). Develop strong oversight and governance, 3). Align cybersecurity and ethical AI, 4). Mitigate bias, and 5). Increase transparency…
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…
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-…