Author: lnbressa
-
How Companies Learn Your Secrets
Author: Charles Duhigg; Publisher: The New York Times; Publication Year: 2012. The following article describes how every time you interact with a company, they learn and record information about you, from demographic details to shopping…
-
Digital Ageism: Challenges and Opportunities in Artificial Intelligence for Older Adults
Accessibility, Ageism, Digital Ageism, Discrimination, Fairness, Harms of Allocation, Harms of Representation, Older Adult Populations, PrejudiceAuthor: Charlene Chu et al.; Publisher: The Gerontologist, Volume 62; Publication Year: 2022. The following paper explores the impacts of ageism and technology. They define “digital ageism” as the repercussions of bias against older individuals…
-
An Ethical Framework for Big Data and Smart Cities
Author: Victor Chang; Publisher: Technological Forecasting and Social Change, Volume 165; Publication Year: 2021. The following paper proposes an ethical framework for the use of big data to address urban concerns, particularly surrounding public transportation…
-
Overconfidence Bias: The False Assumption That Someone is Better Than Others
Author: N/A; Publisher: The Corporate Financial Institute; Publication Year: 2023. The following article covers multiple forms of overconfidence that can be found in analysts in the financial industry. There were 4 types that are discussed:…
-
Standards of Practice Handbook
Author: N/A; Publisher: The Chartered Financial Analyst (CFA) Institute; Publication Year: 2014. Summary: The following handbook discusses regulation in relation to ethics of financial analysts. It covers how to keep privacy of data, to transfer…
-
Transparency and Openness Promotion (TOP) Guidelines
Author: N/A; Publisher: The Center for Open Science; Publication Year: 2014. The following paper addresses how the Center for Open Science’s (COS) mission is to increase the openness, integrity, and reproducibility of research. Toward this…
-
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…
-
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…