Ethics in artificial intelligence have been long debated, and one aspect of that discussion is AI accountability. But while the intricacies of accountability are still being argued, the importance of accountability is not. This article provides three important steps in achieving AI accountability: creating a functional system, providing accurate data, and preventing algorithm bias.
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Artificial intelligence (AI) is a powerful technology that can monitor and change itself without human supervision. Still, AI developers have a serious responsibility to keep their creations in check. If not, the systems can become unethical and stray from the intended path.
We must hold ourselves and AI accountable if we want to see the technology reach new heights. Here’s why accountability is important and how developers and users should approach accountability moving forward.
The AI Life Cycle
The biggest mistake developers make with their AI is evaluating the system only after its completion. Since AI is a machine-learning technology that constantly evolves, we can’t simply wait until the end of its development to study the results. We need to track its progress throughout the life cycle. These are the main stages of an AI system’s growth:
- Design: Defining the system’s objectives and performance requirements
- Development: Establishing new technical restrictions, collecting data, building the model, and optimizing the system
- Deployment: Monitoring compliance, securing compatibility with other systems, and evaluating the user experience (UX)
- Monitoring: Assessing the system’s performance based on output and impact, addressing the problems, and expanding or ending the system
Each step gives ample opportunity to measure progress and reduce risks within AI systems. We cannot separate ourselves from the consequences of our systems at any point. We need to commit to optimizing AI systems from day one so they stay on the right track.
To create ethical AI, we need to have a thorough understanding of digital ethics – the moral standards we rely on to make good decisions about online systems and platforms. The digital code of ethics consists of three critical decisions with their own issues:
- Application: Bias, consent, autonomy, human enhancement
- Governance: Control, law, rights, identity
- Risk: Security, health, privacy, inequality
Each decision has several underlying factors that make an AI system accountable or unaccountable. A well-run system checks each box and leaves no room for the AI to go AWOL and cause problems. In the past, systems that neglected specific ethical factors led to discrimination based on gender or race, false arrests, and even fatal car accidents. These situations can lead to further legal issues and fines or arrests for developers.
For future AI systems to avoid these pitfalls, developers need to account for all potential ethical issues – even those that seem insignificant to machine learning. AI is too complex for us to hope that it follows all the rules without asking. We need to stay in control.
Establishing accountability within our AI systems is a three-part process, according to experts. The first element we need to focus on is functional performance. To gather accurate data about a system and make the necessary changes, one must first make sure the system functions as intended.
For example, a system that helps a company filter job applications should consistently identify quality applicants. If it doesn’t, the company hires the wrong people and loses profits. Someone might also accuse them of discrimination. We must work harder in the design and development stages to nip these problems in the bud.
The second step for establishing accountability is providing AI with accurate data. Systems work best when they have straightforward and unbiased datasets. That means we have to set aside our personal biases and give our systems information that will optimize its performance for everyone, not just a select group or individual.
Third, we have to prevent the algorithms themselves from developing biases, which can easily happen if we allow AI to develop without close supervision. The faulty hiring model is another example of this situation. We must act as safeguards against unintended functions that devolve into ethical problems.
Attentiveness Leads to Accountability
Each step in the AI life cycle has its challenges, but with closer attention to detail, we can spot ethical problems before they occur and find proactive solutions. The more attentive we are, the more accountable our AI systems can become.
About the Author:
Zachary Amos is the Features Editor at ReHack where he writes about artificial intelligence, cybersecurity and other tech topics.