13 AI in Everyday Life & Ethical Principals of AI

AI in Everyday Life

AI impacts the everyday lives of billions of people at home, work, and play.  It has also become seamlessly integrated into a wide range of businesses and industries (Horowitz, 2020; UNESCO, 2021).

• At-home virtual assistants such as Amazon Alexa, Apple Siri, and Google Assistant to perform searches, answer questions, and control appliances
• Learning management systems to facilitate online learning and distance education and improve educational access to vulnerable populations
• eCommerce, online shopping, and automated warehouses to keep homes and businesses running smoothly
• Industrial manufacturing to mass-produce products and monitor quality
• Chatbots to reduce labor costs, answer complex questions, and provide customer service
• Weather systems to monitor climate change, fire hazards, and everyday weather patterns
• Agricultural systems and machinery to monitor crop health and improve crop yield
• Autonomous vehicles (drones) and aircrafts for national and international surveillance and defense
• Enhanced home, business, and national security through video monitoring, facial recognition, and biometrics
• Healthcare management through electronic medical records
• Patient diagnosis, prediction, treatment, and monitoring to improve health outcomes
• Public health surveillance to predict and monitor outbreaks
• Law enforcement to help with crime prevention, situational awareness, predictive policing, facial and voice recognition, and data analysis

Ethical Principles of AI

Some segments of the population have always had a healthy suspicion of AI, which was scoffed at by many scientists.  Indeed, with regards to the threats of automation, Samuel (1960) stated, “The machine is not a threat to mankind, as some people think.  …the conversation about bias and ethics has taken a different level of urgency…The machine does not possess a will, and its so-called “conclusions” are only the logical consequences of its input, as revealed by the mechanistic functioning of an inanimate assemblage of mechanical and electrical parts.”

However, the conversation about bias and ethics has taken a different level of urgency and has gone mainstream since symbolic AI has been complemented and sometimes replaced by deep neural networks and machine learning (Morley, Florid, Kinsey, & Elhala, 2020)

In the past decade, much of the discussion about AI ethics have revolved around machine learning.

Ethical Concerns Related to Algorithmic Use of Machine Learning

(Mittelstadt, et al., 2016)

• Inconclusive evidence
• Inscrutable evidence
• Misguided evidence
• Unfair outcomes
• Transformative effects
• Traceability