Consider another example. Machine learning algorithms can be used in healthcare to recommend diagnoses and treatments. However, these machines can only issue recommendations based on what they 'learn' from the databases used to train them, and, unfortunately, often these databases are affected by the same biases that have historically affected medical research and literature (since the **data** come precisely from there). Using big **data** for diagnostic purposes is, therefore, quite problematic, especially when members of social minorities are involved. In a recent paper, 15, 2) argue that "the very definition of a condition or disease hinges on gender or ethnicity, and has been used in a discriminatory fashion. Even for conditions that do not rely on race or gender, the gender-or race-specific presentation of a condition may be poorly understood, or ignored, in medical education and literature."