Artificial intelligence needs to be deployed in a way that benefits humanity. That requires looking beyond the short-term model to long-term use and AI’s widescale impact on the broader society.
As the use of artificial intelligence and machine learning grows, so, too, will the deployment of automated decision-making systems that could greatly impact well-being, privacy, and livelihood. Organizations must, therefore, develop ethical principles to guide the design, development, and deployment of AI and ML systems to ensure that the power of these technologies is used responsibly.
This is a two-stage process. Stage one is developing the principles. Stage two defines the various core AI ethics principles that will guide the organization.
When developing the principles, the first step is to get multidisciplinary input from a mixed community of ethicists, technologists, legal experts, and sociologists. Representatives of affected communities — for example, health care or finance — also have to be involved to guarantee there’s a comprehensive understanding of the potential implications for its use.
The second step would be a broader public consultation if it’s an AI or ML model that impacts society at large. Public consultations, such as a town hall, can offer insights from ordinary citizens who might be affected while helping to foster trust in the use of AI and ML.
Regularly reviewing ethical principles is critical because AI is evolving so quickly, and they need to remain relevant.
It’s also important to put a feedback mechanism in place to ensure that the AI developers, users, and affected individuals can provide observations and critiques on the AI systems and their implications once they’re deployed. It’s important to know whether the system is working as expected.
When it comes to delineating what the core AI ethics principles should be, the first thing that comes to mind is fairness. The AI model should be designed and trained to avoid bias – something that’s often easier said than done. It needs to provide equitable outcomes regardless of age, gender, race, or any personal characteristics. Proactive steps must be taken to address and rectify any biases that might be inherent in the training data or algorithms.
Transparency is another critical component. Stakeholders and other people impacted by the model should be able to understand how the system works. It’s not enough to have clear documentation of the algorithm and, the data source, and the decision-making process. There needs to be a plain English version that people who aren’t data scientists can understand. Transparency helps users understand the model itself, trust it, and be able to effectively interact with it.
Another critical issue is privacy. To respect the rights of individuals to maintain their privacy, the protection and confidentiality of their data must be ensured through differential privacy mechanisms, such as federated learning or encryption. User data must not be vulnerable to exposure or improper use.
Human oversight is essential. If an automated system errors or acts in an unexpected way, there needs to be human judgment in the loop to be able to intervene or identify that the model is acting improperly and to rectify any damages.
Accountability needs to exist at several levels – one individual cannot be responsible for the entire outcome. There needs to be accountability at the level of development and design and then overall accountability for the model and its use, which probably rises to the corporate executive level.
Continuous learning and monitoring mechanisms must be in place to track how these models are performing and ensure that they remain aligned with ethical standards over time.
Developing and adhering to ethical principles is more than about preventing misuse. It’s about guiding technology to realize its full potential and serving humanity. As technology continues to blend into all facets of our lives, we need a strong foundation to ensure that it remains an ethical tool for the greater good.