Developers need to create artificial intelligence (AI) solutions by keeping in mind certain AI principles to reap the technology’s benefits without incurring its inherent risks.
Artificial intelligence has progressed by leaps and bounds in the past few years. The solutions that can be created with artificial intelligence technologies are unimaginable. However, there is a possibility that artificial intelligence systems will progress to the point where they will be capable of making complex decisions in a matter of seconds with full autonomy. The dark side of such advancement is that the technology will attain complete autonomy, override human decisions, and potentially cause harm to mankind. Scenarios demonstrated in movies such as The Terminator, Wall-E, 2001: A space odyssey, and Avengers: Age of Ultron, where artificial intelligence systems turn evil, may become a reality if the technology isn’t checked. Thus, regulating the potentially limitless capabilities of artificial intelligence technology becomes necessary. For that, developers need to meet a certain set of AI principles that ensures that ethics aren’t compromised during the life cycle of the AI system. This ensures that the AI system doesn’t harm any individual and prevents it from going rogue. These principles can help reduce security risks, improve confidence among users, and help achieve better adoption and outcomes with AI solutions.
Ethical AI Principles Developers Need to Follow
While developing AI solutions, developers need to follow certain principles to ensure that the safety and security of the software aren’t compromised, and the software benefits one and all. Some of the principles developers can focus on include:
1. Human-Centricity
When conceptualizing an AI solution, developers should go ahead with the development, only if the AI system benefits individuals, enterprises, and the human race as a whole during its entire life cycle. AI solutions should be developed for primarily benefitting and improving human life, instead of achieving destructive ends. The AI systems should be aligned with human values, promoting human rights, respecting individual opinions, improving the standard of living, saving human lives, and even protecting the environment. The education and healthcare sector are the two most important sectors which can benefit from a human-centric approach with AI technologies. AI solutions can help improve the quality of education, which will help students find better job opportunities, which, in turn, will help improve the quality of life of such individuals. Similarly, the use of AI technologies in the healthcare industry can potentially help save lives. However, the use of AI technologies should not be restricted to these two sectors and can be leveraged in other areas such as enterprise resource planning, oil and gas operations, entertainment, and environmental protection.
2. Risk Awarness
A risk-based approach should be adopted when creating an AI system. Developers should identify all the risks associated with specific AI systems. And they should only proceed with the development of the AI system if the risks are insignificant or non-existent. For instance, when developers are working with facial recognition technology, they should assess all the things that can go wrong with the technology. They should ensure that facial recognition technology does not harm any individual. For example, facial recognition technology isn’t foolproof and has resulted in false convictions. Thus, developers, when creating such a system, should ensure that the technology has as few risks associated with it as possible. Developers should, thus, not turn a blind eye to risk awareness, assessment, and management when working with AI technology.
3. Reliability
As mentioned above, AI systems should have as few risks associated with them as possible. Developers should aim at creating highly reliable AI solutions. The solutions should work as intended throughout their lifecycle. It includes ensuring that the solutions are highly accurate, reliable, and predictable at every stage. They should not pose risks to users that might get affected by these systems. And thus, developers should ensure that the systems are monitored and tested periodically to check whether the AI solutions are working properly. If any shortcomings are found, then they should be addressed immediately. The bottom line is that developers must ensure the AI system’s robustness and safety during its entire lifecycle.
4. Accountability
No matter how autonomous and self-reliant artificial intelligence technology becomes, human supervision and monitoring remain absolutely necessary. Human oversight should be enabled for AI systems, no matter how reliable or advanced the AI system is. Individuals responsible for various stages of development must be identifiable and should be held accountable for the outcomes caused by the AI system. Mechanisms must be put in place to ensure accountability and responsibility. It includes monitoring all the processes involved, right from the conceptualisation to development, and deployment to the operation phase. Appropriate actions should be taken if an individual is found responsible for the incorrect use of the AI system.
5. Compliance
AI systems should be designed to be flexible enough to adapt to new government regulations and vice versa. The AI system should be developed in such a manner that it does not require many changes to be made to comply with the new regulations. Similarly, governments should draft new laws and regulations in such a manner that the AI systems are not affected severely. There needs to be a balance between the freedom to create new AI technology and government rules, regulations, and compliances. This can be achieved through mutual understanding, partnership, and communication between the parties involved. Additionally, when an AI system significantly impacts an individual, enterprise, a community, or the environment, provisions should be in place that allows people to challenge the adoption, usability, and outcome of the concerned AI system.
6. Privacy
The amount of public and private data that is available to develop AI systems is scary, to say the least. Developers must ensure that privacy and data protection are respected when working with AI systems. It includes ensuring that the data generated and used by the AI systems during its lifecycle is governed and managed appropriately. Developers must ensure that the autonomy of data and information is maintained so as not to be used inappropriately by hackers or scammers. Thus, there should be appropriate data security measures in place to protect user data and ensure privacy.
7. Cost-Effectiveness
The AI systems developed should be affordable to enterprises and end-users. Developers should ensure that the AI systems are reasonably priced and can benefit a large number of people, even if the technology is proprietary. Developers can even make the technology available for free or share the source code on open-source platforms for other users to improve and build upon. The maintenance costs of AI systems, too, should be minimum. There have been questions raised regarding the ethics of AI in recent years. And rightly so. There have been instances where AI technology has been misused or has taken decisions on its own which were questionable. And it’s not just the average individual but also leaders and scientists such as Stephen Hawking, Bill Gates, and Elon Musk who have voiced valid concerns regarding AI technology. Thus, there arises a need to check AI systems. As a step in this direction, the White House has come up with its own set of AI principles that need to be followed. These include encouraging public participation, ensuring scientific integrity and information quality, transparency, and many other principles. Developers must ensure that AI systems follow the ethics and principles outlined by the White House in addition to adherence to the ones mentioned above to ensure that their solutions are ethically sound.
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