Interest in Artificial Intelligence (AI) is increasing as more individuals and businesses witness its benefits in various use cases. However, there are also some valid concerns surrounding AI technology:
In this article, we focus on AI bias and will answer all important questions regarding biases in artificial intelligence algorithms from types and examples of AI biases to removing those biases from AI algorithms.
AI bias is an anomaly in the output of machine learning algorithms, due to the prejudiced assumptions made during the algorithm development process or prejudices in the training data.
AI systems contain biases due to two reasons:
Figure 1. Inequality and discrimination in the design and use of AI in healthcare applications
Technically, yes. An AI system can be as good as the quality of its input data. If you can clean your training dataset from conscious and unconscious assumptions on race, gender, or other ideological concepts, you are able to build an AI system that makes unbiased data-driven decisions.
However, in the real world, we don’t expect AI to ever be completely unbiased any time soon due to the same argument we provided above. AI can be as good as data and people are the ones who create data. There are numerous human biases and ongoing identification of new biases is increasing the total number constantly. Therefore, it may not be possible to have a completely unbiased human mind so does AI system. After all, humans are creating the biased data while humans and human-made algorithms are checking the data to identify and remove biases.
What we can do about AI bias is to minimize it by testing data and algorithms and developing AI systems with responsible AI principles in mind.
Firstly, if your data set is complete, you should acknowledge that AI biases can only happen due to the prejudices of humankind and you should focus on removing those prejudices from the data set. However, it is not as easy as it sounds.
A naive approach is removing protected classes (such as sex or race) from data and deleting the labels that make the algorithm biased. Yet, this approach may not work because removed labels may affect the understanding of the model and your results’ accuracy may get worse.
So there are no quick fixes to removing all biases but there are high level recommendations from consultants like McKinsey highlighting the best practices of AI bias minimization:
Steps to fixing bias in AI systems:
A data-centric approach to AI development can also help minimize bias in AI systems.
IBM released an open-source library to detect and mitigate biases in unsupervised learning algorithms that currently has 34 contributors (as of September 2020) on Github. The library is called AI Fairness 360 and it enables AI programmers to
However, AI Fairness 360’s bias detection and mitigation algorithms are designed for binary classification problems that’s why it needs to be extended to multiclass and regression problems if your problem is more complex.
IBM’s Watson OpenScale performs bias checking and mitigation in real time when AI is making its decisions.
Using What-If Tool, you can test performance in hypothetical situations, analyze the importance of different data features, and visualize model behavior across multiple models and subsets of input data, and for different ML fairness metrics.
Bay Area startup Sanas developed an AI-based accent translation system to make call center workers from around the world sound more familiar to American customers. The tool transforms the speaker’s accent into a “neutral” American accent in real time. As SFGATE reports, Sanas president Marty Sarim says accents are a problem because “they cause bias and they cause misunderstandings.”
Racial biases cannot be eliminated by making everyone sound white and American. To the contrary, it will exacerbate these biases since non-American call center workers who don’t use this technology will face even worse discrimination if a white American accent becomes the norm.
With the dream of automating the recruiting process, Amazon started an AI project in 2014. Their project was solely based on reviewing job applicants’ resumes and rating applicants by using AI-powered algorithms so that recruiters don’t spend time on manual resume screen tasks. However, by 2015, Amazon realized that their new AI recruiting system was not rating candidates fairly and it showed bias against women.
Amazon had used historical data from the last 10-years to train their AI model. Historical data contained biases against women since there was a male dominance across the tech industry and men were forming 60% of Amazon’s employees. Therefore Amazon’s recruiting system incorrectly learnt that male candidates were preferable. It penalized resumes that included the word “women’s,” as in “women’s chess club captain.” Therefore, Amazon stopped using the algorithm for recruiting purposes.
A health care risk-prediction algorithm that is used on more than 200 million U.S. citizens, demonstrated racial bias because it relied on a faulty metric for determining the need.
The algorithm was designed to predict which patients would likely need extra medical care, however, then it is revealed that the algorithm was producing faulty results that favor white patients over black patients.
The algorithm’s designers used previous patients’ healthcare spending as a proxy for medical needs. This was a bad interpretation of historical data because income and race are highly correlated metrics and making assumptions based on only one variable of correlated metrics led the algorithm to provide inaccurate results.
There are numerous examples of human bias and we see that happening in tech platforms. Since data on tech platforms is later used to train machine learning models, these biases lead to biased machine learning models.
In 2019, Facebook was allowing its advertisers to intentionally target adverts according to gender, race, and religion. For instance, women were prioritized in job adverts for roles in nursing or secretarial work, whereas job ads for janitors and taxi drivers had been mostly shown to men, in particular men from minority backgrounds.
As a result, Facebook will no longer allow employers to specify age, gender or race targeting in its ads.
Krita Sharma, who is an artificial intelligence technologist and business executive, is explaining how the lack of diversity in tech is creeping into AI and is providing three ways to make more ethical algorithms:
Barak Turovsky, who is the product director at Google AI, is explaining how Google Translate is dealing with AI bias:
Hope this clarifies some of the major points regarding biases in AI. For more on how AI is changing the world, you can check out articles on AI, AI technologies and AI applications in marketing, sales, customer service, IT, data or analytics.
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Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 55% of Fortune 500 every month. Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple. Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization. He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch like Business Insider. Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
