Statice is Now part of Anonos Data Embassy Platform

Statice's synthetic data technology is now part of Anonos Data Embassy, the award-winning data security and privacy solution.


The impact of data bias on your business & the benefits of fair AI

risk of bias
Joanna Kamińska

The impact of data bias on your business & the benefits of fair AI

According to the 2022 Gartner CIO and Technology Executive Survey, 48% of CIOs have already deployed or plan to deploy AI and machine learning technologies within the next 12 months. Although ML model development is getting popular, AI bias is lurking in the success of many business projects. 

State of AI Bias by DataRobot revealed that companies with biased algorithms experienced harmful consequences. 

But not all is washed up yet. If you consider running an AI project but are hesitant because of possible data bias impact on business, we’re here to help. In this article, you’ll learn about AI & data bias' impact on business. And how introducing fairness in AI might pay off for your business. 

These are the key takeaways:

  • What is data bias in AI and machine learning.
  • Concerns around AI & data bias and its real impact on business, including examples.
  • The EU AI Act & why it is relevant.
  • Fairness in AI: How businesses can benefit from AI bias mitigation.
  • Data ethics in AI: Best practices for business leaders to implement.

What is data bias in AI and machine learning

Data bias in artificial intelligence (AI) and machine learning (ML) is an error that occurs when specific data points in a dataset are over or underrepresented. As the input data is skewed, you get errors in the output. 

Machine learning models trained on biased data inaccurately represent the desired use cases. As a result, the quality, accuracy, and reliability for analysis are low

The most common AI data bias types are the following:

  • Social or systemic biases that discriminate against the specific group(s).
  • Data sampling that over- or underrepresents specific groups.
  • The cognitive biases of a data scientist, analyst, or researcher.
  • In the field of data science, bias is perpetuated by the wrong methods of data analysis, data collection, clearing and formatting.
  • Implicit biases representing attitudes and stereotypes we hold about others, even when we are unaware of it.

You can learn more about the common types of bias in data analysis, data science and artificial intelligence here.

AI biases can have harmful consequences. From producing unfair or erroneous results to making a dent into your company’s reputation or damaging your bottom line. No matter the cause, it’s best to mitigate AI bias in advance.

And talking about consequences, let’s jump into the next section. 

AI & data bias concerns and its real impact on business, including examples

The concern around data bias is growing. DataRobot conducted a survey on 350 U.S. and U.K.-based technology leaders, including CIOs, IT directors, IT managers, data scientists, and development leads, who use or plan to use AI.

According to the survey analysis, 54% of technology leaders say they are very or extremely concerned about AI bias. It’s 12% more than in 2019 (42% shared such a sentiment). At the same time, the overwhelming majority (81%) are calling for more AI regulation.

So, the main concerns around bias in AI are fear of losing customer trust, reputation, and exposure to detailed compliance checks.

What's the real impact of data bias in AI and ML on businesses?

According to the DataRobot report findings, the concern over bias in AI is justifiable. Out of 350 organizations surveyed, 36% (126 organizations) said their organization suffered from biased data in one or several of their algorithms.

For example, the Consumer Federation of America states that in Oregon, women will be charged more for their car insurance. It’s on average $976.05 annually for basic coverage, while men will be quoted a premium of $876.20. With everything else being equal it’s a $100 gap or 11.4% gender penalty. This AI bias can easily lead to loss of customers’ trust and, as a consequence, the revenue. Every client who is outraged by the inequality might find a fairer insurance company. 

Losing employees was another issue affecting organizations. Based on Amazon’s recruiting tool example, the system wasn’t rating candidates for software development positions in a gender-neutral way. This is because Amazon's AI was trained on resumes from over a decade ago. As the tech industry is dominated by men, the AI pattern advantaged resumes from a male group.

The real impact of data bias hits hard into the most valuable assets their companies had – specialists. Such a bias could not only harm your bottom line, but also damage your employer branding strategy.

Biased data and AI & ML models directly affects your company reputation. For example, the analysis of the racial bias in pulse oximetry measurement found that black patients had nearly 3 times the frequency of occult hypoxemia that was not detected by pulse oximetry as white patients. This might put some patients’ lives at risk.

And according to NCBI, “generally applicable” AI models in healthcare aren’t developed on datasets reflecting the diversity of patients. 

Bias puts the reputation of your healthcare entity at risk, resulting in lost patients, revenue, and legal issues.

What’s more, Gartner states that by 2022, 85% of AI projects will deliver erroneous outcomes due to bias in data, algorithms, or the teams responsible for managing them. Skewed data impacts your business and the problem is real.

As you can see, AI bias creates a vicious cycle. It’s because all the harmful consequences of data bias are interconnected with each other. If you don’t have trust or reputation, you won’t get revenue. If you lose your customers’ trust, it’s a long way to get it back.

Sometimes even clients that aren’t directly affected by bias’ harmful consequences might leave your business too. It’s because clients who value fairness or equality don’t want to support brands that fail in this area. 

A good way to keep your business safe is to mitigate bias in machine learning and AI. How to do it? To begin with, let's examine the relevant legislation.

The EU AI Act

With the upcoming enactment of the EU AI Act, it is important that you familiarize yourself with this topic. The Act is still a work in progress but the idea is to regulate AI systems within enterprises to protect people's rights and avoid misuses of such systems.

The mission is to build a framework around AI so that businesses produce fair, accountable and safe AI systems. If you’re already having AI projects or thinking about starting them, gather all enterprise requirements and get prepared. 

If you proactively prepare your business for the upcoming changes, your enterprise won’t incur high alignment costs and gain a competitive advantage.

But what is fairness in AI and data ethics? 

Fairness in AI is about evaluating every machine learning algorithm so you can remove potential biases from the data. If there is a bias in data, your company has to establish the next steps of how to act ethically and use fair principles to mitigate it. 

Data ethics is a set of rules and best practices for gathering, keeping, and analyzing internal data. If your company follows ethical values, both prospects and stakeholders with a similar approach will be more open to collaboration. 

And these days, it’s not only about the new AI Act. It’s also about data protection regulations that are on the rise and most companies worldwide fall under specific jurisdictions. This means most enterprises have to maintain their data ethically and act responsibly now. 

In the next section, you’ll learn what your business can do to support ethical AI and ML systems. 

What can you do to support ethical data processing and use?

Here is the summary of ideas that can help you support ethical data processing and bias mitigation. 

Stay compliant with applicable data privacy regulations (GDPR, CCPA, LGPD)

  • Know where your prospects and customers are based and what laws apply to your case.
  • Explore, choose and use a combination of data anonymization methods to protect customer privacy and comply with the GDPR, CCPA, and LGDP.
  • Design your digital systems with “privacy-by-design” principles.
  • Get prepared for the new AI Act

  • Do your research, gather all requirements and potential use cases and check how to stay compliant with the new regulations.
  • Check how you would mitigate AI bias and how long would it take to detect and reduce it.
  • Use AI software and systems that are accountable and transparent from day 1.
  • Introduce privacy-enhancing technologies in your company

    Although PETs don’t exempt controllers from the ambit of GDPR, CCPA and other regulations, they increase safety around data processing. 

    For example, AI-generated synthetic data mirrors the patterns, balance, and composition of the original dataset. Since privacy-preserving synthetic data does not contain personal data, it can be used more freely in projects that require good quality data in large quantities. 

    There are situations in which synthetic data can help mitigate bias, such as when there is insufficient data, when it is too expensive, or when there is no consent to use it in ML projects.

    Best practices for business leaders to fight AI bias

    In artificial intelligence, most efforts have been directed at solving "technical" biases through mathematical fixes and failed to address systematic patterns of exclusion and power inequality.

    What can you do as a business leader? Empower structural company-wide changes because you can drive them across departments to build and use AI systems responsibly. Even if there are no quick fixes for handling bias, start by considering best practices of AI bias minimization:

    1. Assist multidisciplinary teams in developing algorithms and AI systems.
    2. Promote an ethical and responsible approach to AI.
    3. Maintain a responsible dataset development process.
    4. Create policies and practices for responsible algorithm development & use them in decision making processes.
    5. Set up a corporate governance system for responsible AI and internal policies to mitigate bias.
    6. Speak out and influence industry changes and regulations for responsible AI - set an example for others.
    7. Embrace corporate social responsibility to promote responsible/ethical AI and system changes.

    And lastly, if you're the person responsible for the AI project, you can impact the effectiveness and fairness of outcomes. As a business that is dedicated to customer-centricity, implementing fair AI is essential to maintaining your company's reputation.

    Learn more about data privacy

    Get the latest content straight in your inbox!

    Get the latest content straight in your inbox!

    Articles you might like

    Enhancing machine learning model monitoring with synthetic data

    Read more

    Boosting fraud detection with synthetic data

    Read more

    How to evaluate synthetic data compliance?

    Read more