What Is Ethical AI?
Ethical AI is the development and use of artificial intelligence systems in a way that considers and prioritises ethical principles. The ethics of AI can also be concerned with the moral behaviour of humans as they design, make, use and treat AI systems.
AI is a constantly evolving field, and as such, several important ethical considerations must be made, keeping future developments in mind to serve as guide rails for AI development.
According to a 2020 research paper, ethical AI considerations have converged globally around five principles (transparency, justice and fairness, non-maleficence, responsibility and privacy).
What Is Bias In AI?
The most significant ethical consideration with any AI system is the bias it inherits, either from the system’s developers or from the data it trains on. Since all data produced by humans carries the risk of biases, it becomes inevitable for AI systems to acquire biases.
AI systems can acquire five types of biases:
- 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 occurs when the data used to train an AI system is incomplete, unrepresentative or inaccurate. For instance, an AI facial recognition system trained primarily on images of white men may struggle to accurately identify the faces of women or people of colour.
- Confirmation bias: This happens when an AI system is designed to reinforce existing beliefs or expectations. For example, a news recommendation algorithm that prioritises articles confirming users’ existing political views can create echo chambers.
- Stereotyping bias: This occurs when an AI system makes generalisations about individuals or groups based on their perceived characteristics, often perpetuating harmful stereotypes. For example, a language translation tool that consistently translates gender-neutral terms into masculine pronouns may reinforce gender stereotypes.
- Exclusion bias: This arises when certain groups or individuals are entirely excluded from the data used to train an AI system, leading to their needs and perspectives being overlooked. For example, an AI healthcare system trained primarily on data from wealthy countries may not be effective in providing care for people in developing countries.
How Can AI Systems Be Used For Spreading Misinformation?
Unfortunately, as AI systems, especially generative AI systems, come closer to creating humanlike responses and art, malicious actors have come to misuse these systems.
One of the recent examples of this has been the rise of deepfakes across the globe. In India, the deepfakes started by targeting popular celebrities, with politicians and other important people across various circles being targeted as well.
Further, GenAI tools like ChatGPT have also allowed frauds to become much more sophisticated. For instance, while phishing emails tend to have a telltale sign of being rife with grammatical errors, a bad actor using ChatGPT can generate a more convincing ‘hook’ for unsuspecting people to be trapped.
What Are The Legal Issues With AI-Based Tools?
While AI systems are making humans more productive for each hour they spend, several complex legal issues remain unaddressed.
From questions around intellectual property (IP) ownership and violations to accountability, bias and privacy, there is a large grey area around the legal implications of AI systems being used.
How Can Ethical AI Systems Be Developed?
Developing AI systems requires a multifaceted approach that considers various principles and practices throughout the AI life cycle, from concept to deployment and beyond. Here are some of the steps that can be followed while developing an AI system:
- Establish A Strong Ethical Framework: Aligning the development of an AI system with a strong ethical framework allows the system to stay away from biases and makes the system more robust against misuse.
- Implement Fairness & Non-Discrimination: Training AI models with diverse data representing a wide range of sources allows AI models to steer clear of most biases.
- Prioritising Transparency & Explainability: Providing clear details on how AI systems work and arrive at that decision will allow users to understand the reasoning behind solutions the system comes up with.
- Design For Accountability & Responsibility: It is important to define who is responsible for different aspects of the AI lifecycle, ensuring there are clear avenues for redress in case of issues.
