What is Machine Learning?
Machine learning or ML is a study of computer algorithms that use data to improve users’ experience. It is part of Artificial intelligence and finds application in every walk of life. Everything from the email filters which sort spam to the facial recognition software on our phones is an outcome of ML. Machine learning algorithms use past data to identify, classify, process, and predict future outcomes based on existing data.
What is the role of Machine Learning in Organizations?
Organizations are using machine learning to improve HR processes. For instance, most organizations are switching to an applicant tracking system or an ATS to automate the repetitive process in hiring, like application review. As each job posting receives over 250 applications, it is almost impossible for a human recruiter to scrutinize every resume and pick the ideal candidates.
An AI-based tool can simplify this process by using past recruitment data. Machine learning algorithms filter through tons of resumes and applications to find candidates that match the job posting, skills, experience, salary, etc., from the large candidate pool. This not just reduces the time spent scouting through resumes but also reduces the hiring time.
What is a Machine Learning Bias?
Machine learning bias, or AI bias, occurs when algorithms produce systematically unfair or prejudiced results due to flawed assumptions or poor-quality data. Since machine learning relies on training data, biased or incomplete datasets can lead to inaccurate predictions.
While these biases are often unintentional, they can have serious consequences, including poor customer experiences, reduced sales, unfair decisions, and potentially harmful outcomes.
Amazon, the tech giant, uses machine learning and artificial intelligence for numerous operations. However, it was not until 2015 that the organization realized the bias in its AI systems against women. The data collected over the previous decade regarding resumes showed that most of the applicants were male. Hence the algorithm began discriminating against female applicants. However, the organization resolved the issue and updated its recruitment data to resolve the bias.
The History of Bias In AI
A study by the University of Virginia and the University of Washington found that AI systems can develop biased associations based on their training data. In one example, the algorithm linked “cooking” with “woman” because that pattern appeared frequently in the data.
As a result, it incorrectly identified the gender of a person as female, even when the image showed a man cooking. This example highlights why organizations must use diverse, high-quality training data to reduce bias and improve AI accuracy.
What Are the Different Types of Machine Learning Bias?
A bias can be introduced into the machine learning system through numerous means. Common scenarios, or types of bias, include the following:
Algorithm Bias:
An algorithm bias is when an error in the algorithm computes the data and interferes with the machine’s ability to make a decision. In other words, this happens when there are repeatable errors in the system, which creates a biased outcome that is unfavorable to other users while privileging one arbitrary group. This can be fixed by increasing the scrutiny of data to reduce the bias consciously. One example of the harmful effects of artificial intelligence bias is the findings of a private hospital in the US.
In 2018, the healthcare service provider tried to understand the patients who would need additional care. The data findings suggested an unreasonable bias towards patients who were white over other ethnicities. As a result, researchers had to invest additional time and input new data to reduce this bias by 80%. Had there been no human intervention, the AI bias would have continued to discriminate severely.
Sample Bias:
Sample bias is a common issue when humans collect data. The belief was that machine learning to data collection would reduce the errors and eliminate sampling bias. However, humans are involved in computing data, which means a greater chance of sampling bias, even in machine learning. A data scientist’s job is to ensure that the sample they are building on aligns with the environment they would be deployed in.
Measurement Bias:
Measurement bias occurs when data is labeled incorrectly, causing AI systems to learn and repeat those mistakes. For example, if an image recognition model labels dogs as cats, it will continue classifying similar images incorrectly.
While this may be a minor issue in photo apps, the consequences can be far more serious in industries like healthcare or recruitment, where incorrect labels can lead to unfair decisions or inaccurate outcomes.
How To Avoid Machine Learning Bias?
Fixing bias in machine learning is not easy to resolve as bias can creep in at any stage of the deep learning process. Data scientists aim to develop a machine-learning system that can achieve social and legal outcomes without bias once introduced into a social context. However, the fact that biases creep in unconsciously during the learning process makes it hard to detect. Once the data is entered, it is impossible to understand the downstream impact it could create.
Addressing AI bias begins with identifying it. For example, Amazon had to revise its recruiting algorithm after discovering it unfairly penalized female candidates, removing gender-related signals to improve fairness.
Eliminating bias is an ongoing process because AI reflects the data and decisions made by humans. Organizations must continuously monitor and improve their models, while following ethical AI principles to build systems that are fair, transparent, and trustworthy.
- Research your users in advance and be aware of the potential use cases.
- Use a diverse team of data scientists and data labelers.
- Include inputs from diverse sources.
- Use multi-pass annotation for projects to ensure data accuracy and to avoid bias. Sentiment analysis, content moderation, and intent analysis can help overcome bias.
- Analyze and update data regularly. Look for anomalies and analyze the predictions for a possible data bias.
- Use tools from Google, IBM, and Microsoft to test your data for bias as part of the development cycle.
Conclusion
Machine Learning Bias is a key challenge in organizations trying to solve problems through people’s data. Fortunately, organizations can use some of the debiasing approaches like increasing diversity to improve data quality. Organizations and HR leaders often talk about recruiting people of color and women to increase diversity across the organization and serve as equal opportunity employers.
However, the marginalized communities are often underrepresented, and the statistical minorities lead to misrepresenting predictive data. Reverse engineering the AI algorithms can be made possible by consciously increasing the diversity of hires across the organization. IBM is currently working on debiasing suits as part of its AI fairness project, which will help organizations identify the AI bias and mitigate against it.
Peterson Technology Partners (PTP) has partnered with some of the biggest Fortune brands to offer excellence of service and best-in-class team building for the last 25 years.
PTP’s diverse and global team of recruiting, consulting, and project development experts specialize in a variety of IT competencies which include:
- Cybersecurity
- DevOps
- Cloud Computing
- Data Science
- AI/ML
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Peterson Technology Partners is an equal opportunities employer. As an industry leader in IT consulting and recruitment, specializing in diversity hiring, we aim to help our clients build equitable workplaces.
