What is ethical AI?
Ethical AI is the development and use of artificial intelligence systems in a way that considers and prioritizes ethical principles. AI ethics can also concern the moral behavior of humans when designing, making, using, and processing AI systems.
AI is an ever-evolving field and as such, several important ethical considerations need to be taken into account, keeping future developments in mind in order to serve as guides for AI development.
According to a Research Paper 2020AI ethical considerations have converged globally around five principles (transparency, justice and fairness, non-maleficence, accountability and privacy).
What is bias in AI?
The most important ethical consideration regarding any AI system is the bias it inherits, either from the developers of the system or from the data it trains on. Since all data produced by humans carries a risk of bias, it becomes inevitable that AI systems will acquire biases.
AI systems can acquire five types of bias:
- Algorithmic bias: This arises from the inherent assumptions and limitations programmed into the algorithm itself. For example, an algorithm trained on biased data may perpetuate that bias in its future predictions, unfairly disadvantaging certain groups.
- Data bias: This happens when the data used to train an AI system is incomplete, unrepresentative, or inaccurate. For example, an AI facial recognition system trained primarily on images of white men may struggle to accurately identify the faces of women or people of color.
- Confirmation bias: This happens when an AI system is designed to reinforce existing beliefs or expectations. For example, a news recommendation algorithm that prioritizes articles confirming users’ existing political views can create echo chambers.
- Stereotype bias: This happens when an AI system generalizes individuals or groups based on their perceived characteristics, thereby perpetuating harmful stereotypes. For example, a translation tool that systematically translates gender-neutral terms into masculine pronouns may reinforce gender stereotypes.
- Exclusion bias: This happens when certain groups or individuals are completely excluded from the data used to train an AI system, leading to their needs and perspectives being neglected. For example, an AI-based healthcare system trained primarily on data from rich countries might not be effective in providing care to people living in developing countries.
How can AI systems be used to spread disinformation?
Unfortunately, as AI systems, especially Generative AI systems, come closer to creating human responses and arts, malicious actors have come to abuse these systems.
One of the recent examples of this situation is the rise of deepfakes across the world. In India, deepfakes launched by target popular celebritiespoliticians and other prominent figures from various circles also being targeted.
Additionally, GenAI tools like ChatGPT have also allowed frauds to become much more sophisticated. For example, while phishing emails tend to have a telltale sign of grammatical errors, a bad actor using ChatGPT can generate a more convincing “hook” to ensnare unsuspecting people.
While AI systems make humans more productive for every hour spent, several complex legal issues remain unanswered.
From issues surrounding intellectual property (IP) and infringement to liability, bias and privacy, there is a vast gray area around the legal implications of using AI systems.
How to develop ethical AI systems?
Developing AI systems requires a multi-dimensional approach that considers various principles and practices throughout the AI lifecycle, from concept to deployment and beyond. Here are some of the steps that can be followed when developing an AI system:
- Establish a solid ethical framework: Aligning the development of an AI system with a strong ethical framework allows the system to stay away from bias and makes it more robust against misuse.
- Implement equity and non-discrimination: Training AI models with diverse data representing a wide range of sources allows AI models to avoid most biases.
- Prioritize transparency and explainability: Providing clear details about how AI systems work and making this decision will allow users to understand the reasoning behind the solutions proposed by the system.
- Design for responsibility and accountability: It is important to define who is responsible for different aspects of the AI lifecycle, ensuring that there are clear avenues for recourse if something goes wrong.
