Author: Vivek Katial; Publisher: Multitudes; Publication Year: N/A. The following article starts by introducing the profound effect that algorithms and, in particular, decisions from algorithms have on our life today. The author goes on to define algorithmic bias as “the ability of algorithms to systematically and repeatedly produce outcomes that benefit one particular group over another.” The article then goes on to state…
Author: Tony Sun, Andrew Gaut, Shirlyn Tang, et al.; Publisher: Arxiv; Publication Year: N/A. The following article discusses how despite their success in modeling various applications, natural language processing (NLP) models propagate and may even amplify gender bias found in text corpora. While the study of bias in artificial intelligence is not new, methods to reduce gender bias in NLP are still in their early stages. The authors of this paper…
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: Tamara K. Nopper; Publisher: Medium; Publication Year: 2022. The following article examines the possibilities of alternative data initiatives for mitigating racial inequality in the U.S. credit scoring system This discusses the for-profit credit evaluation system and how alternative data and fundamental shifts in the system that performs credit evaluation can improve the experience for minority users…
Author: Sonja Kelly, Mehrdad Mirpourian; Publisher: Women’s World Banking; Publication Year: 2021. The following report discussed where gender-based bias originates and how to mitigate such biases in the emerging digital credit space. The report discussed the data collection process on emerging credit platforms and what data would be collected. It also introduced 3 potential origins of bias in algorithms, including sampling bias, labeling bias…
Author: Nicol Turner Lee, Paul Resnick, Genie Barton; Publisher: Brookings; Publication Year: 2019. The following piece describes how artificial intelligence (AI) is being used to manage greater aspects of our economy. There is risk that algorithms can magnify human biases. Bias in this piece is defined when outcomes to one group disproportionally bears a negative impact of an algorithm. In response to this, there should be processes in place that…
Author: Jeff Saltz; Publisher: Data Science Process Alliance; Publication Year: 2022. The following article provides 10 questions for a data science team to incorporate ethics into their projects. These questions address various aspects of ethics in data science. It is important to consider the laws surrounding the scope of projects. This includes the legal rights of individuals being affected by their data’s usage. In addition to the legal…
Author: Javier Sánchez-Monedero, Lina Dencik, Lilian Edwards; Publisher: Conference on Fairness, Accountability, and Transparency; Publication Year: 2020. The following article describes how discriminatory practices in recruitment and hiring are a persistent problem that affects not only workplace relations but also broader understandings of economic justice and inequality. With the introduction and increased adoption of automated hiring systems (AHSs) powered by data-driven tools, the way…
Author: James Manyika, Jake Silberg, Brittany Presten
Publisher: Harvard Business Review
Publication Year: 2019
Summary: The following article discusses how artificial intelligence (AI) can help identify and reduce the impact of human biases. But it can also make the problem worse by baking in and deploying biases at scale in sensitive areas. In Broward Country, Florida, an algorithm mislabeled African-American defendants as “high risk.” Bias can creep into algorithms in…
Author: Genevieve Smith, Ishita Rustagi; Publisher: Berkeley Haas, Center for Equity, Gender and Leadership; Publication Year: N/A. The following resource is a business playbook for mitigating bias in artificial intelligence (AI). It has several real-life examples of bias in AI that could lead to unethical situations or business decisions. The authors introduce 7 plays that when used correctly can unlock the potential of AI to advance business and society responsibly and equitably. The 7…