As science fiction becomes scientific fact, a focus on ethics will ensure that AI strategies help the whole human race rather than just a select few.
To keep our attention, the algorithms incorporated into social media platforms feed us information that triggers the most primal inclinations of our complicated psychologies. For example, fake news spreads six times as quickly as actual news. The Netflix documentary “The Social Dilemma” exposes some of the most shocking realities. The robot was programmed to keep our attention.
Infinite-scroll news feeds are, of course, only a tiny part of the artificial intelligence issue. AI strategies and machine learning impact how we shop, bank, travel, and even decide who gets an interview vs who receives a rejection letter.
Although robots may or may not be coming to take our jobs, they are already becoming increasingly involved in our daily lives. What’s the bottom line? It is critical for everyone, not just engineers, to understand whether software benefits or damages us. This is where ethical AI enters the picture.
Practical Applications Of Ethical AI
Ethical AI assures that an organization’s or institution’s AI projects respect basic humanity and do not damage people.This encompasses a wide range of problems, including fairness, non-weaponization, and liability, such as when self-driving cars cause accidents.
Businesses can have an ethical AI ideology that their machine learning and artificial intelligence activities adhere to at the organizational level. This direction isn’t always terrible, although it varies by organization.
Ethics Of Artificial Intelligence And Robotics
According to Dr Catriona Wallace, founder and CEO of Ethical AI Advisory and creator and director of Flamingo AI, governments and other regulators are at least five years behind the curve in comprehending what AI is capable of and how to regulate it.
Dr Wallace argues that humans value various things, but it all boils down to two principles: do no harm and emphasize health and safety.
The Ethical AI website includes eight important ethical AI principles and guidelines:
- Human, Societal, And Environmental Well-Being
AI systems should help individuals, society, and the environment throughout their lifespan.
- Values Focused On People
AI systems should respect human rights, diversity, and individual autonomy throughout their existence.
During their lifecycles, AI strategies should be open and accessible, and they should not include or lead in discriminatory practices against individuals, organizations, or groups.
- Security And Privacy Protection
AI systems should respect and preserve privacy rights and data protection throughout their existence and ensure data security.
- Safety And Dependability
Throughout their existence, AI systems should consistently fulfil their original purpose.
- Essential Transparency And Comprehensibility
Transparency and responsible disclosure should be implemented to ensure that people are aware when an AI system is having a substantial impact on them and that they are aware when an AI system is interacting with them.
When an AI system has a substantial impact on a person, community, group, or environment, a timely method should be in place to allow people to question the AI system’s use or output.
Human oversight of AI strategies should be enabled, and those responsible for the various phases of the AI system lifecycle should be identified and held accountable for the outcomes of the AI systems.
According to Xuhui Shao, managing partner of Los Altos, Calif.-based Tsingyuan Ventures, which invests in software and life sciences startups, the adoption of ethical AI principles is critical for the development of all AI-driven technologies, and self-regulation by the industry will be far more effective than any legislative effort.
Data is the lifeblood of any AI strategies, and the acquisition and use of consumer data, particularly in large-scale commercial systems, must be monitored appropriately. He used the example of AI-driven redlining or unfavorable decisions based on discriminatory characteristics that may be difficult to identify, even by the operators. These AI-based decisions must be transparent and constantly monitored.