People with higher cognitive abilities are often better able to spot patterns in the world around them, allowing them to excel in a wide range of tasks, from learning languages to recognizing faces. But, in some situations, even being intelligent has its drawbacks. A new study has found that these people are more likely to stereotype others based on the patterns they detect, potentially leading to negative consequences as they perpetuate social biases. A new study found that people with higher cognitive abilities are more likely to stereotype others. In the study, the researchers manipulated image-description pairings so that the faces with particular features were linked to negative stereotypes.
It can also reflect human flaws and inconsistencies, including 180 known types of bias. Biased AI is everywhere, and like humans, it can discriminate against gender, race, age, disability and ideology. AI bias has enormous potential to negatively affect women, minorities, the disabled, the elderly and other groups. Computer vision has more issues with false-positive facial identification for women and people of color, according to research by MIT and Stanford University. A recent ACLU experiment discovered that nearly 17 percent of professional athlete photos were falsely matched to mugshots in an arrest database.
AI, the buzz word known as Artificial Intelligence, in practice can be explained as Augmented Intelligence; a tool that has been in development since 1950s to extend human capabilities to complete tasks no human or machine could accomplish on their own. The world has already shifted towards building an AI integrated future and it's our responsibility to ensure it's heading in the right direction. "People are overlooked for a variety of biased reasons and perceived flaws; mathematics cuts straight through them" (Moneyball, 2011) Despite its immense potential, some major barriers still exist for AI hindering its progress. Biased behavior uncovered in current AI models has made us question if AI is the right way forward. To avoid such unfavourable outcomes and consequences, it is imperative to regulate the implementation of AI by an ethical framework assuring the key attributes; Transparency, Accountability, Privacy and Lack of Bias.
Machine learning bias, also known as algorithm bias or AI bias, is a phenomenon that occurs when an algorithm produces results that are systematically prejudiced due to erroneous assumptions in the machine learning process. Algorithms can have built-in biases because they are created by individuals who have conscious or unconscious preferences that may go undiscovered until the algorithms are used, and potentially amplified, publically. Even the most seasoned IT shop can struggle with honing in on the various elements of an AI initiative. We designed this expert guide to help you better understand all of the considerations for building and maintaining the infrastructure and engine that support the initiatives. Plus, you'll learn about the products and players to help you make the best buying decision.
According to Wikipedia, cognitive biases "are tendencies to think in certain ways that can lead to systematic deviations from a standard of rationality or good judgment, and are often studied in psychology and behavioral economics." Far more than simply an exercise in academia, cognitive biases have all sorts of practical impacts on our lives, whether or not we admit it. A very broad umbrella, cognitive bias comes in many forms, as evidenced by the fact that Wikipedia lists over 170 of them. Some of these biases are more prevalent in certain areas of life than in others. Below is an infographic from Business Insider, of all places, which is an elementary summary of what it refers to as "20 cognitive biases that screw up your decisions."