Artificial intelligence (AI) has tremendous potential and offers numerous benefits, but it also comes with several challenges and threats. AI being a new field, government agencies and organizations need to take a leap in understanding its pitfalls to ensure it is developed and used responsibly.
Common pitfalls of artificial intelligence are:
AI systems rely on huge amounts of data, therefore, raise data privacy concerns. The sensitive data can be potentially misused or can be accessed by unauthorized entities. AI systems are also vulnerable to hacking attacks to either access confidential data or to manipulate data to deceive the AI model. There are also ethical concerns, such as AI can be used for surveillance, spreading misinformation, and scams. AI generated images can be one such example, where the images can be used to scam people. AI models can inherit biases which are present in their training data leading to biased predictions and outcomes. Also, it can be difficult for AI to adjust to challenging situations where it may have to generalize new data, therefore, questioning its robustness. The lack of transparency in AI models can be challenging in understanding how these models were trained and arrive at their predictions.
Apart from these concerns, there also lies legal and regulatory challenges. AI usage picked up like wildfire but we are way behind in enacting regulation. Developing regulations can be challenging as various countries may have different approaches. There are also questions of data ownership – who owns and controls the data? Who should be liable in case of misuse? So, there is an urgent need to evolve with the changing technology landscape for AI to be used ethically. Training large AI models can have significant energy consumption, raising environmental concerns.
Over reliance on AI models can lead to job displacement and reduced human oversight in the processes. It is important to anticipate the ways AI can be misused and address these pitfalls by collaborating with various stakeholders as we do not know what the landscape would be after 5-10 years. Ensuring AI systems work reliably across diverse situations should be our next step forward by prioritizing ethical considerations, transparency, fairness, and accountability.
