Real-Time AI Decision-Making in Financial Markets: Opportunities, Risks, and Ethical Considerations

This investigation explores the use of artificial intelligence in the context of real-time market decision-making by comparing AI-driven techniques with more traditional decision-making models. Through the completion of a comprehensive review of existing literature, the research explores the advantages and disadvantages of using artificial intelligence in market environments that are in a constant state of flux. Through the use of artificial intelligence technology, companies have the potential to acquire a competitive edge over conventional ways by means of enhancing the accuracy of their decisions, maximising operational efficiency, and reacting rapidly to market volatility. The use of artificial intelligence brings up a variety of challenges, both from an operational standpoint and from an ethical one. These issues include algorithmic bias, data privacy, transparency, and the potential for systemic risks. As shown by the study, the implementation of responsible artificial intelligence adoption can only be accomplished via the establishment of robust governance structures and ethical norms. It is vital that engineers, business strategists, regulators, and end-users collaborate in order to guarantee that decision-making that is powered by artificial intelligence adheres to both societal values and corporate goals. Recommendations for future research include investigating the long-term implications of artificial intelligence on market dynamics and consumer behaviour, developing artificial intelligence systems that are simple to comprehend and explain, and determining methods to mitigate bias in real-time analytics. This research is dedicated to the pursuit of balance between technological innovation and openness, as well as justice and responsibility. It offers crucial insights for organisations that want to use artificial intelligence into their market decision-making processes in a manner that is both ethical and effective.

Paper

The full text of this publication is not hosted on 44B due to licensing.

Read it at OpenAlex

Similar papers

© 2026 NYSGPT2525 LLC