Artificial intelligence’s rapid development initially promised to improve efficiency and cut costs, but this progress has recently tapered off amid growing concerns about the potential consequences of unethical employment. Due to its novelty, regulatory frameworks surrounding AI's implementation remain underdeveloped, letting private companies rely on it as a central component for their operations with a dangerous absence of oversight. Early on, many assumed that AI would focus on relatively simple tasks requiring low-level decision-making; however, its sophistication over time has enabled it to outperform humans in high skill-based situations, driving global spending on machine learning to skyrocket. For instance, Google's DeepMind Health has developed an artificial neural network capable of diagnosing eye diseases from retina scans, surpassing the accuracy of human specialists.
However, do certain elements of human judgment remain indispensable? The truth is, AI still lacks much of the cognitive understanding necessary to navigate our complex and nuanced society. Moreover, our own prejudices can seep into the data and algorithms that we create, and currently, AI is not yet equipped to identify or correct them—so who is responsible when it makes a biased decision? Due to AI's inability to take a top-down, contextual approach to decision-making, many experts emphasize the importance of using AI mindfully and in a way that prioritizes fairness, accountability, and transparency.
Below you’ll find a collection of resources that explore how we can use AI effectively whilst mitigating potential risks and the consequences of failing to do so.
