7 Essential Practices for Ensuring Ethical and Bias-Free AI in Business Analytics

Today’s business leaders increasingly use artificial intelligence (AI) tools to analyze company trends and make critical decisions. However, AI algorithms often make biased suggestions that could be misleading. Moreover, people intending to use AI in business analytics must ensure they only do so ethically. Following best practices can keep company decision-makers, data scientists and others on the right track. Here are seven ways to ensure ethical and bias-free AI in business analytics.

1. Scrutinize the Input Data

Examining the data used to train AI algorithms is a good starting point. If the data includes biases, so will the conclusions. It is important to remember that algorithms are only as reliable as the information used to train them. The algorithms could give the wrong results if a company uses data containing duplicate records or missing information.

The input data could also contain systemic biases, such as if people put too much trust in small sample sizes or do not have time to collect information from sufficiently diverse sources. Check for systemic biases by asking the following questions:

• Are all groups accurately represented in the data?
• Are the models appropriate for the desired use case?
• Has someone checked to ensure there are no skewed results?
• Have algorithm developers defined the key metrics?
• Will the algorithms include a fairness regulator to reduce bias?

Having more than one person examine the data for errors or other problems before it is used to train algorithms is another good step. The more people there are who see the information, the more likely someone will spot a problem that may need further investigation before it is fed into an AI algorithm.

2. Commit to Responsible AI

Responsible AI (RAI) centers on designing artificial intelligence tools by adhering to intentional, moral-driven frameworks. RAI products also offer users interpretability and transparency, making them well-suited for recruitment-based tasks. Getting a job offer – or being denied one – could be life-changing for an applicant. RAI-based tools support fairness for job applicants.

Suppose a tool’s developers and users can see why it made certain decisions over others. They can check for potential bias, ensuring it did not influence a particular outcome. That capability is also important if a C-suite member wants to analyze whether now is the right time to expand into a new country or region.

Such decisions could forever change the company’s course, making it necessary to have as much trust in the AI as possible. RAI allows this with its transparency emphasis.

However, not all company leaders can immediately approve the development resources for an RAI tool. The next best thing is to only use AI for applications that would not substantially change someone’s life or cause harm if the technology reached flawed conclusions.

3. Keep Humans Involved

AI development has occurred so rapidly that the technology is now readily available to anyone who want to use it. For example, the AI built into Google Maps offers real-time suggestions to help drivers take the quickest routes. Spotify and Netflix rely on AI to make content suggestions, increasing the likelihood that users will engage with those platforms longer.

Some company leaders are so enthusiastic about AI’s capabilities that they want to remove human oversight. Besides putting jobs at risk, this approach could be particularly dangerous when using AI to handle decision-making.

In one 2023 study of business leaders in the United Kingdom and Ireland, 93% favored maintaining humans’ involvement in AI-based decision-making. Additionally, only 29% felt confident that people use AI ethically in business.

Continuing to prioritize humans’ input could perhaps increase the confidence in AI because a human could flag unethical or otherwise questionable uses of the technology. That said, 65% of the business leaders polled felt pressured to use AI in their organizations, which could mean some are using AI before they’re ready just to keep up with the Joneses. Most changes take time to implement well, and the organizations that feel rushed could inadvertently begin relying on AI tools containing biases or presenting ethical dilemmas.

AI does an excellent job of processing huge amounts of data to find meaningful trends. However, human expertise remains valuable, and people should always question AI conclusions that seem highly unusual or otherwise. It’s better to take the time to investigate than trust something that seems even slightly off just for a quick result.

4. Choose Internal AI Ethics Champions

Creating and upholding ethical AI applications will likely require several employees within an organization to oversee all AI activities and ensure they meet stated objectives. The chosen individuals can come from various departments, within or outside of technical teams.

In a 2022 study, 80% of respondents indicated the primary AI ethics person within their organization was a nontechnical executive. Additionally, three-quarters of those polled viewed ethics as a competitive differentiator.

However, many still have significant work to do to meet their ethics-related goals. Fewer than 25% reported implementing AI ethics in their workplaces, but 79% of CEOs said they were prepared to do so.

Another relevant finding was that 68% of respondents felt diverse, inclusive workplaces reduced AI bias. However, data indicates that many AI teams still need more gender diversity, underrepresented minorities and further inclusion of those who identify with the LGBTQIA+ community.

5. Select and Track Relevant Metrics

Creating bias-free ethical AI tools won’t happen overnight, but people tend to get better results when defining specific goals and identifying the most practical paths to success. Sustainability is another popular topic requiring clear definitions and strategies. Statistics suggest as many as 60% of companies have sustainability strategies. However, they’ll differ based on resources, timelines, current circumstances and other business factors.

Those overseeing AI ethics and anti-bias efforts in an organization should determine what constitutes progress in their work. How can they verify a new business analytics tool operates ethically? Has the company established a bias baseline so people can verify that AI is becoming more balanced over time?

Choosing and tracking the appropriate metrics allows you to create and stay focused on goals. Also, once it becomes clear things are going in the right direction, the statistics make it easier to justify further investments for more improvements.

6. Support Government Efforts to Regulate AI

Numerous government agencies and legislators are considering how and when they may regulate AI to minimize the harm it could cause. In April 2023, representatives from four U.S. federal agencies affirmed they would ensure people always use AI tools in ways consistent with federal laws, and they had already seen ways the technology could worsen fraud and discrimination. The parties also mentioned their legal authorities extend equally to new technology applications as any other type of conduct.

The European Union is working on the AI Act, the world’s first comprehensive law for the technology. It splits applications into three risk categories. The law would ban the riskiest applications and require tight regulations for others.

As these efforts and others continue, companies can support this process as part of their bias reduction and efforts plans. Even though some regulations are in the early stages, they’ll likely create frameworks for organizations to build AI tools that operate responsibly.

A 2022 study of more than 350 organizations across various industries also indicated that people support increased regulations. For example, a growing desire for government regulations to prevent AI bias was a sentiment shared by 81% of business leaders. About 54% said they felt deeply concerned about bias-associated risks.

Additionally, 36% said AI bias had already caused problems in their organizations. In 62% of instances, it resulted in the company losing revenue, and in 61% of cases, the issue resulted in lost customers. Bias could also detrimentally affect worker retention – 43% of those polled said it had made employees leave.

7. Establish a Training Program to Raise Employee Awareness

Artificial intelligence is a buzzworthy topic, but some need help seeing through all the impressive case studies and slick websites. AI has abundant potential, but its shortcomings are also apparent. Several years ago, Amazon tried using an AI hiring tool, but the results showed bias against women, so the company ultimately scrapped it.

It is important to balance being open to how AI could assist in business analytics while understanding it’s an imperfect technology. Company leaders could establish training programs to teach employees to recognize bias and unethical performance.

A 2023 study comprising interviews with 640 business and senior-level IT professionals found that 51% thought a lack of awareness and understanding of bias created obstacles to addressing it. However, training sessions for development teams and employees within the wider organization could enable them to meaningfully address these issues.

Making the training as interactive as possible – such as by asking participants to spot bias and unethical practices – should help them stay engaged and see how the material relates to their work. Such education can also support a culture of responsible AI use throughout an organization.

Proactive Decisions Limit Bias and Unethical Practices

Artificial intelligence is still emerging, although people are rapidly expanding its potential business analytics applications. These seven tips will help users and developers get the best results from AI-based systems and increased confidence in the outcomes, as well as enable organizations to remain ethical and combat bias, even as they quickly scale AI for more company applications. Simply put, check for biases before jumping on the AI bandwagon.