Bias mitigation methods for binary classification decision-making systems have been widely researched due to the ever-growing importance of designing fair machine learning processes that are impartial and do not discriminate against individuals or groups based on protected personal characteristics. In this paper, we present a structured overview of the research landscape for bias mitigation methods, report on their benefits and limitations, and provide recommendations for the development of future bias mitigation methods for binary classification.

We introduce a machine-in-the-loop pipeline that aims to address root causes of unwanted bias in natural language based supervised machine learning tasks in the education domain. Learning from the experiences of students is foundational for education researchers, and academic administrators. 21st-century skills learned from experience are becoming a core part of college and career readiness as well as the hiring process in the new knowledge economy. Minoritized students demonstrate these skills in their daily lives, but documenting, assessing, and validating these skills is a huge problem for educational institutions. As an equity focused online platform, LivedX translates minoritized students' lived experiences into the 21st century skills, issues micro-credentials, and creates personal 21st century skills portfolio. To automate the micro credential mining from the natural language texts received from the students' submitted essays, we employed a bag-of-word model to construct a multi-output classifier. Despite our goal, our model initially exacerbated disparate impact on minoritized students. We used a machine-in-the-loop model development pipeline to address the problem and refine the aforementioned model to ensure fairness in its prediction.

Recently, my family finally decided to transition from the Wii console (yes people still use these) and invested in a PS4 with the Camera. When we were setting up the camera, it would not detect us. We went online and searched up what we could do. We ended up buying 2 huge lights to add to the living room and the camera still couldn't recognize. However, our mom (who is a few shades lighter) happened to walk behind us and the camera immediately caught her.

The possible risk that AI systems could promote discrimination by reproducing and enforcing unwanted bias in data has been broadly discussed in research and society. Many current legal standards demand to remove sensitive attributes from data in order to achieve "fairness through unawareness". We argue that this approach is obsolete in the era of big data where large datasets with highly correlated attributes are common. In the contrary, we propose the active use of sensitive attributes with the purpose of observing and controlling any kind of discrimination, and thus leading to fair results. Systematic, unequal treatment of individuals based on their membership of a sensitive group is considered discrimination.

In 2016, the World Economic Forum claimed we are experiencing the fourth wave of the Industrial Revolution: automation using cyber-physical systems. Key elements of this wave include machine intelligence, blockchain-based decentralized governance, and genome editing. As has been the case with previous waves, these technologies reduce the need for human labor but pose new ethical challenges, especially for artificial intelligence development companies and their clients. The purpose of this article is to review recent ideas on detecting and mitigating unwanted bias in machine learning models. We will discuss recently created guidelines around trustworthy AI, review examples of AI bias arising from both model choice and underlying societal bias, suggest business and technical practices to detect and mitigate biased AI, and discuss legal obligations as they currently exist under the GDPR and where they might develop in the future. All models are made by humans and reflect human biases.

I'll never forget my "aha" moment with bias in AI. I was working at IBM as the product owner for Watson Visual Recognition. We knew that the API wasn't the best in class at returning "accurate" tags for images, and we needed to improve it. I was nervous about the possibility of bias creeping into our models. Bias in Machine Learning (ML) models is the exact sort of problem the ML community has seen time and again, from poor facial recognition of diverse individuals to an AI beauty pageant gone awry and countless other instances.