To mitigate algorithmic bias, businesses can take several proactive steps: 1. Diverse Data: Ensure that the training data is representative of all demographics to avoid replicating existing biases. 2. Bias Audits: Regular audits and testing of algorithms for biases can help identify and rectify unfair practices. 3. Transparency and Accountability: Maintaining transparency in algorithm design and decision-making processes can foster accountability. This can include documenting how algorithms are developed and the criteria used for decision-making. 4. Human Oversight: Incorporating human oversight in critical decision-making processes can help catch biases that an algorithm might miss. 5. Cross-functional Teams: Employing cross-functional teams that include ethicists, sociologists, and legal experts can provide a well-rounded perspective on the potential impacts of algorithmic decisions.