The 2024 AI Index Report is clear on one thing: AI has the power to reduce costs and increase revenues. According to the cited research, AI implementation leads to cost savings for 42% of organizations and a revenue boost for 59%.
But as transformative as AI impact can be, the technology isn’t perfect. The same report highlights several risks inherent to AI projects: privacy and governance, reliability, security, transparency, and fairness.
Here are the five ethical considerations to address these risks during AI development.
Fairness
AI-powered software systems may perpetuate bias because of flaws in the algorithms or training datasets. For example, researchers found that Stable Diffusion was more likely to generate an image of a white man when asked to portray a CEO. Doctors, lawyers, and judges were also more likely to be men than women in the model’s output.
Gender and race bias aren’t the only ones that can persist in an AI system. Current large language models (LLMs) may reproduce language bias as they are trained primarily on English-language data. Political bias is also a possibility: for example, ChatGPT was deemed politically biased by the 2024 AI Index Report.
In practice, ensuring algorithmic fairness involves:
- Using diverse and representative data sets for training
- Incorporating fairness metrics into the development process
- Applying anti-bias techniques (e.g., re-weighting, re-sampling)
Explainability
Complex artificial intelligence systems often have a black-box problem: not even the system’s creators can explain how they produce a specific output.
Take Kevin Roose’s creepy chat with Bing’s Chatbot. Even Microsoft couldn’t explain why the chatbot told the reporter it wanted to be alive or tried to convince him he was unhappy in his marriage.
The black-box problem is prevalent among deep neural networks, a form of machine learning algorithms. Preventing it can be accomplished by calculating prediction accuracy using techniques like Local Interpretable Model-Agnostic Explanations (LIME).
Transparency
While sophisticated models can analyze hundreds or thousands of data points in a matter of seconds, the output isn’t always infallible. AI systems can produce erroneous output, best illustrated by the many examples of LLM chatbots’ hallucinations.
Being transparent with users regarding such limitations is key to ensuring the system doesn’t get accidentally misused.
That’s why AI systems must enable end users to trace the system’s processes and predictions. Otherwise, users may rely on the potentially incorrect output in making decisions, all without questioning it.
Enabling transparency in AI system functioning also means educating users on how it works. With that information, users can:
- Understand the system’s limitations and strengths
- Know when their data is collected for training AI systems and provide informed consent
- Determine whether the system’s output should be used in a specific case
- Analyze the output for potentially biased information
Privacy
AI systems may be dealing with highly sensitive data, such as health records in insurance claim processing or financial information in credit risk assessment. Productivity tools used in a corporate setting may also expose corporate secrets (e.g., Samsung’s proprietary code leak via ChatGPT).
AI developers must protect this data to foster customer trust and prevent security incidents. They should also comply with applicable privacy regulations, such as the GDPR in the European Union and PIPEDA in Canada. Other laws may impose additional obligations (e.g., HIPAA for health information in the U.S.).
Security
In addition to protecting sensitive data from accidental exposure, AI systems also run the risk of being a target of malicious attacks. As AI becomes more prevalent across industries and markets, AI systems become a new way to gain unauthorized access to sensitive data.
To ensure AI system security, developers should:
- Conduct threat modeling before designing the system architecture
- Prevent data leakage
- Conduct regular vulnerability assessments
- Introduce strong data encryption and other cybersecurity measures
In Conclusion
Ensuring your AI system is developed ethically is key to building trust with its future users. Since 52% of the 2024 AI Index Report respondents said AI makes them nervous, building that trust may not be easy. Still, the five considerations above are a good place to start.
Need a reliable AI development partner that ponders these five ethical considerations at every step of the process? Consider S-PRO, an AI and blockchain development company with 10+ years of full-cycle development experience.