The pipeline of a fair ML practitioner is generally divided into three phases: 1) Selecting a fairness measure. 2) Choosing a model that minimizes this measure. 3) Maximizing the model's performance on the data. In the context of group fairness, this approach often obscures implicit assumptions about how bias is introduced into the data. For instance, in binary classification, it is often assumed that the best model, with equal fairness, is the one with better performance. However, this belief already imposes specific properties on the process that introduced bias. More precisely, we are already assuming that the biasing process is a monotonic function of the fair scores, dependent solely on the sensitive attribute. We formally prove this claim regarding several implicit fairness assumptions. This leads, in our view, to two possible conclusions: either the behavior of the biasing process is more complex than mere monotonicity, which means we need to identify and reject our implicit assumptions in order to develop models capable of tackling more complex situations; or the bias introduced in the data behaves predictably, implying that many of the developed models are superfluous.

At a time when the value of data is never higher, many companies are investing in "artificial intelligence," in one or the other form, to help them transform business processes and make decisions faster and more accurate. But, despite what some purport, the fact remains that "training and learning" are still only as good as the data it uses -- and as such can be prone to all of the same cognitive biases that plague human decision-makers. These models account for most of the digital decisions AI systems make today, using information from behavioral, physiological, or any other source their creators deem relevant. Artificial intelligence has grown exponentially, but how can organizations ensure that AI doesn't make mistakes that are more costly than the technology itself? Cognitive bias is a real problem for AI developers trying to create systems that can make fair and unbiased decisions.

Artificial neural networks have given AIs the functionality for complex problem solving and pattern recognition, and they have entered the workforce, particularly in areas of big data analysis and global finance. As we begin to interact with and study these new learning machines, interesting questions arise concerning unbiased decisions. Are they going to take on human behavioral and gender distinctions (gender identity), because they have been programmed with data sets that have unconscious bias? Will those who are giving the learning machines feedback to focus their problem solving allow behavioral constraints into the teaching? If we give the AIs a woman's voice, and a woman's name, will we interact with her as if she was a woman?

Humans look to the power of artificial intelligence (AI) to make better and unbiased decisions. However, a new study has found that the technology is becoming racist and sexist as it learns, thus hindering its ability to make balanced resolutions. Researchers discovered that the better AI becomes at interpreting the human language, the more likely it will adopt human bias about race and gender that lurks within the data it is fed. A Study found that AI is becoming racists and sexist as it learns, hindering its ability to make unbiased decisions. Princeton University conducted a word associate task with the algorithm GloVe, an unsupervised AI that uses online text to understand human language.