#AITradingAffectsForex Addressing bias and fairness in AI-driven Forex trading systems is crucial to ensure that these systems make objective and unbiased trading decisions. Bias can arise from various sources, including: Data bias: Historical data may contain biases, such as unequal representation of different market conditions or instruments. Algorithmic bias: AI models may learn and perpetuate biases present in the data or introduced during the development process. To address bias and fairness in AI-driven Forex trading systems, traders can: Use diverse and representative data: Ensure that training data is diverse, representative, and free from biases. Regularly audit and test models: Regularly evaluate AI models for bias and fairness, using techniques such as backtesting and walk-forward optimization. Implement fairness metrics: Incorporate fairness metrics, such as equality of opportunity and demographic parity, into the development and evaluation process. -Consider human oversight and review: Implement human oversight and review processes to detect and correct potential biases in AI-driven trading decisions.