Fairness in Machine Learning Systems | Quick Guide

Introduction to Machine Learning (ML)

Machine Learning (ML) has proven to be one of the most transformative technological advances in the last decade. In today's highly competitive business landscape, ML is helping firms to accelerate digital transformation and enter the age of automation. Some claim that AI/ML is essential to remain relevant in some industries, such as digital payments and fraud detection in banking or product suggestions.

The ultimate acceptance and pervasiveness of machine learning algorithms in corporations are well documented, with several companies using machine learning at scale across sectors. Machine learning is now used in some way or another by every other tool and software on the Internet. Machine Learning has become so prevalent that it is now the go-to method for businesses to handle various issues.


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What does fairness mean for Machine Learning systems?

Fairness in Machine Learning refers to numerous initiatives to address algorithmic bias in machine learning-based automated decision systems. Definitions of fairness and prejudice, like many other ethical ideas, are constantly contentious. Fairness and prejudice are regarded as necessary anytime a choice affects people's lives, particularly when a collection of sensitive characteristics, such as gender, race, sexual orientation, handicap, and so on, are involved. The algorithmic bias in machine learning is well recognized and thoroughly investigated. Outcomes may be skewed by various circumstances and hence be regarded as unfair in relation to specific groups or people. One example is how social media sites give customized news to users.

Why is fairness important in Machine Learning (ML)?

Fairness in data and machine learning algorithms is essential for designing safe and responsible AI systems from the start. Both technical and corporate AI players are constantly striving for fairness to handle issues such as AI bias effectively. While accuracy is one metric for assessing a machine learning model's performance, fairness allows us to comprehend the practical consequences of deploying the model in a real-world context.

The act of recognizing bias provided by your data and ensuring your model produces equal predictions across all demographic groups is known as fairness. Rather than considering fairness as a distinct project, it is critical to use fairness analysis across the whole ML process, making sure to constantly assess your models from the standpoint of fairness and inclusiveness. This is especially crucial when AI is used in critical business operations influencing various end-users, such as credit application evaluations and medical diagnoses.


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Why do we care about fairness in Machine Learning?

Our world would not be a better place if we made judgments based on preconceptions and qualities unrelated to the decision-making process. It would only be a matter of time until we, as people, were victims of prejudice if such a world existed.

Here are some recent instances of incidents in which ML systems were not intended to be biassed, but when put into practice, they proved to be discriminatory and damaging to the public:

  • COMPAS is a case management and decision support tool used by US courts to estimate the possibility of a defendant becoming a repeat offender. According to ProPublica, the COMPAS algorithm incorrectly projected that black offenders were more likely to recidivate than they were.
  • The Amazon Hiring Algorithm Amazon worked on a project to automate the candidate resume evaluation process in 2014. Amazon discontinued its experimental ML recruiting tool after being discovered to be discriminatory toward women.
  • Apple Card - The Apple Card is a credit card provided by Goldman Sachs that Apple Inc designed. It was released in August of 2019. After a few months of protest, customers began to protest that the card's algorithms were biased against women.

What causes bias in ML?

Because decision-making solely depends on facts, machine learning algorithms appear objective. In a normal workflow, an algorithm is provided a significant quantity of representative data to learn from, and what it sees defines its decision-making process. However, whatever data we provide, the algorithm describes, directly or indirectly, societal decisions that have already been made. If black defendants are already mistakenly found to be more dangerous than white defendants, an algorithm will learn this from the data as if it were true. This bias inaccessible training data creates a feedback loop in which the algorithm makes unjust conclusions based on what it has learned, perpetuating more social inequality and tainting future data.

Too much uniformity in the data can also lead to unfairness. A typical example is Nikon's facial recognition technology automatically recognizes blinking in photographs. However, this system incorrectly identified Asians as blinking at a far greater rate than other groups. Although Nikon did not specify the specific explanation, this circumstance is a textbook illustration of what might happen when an algorithm is only provided data from a subset of the population. The algorithm would not have been able to accurately understand what an Asian person is blinking looks like if it had not seen numerous samples of Asian individuals.


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How to eliminate bias in Machine Learning?

Getting rid of data bias in machine learning is a never-ending task. Near-constant data cleansing and machine learning bias are required to construct reliable and meticulous data gathering systems.

Machine learning bias may be avoided with education and proper governance. This is because eliminating data bias necessitates first determining where the prejudice exists. Once found, the bias in the system may be eliminated. (See Automation: What Does the Future Hold for Data Science and Machine Learning?)

However, it is frequently challenging to determine whether the data or model is skewed.

Nonetheless, specific procedures may be performed to handle this type of circumstance. These are some examples:

  • Testing and verifying machine learning system findings guarantees that algorithms or data sets do not cause bias.
  • Ensure a varied mix of data scientists and data labelers.
  • Setting specific standards for data labeling expectations so that data labelers know what procedures to take when annotating.
  • Bringing several source inputs together to provide data diversity.
  • Analyzing data regularly and keeping track of faults so they may be resolved as soon as feasible.
  • Using a domain expert to examine the gathered and annotated data. Someone outside the team may see unchecked prejudices.
  • Examining and inspecting ML models using external resources such as Google's What-if Tool or IBM's AI Fairness 360 Open Source Toolkit.
  • Implementing multi-pass annotation for any project where data perfection is likely to be skewed.

Conclusion

Fairness in machine learning is about more than simply preventing a model from injuring a protected group; it may also assist focus attention where it is most needed. Models might be used to provide translation services in regions where in-person translators are uncommon, medical knowledge in areas where experts are scarce, and even enhance diagnostic accuracy for rare disorders frequently misdiagnosed. We can ensure that all patients benefit from this technology by prioritizing fairness in building, deploying, and assessing the models.