How to prevent AI bias: 5 tips for data scientists

AI is increasingly involved in heavy business processes such as credit assessment and CV screening to identify ideal candidates. As a result, AI and its consequences can be understood Under the microscope. The main question that worries implementers: Is the AI ​​algorithm biased?

Bias can creep in through multiple ways, including sampling practices that ignore large segments of the population, and confirmation bias, in which a data scientist only includes data sets that align with their view of the world.

Here are several ways data scientists tackle the problem.

1. Understand the potential for AI bias

Supervised learning, one of the subsets of artificial intelligence, works on rote ingestion of data. By learning under ‘supervised’, the trained algorithm makes decisions on data sets that it has never seen before. distance The principle of “Enter garbage, take out garbage”the quality of an AI’s decision can only be as good as the data it ingests.

Data scientists should evaluate their data to ensure that it is an unbiased representation of the realistic equivalent. To address confirmation bias, the diversity of data teams is also important.

2. Increase transparency

AI still faces a challenge due to the opacity of its operations. Deep learning algorithms, for example, use neural networks modeled on the human brain to arrive at decisions. But how they got there remains unclear.

“Part of the move toward ‘explainable AI’ is to highlight how you train data and how you use algorithms,” said Jonathon Wright. Keysight Technologies’ lead technology evangelist, testing technology provider.

While making AI explainable will not completely prevent biases, understanding the cause of bias is a critical step. Transparency is especially important when companies use AI software from third-party vendors.

3. Institute Standards

When Publishing artificial intelligenceWright said, organizations must follow a framework that standardizes production while ensuring ethical models.

Wright has cited the European Union’s Artificial Intelligence Act as a game-changer in an effort to clean up bias-free technology.

4. Test models before and after publication

Testing AI and machine learning models is one way to prevent biases before the algorithms are released into the wild.

Software companies, designed specifically for this purpose, are becoming more and more popular. “It’s where the industry is headed right now,” Wright said.

5. Use of synthetic data

You want data sets that represent a larger population, but “just because you have real data from the real world doesn’t mean it’s unbiased,” Wright noted.

In fact, the learning biases of AI from the real world pose a risk. To address this problem, synthetic data could be seen as a potential solution, said Harry Kane, CEO and co-founder of Hazy, a startup that creates synthetic data for financial institutions.

Synthetic data sets are statistically representative versions of real data sets and are often published when the original data is related to privacy concerns.

Ken confirmed that Use of synthetic data To address bias is an ‘open research topic’ and this approximation of data sets – eg, getting more women into resume models – may introduce a different type of bias.

Kane said that synthetic data sees the most attraction in the evening outside of “low dimensional structured data” such as images. For more complex data, “It can be a bit of a Whack-a-Mole game, where you solve one bias but you can introduce or amplify others….Data bias is a rather thorny issue.”

However, it is a problem that must be solved, given that the technology is growing at an impressive annual rate of 39.4%, according to a study by Zion Market Research.

About the author

Purnima dad, a bullet in the headPurnima Apte Trained engineer turned writer specializing in robotics, artificial intelligence, IoT, 5G, cybersecurity, and more. Purnima is an award winning journalist from the South Asian Journalists Association, and loves to learn and write about new technologies and the people behind them. Its client list includes numerous B2B and B2C outlets, which commission features, profiles, white papers, case studies, infographics, video scripts, and industry reports. Poornima is also a card-holding member of the Cloud Appreciation Society.

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