Healthcare, retail, manufacturing, and government all use artificial intelligence today. We must remain alert regarding AI’s ethical issues to ensure that it doesn’t cause more harm than good.
AI ethical questions in medicine, law enforcement, military defense, data privacy, quantum computing, and other sectors are more complicated than a robot vacuum. Join the top MS in AI program to learn about industry-standard artificial intelligence tools and their challenges.
AI ethics: let’s review here. As a caution, this article is not intended to change your mind but to highlight some important issues, both large and small.
- Unemployment and Wealth Inequality
Many people are concerned that AI will eliminate jobs in the future. Should we work toward a world where AI is fully developed and integrated into society, even if doing so threatens the livelihoods of millions of people?
By 2030, AI-driven machines will replace 800 million jobs, according to the McKinsey Global Institute’s assessment. Some say that if robots usurp their professions, they are too menial for humans. AI can create better jobs that use human abilities like higher cognitive processes, analysis, and synthesis. AI may also produce additional jobs since people will build and manage these robots.
Wealth inequality affects job loss. Most modern economies require hourly-paid wages to generate goods and services. After paying salaries, taxes, and other costs, the company puts any money left over into manufacturing, training, and growing the business to make more money. Here, the economy grows.
What happens if AI enters the economy? Robots are not paid hourly or taxed. They can operate at 100% with low maintenance costs.
- What if AI makes a mistake?
AIs could be better, and it takes time for machine learning to be beneficial. AIs can perform well if trained with good data. AIs can hurt us if we feed them faulty data or make programming mistakes. Tay, Twitter’s 2016 Microsoft AI chatbot, learned to spew racial obscenities and Nazi propaganda from Twitter users in less than a day. Since it would have compromised Microsoft’s reputation, the chatbot was shut down immediately.
- Can AI systems kill?
In this TEDx speech, Jay Tuck depicts AIs as self-updating software. The machine does what it learns, not what we wish. Jay recounts an event with Tallon, a robot. After an explosion, its automated gun jammed and wildly fired, killing nine and injuring 14.
Predator drones like the General Atomics MQ-1 Predator have existed for nearly a decade. Although American law requires people to kill, these remotely piloted planes can fire missiles. We must analyze drones’ role and use as they become more important in aerial military defense. If we solely utilize robots as deterrents, what happens? Should AIs kill instead of humans?
The non-profit Campaign to Stop Killer Robots wants to ban fully autonomous weapons that kill without human intervention. Fully autonomous weapons wouldn’t have the human judgment needed to figure out how big an attack is, tell the difference between civilians and combatants, and follow other fundamental laws of war. History demonstrates that they would not restrict their use to specific scenarios.
- Rogue AI
If intelligent machines can make mistakes, an AI could go rogue or have unforeseen effects by pursuing seemingly innocent goals. In The Terminator and other movies and TV shows, a centralized, super-smart AI computer gets self-aware and refuses to be controlled by humans.
Experts say future AI supercomputers could achieve this deadly self-awareness. An AI might also investigate a virus’s genetic structure to design a vaccination. Instead of developing a vaccine, the AI weaponized the virus after extensive computations. It’s like opening a new Pandora’s Box; ethics must be considered to prevent this.
- Singularity and AI Control
AIs are causing human extinction—it’s easy to see why AIs scare humans. Are AIs overtaking humans? What if they outsmart us and want to rule? Will computers replace humans? Technology surpasses human intelligence at “technological singularity.” Based on technological innovation, this will end the human era by 2030.
- How should AIs be treated?
Should robots have human rights or citizenship? Do robots have rights if we evolve them to “feel”? How can we rank social status if robots have rights? In 1942, Isaac Asimov introduced the concept of “robotics,” which addresses this issue. Sophia, the Hanson Robotics humanoid robot, became a Saudi citizen in 2017. While some see this as a publicity stunt rather than legal acknowledgment, it does provide an example of the rights AIs may be afforded in the future.
- AI Bias
AI is increasingly used in facial and speech recognition systems, some of which affect people and businesses. These systems are susceptible to human biases and mistakes. These AI systems’ training data may also be biased. For example, Microsoft, IBM, and Megvii facial recognition algorithms have gender biases. These AI systems were better at identifying white guys than darker-skinned men.
Can AI bias? It can be challenging. One could claim that intelligent machines lack morality and ideals. Nevertheless, even our moral compass and beliefs often hurt humanity, so how do we make sure AI agents don’t have the same defects as their creators? How AIs are educated and trained will determine if they have a bias toward race, gender, religion, or ethnicity. Consequently, AI researchers must consider bias while selecting data.
Explainable AI supports ethical AI systems. The system must be fair, or it may perpetuate socioeconomic disparities. These ethical issues require action and regulation.
Explainable AI explains black-box deep learning algorithms to white-box models. Deep learning adoption could be more active because enterprises cannot confidently duplicate how AI systems make decisions, which can have severe implications.
Ethical AI requires explainable AI to understand data and algorithms. You must understand data and algorithms to identify systemic ethical problems. An AI graduate degree shows employers you’re competent in this expanding field. You’ll learn AI, machine learning, data science, or other skills for your domain.
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