Ethics and AI: 5 Issues Facing B2B Marketers

Ethics and AI: 5 Issues Facing B2B Marketers

As brands explore uses of AI and machine learning in business, they are also coming to terms with ethics and AI. Join the B2B community at the True Influence Summit to hear John C. Havens, Executive Director, IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems. He joins a panel at the True Influence Summit to talk about issues related to AI and B2B.

We’ve come to recognize and appreciate the numerous benefits of artificial intelligence (AI) in business, some quite amazing. B2B brands have used AI capabilities for growth in sales and revenue, improvements to buyer and seller relationships, data-based B2B marketing, and boosts in productivity and efficiency.

Despite these transformative perks, we’ve also encountered AI’s ethical issues, which we can’t overlook. In this article, we look at the five ethical issues of AI in business. Missing the mark can expose your business to reputational, regulatory, and legal risks.

Five Ethical Issues of AI in B2B Business 

According to a global research study, 88% of respondents don’t have confidence in AI-based decisions. They believe the more you depend on AI for decisions, the more you must pay attention to risks in areas like reputation, regulation, human resources and privacy. That’s certainly true. When not well-thought out, problems happen. Be alert and give your attention to these five areas when developing AI practices and policies. 

#1 Privacy

According to the Forbes report, 83% of respondents say AI is their strategic priority today, and to make scalable AI solutions, companies use massive troves of data. As this information is often personal, behavioral and highly sensitive, such as health and biometric information, its use to train artificial intelligence might be in violation of data privacy and AI ethics.

This concern has long been associated with AI applications. For example, in 2017, Sidewalk Labs, a Google subsidiary, faced a massive backlash by citizens and government officials. Due to the lack of clear ethical standards for data handling, the company scrapped the project to build an IoT-fueled “smart city” within Toronto with a loss of two years of effort and USD 50 million.

2# Transparency

AI leverages machine learning and neural networks to analyze massive amounts of data and deliver results. But AI does not tell you what data it uses, who’s responsible for its training, and how AI jumps into the final decisions. This could make AI opaque and questionable.

According to a recent study, 76% of respondents say they are concerned with the AI’s transparency issue. Hence, to make AI transparent and trustable, they want it to explain three points:

  • How does AI collect data, and for what purpose?
  • In what way AI-algorithms/applications work based on the data collected?
  • Its ability to trace and explain the behavior to make a decision.

3# Bias and discrimination 

Biases in AI refers to an abnormality in the output of machine learning algorithms. It occurs when developers use prejudiced assumptions during the algorithm development process.

For instance, Amazon engineers spent years working on AI and invented a recruiting tool. But after one year of its employment, the company scrapped the program because they couldn’t understand the process to create a model that doesn’t systematically discriminate against women.

Bias is primarily concerned with ensuring that AI machines don’t harm your business and customers with unfair treatment. The report “The Ethics Of AI: How To Avoid Harmful Bias And Discrimination” explains how AI systems can inherit bias and guides you on preventing bias from an organizational and technical perspective.

#4 Accountability

Accountability is all about the acknowledgment and assumption of responsibility and explanation for AI-based actions, decisions, products, and policies.

An AI system is often a combination of a complex supply chain that may include data providers, data labelers, technology providers, and system integrators. When AI systems deliver unwanted results, who is to blame and who is accountable are AI ethical dilemmas.

#5 Security

In today’s tech world, a robust cybersecurity plan is non-negotiable. It protects your company’s and customers’ data and engender trust. AI is a valuable tool for that, with the ability to detect fraudulent activity, malware and scams.

But ironically, the rapid advancement in AI is also its major drawback. It gives rise to cybersecurity risks and adversarial attacks. As AI uses piles of data to train and update itself, it offers opportunities to hackers to capture those data and either create their own programs or manipulate existing systems for malicious purposes.

To Govern in The Future, You Must Act Today

What’s the key takeaway from this? Every organization needs to address these ethical issues of AI. Ethics guidelines provide prescriptive, specific and technical strategies to develop AI systems that are secure, transparent, explainable and accountable.

Want to hear more on AI and ethics? Register for the True Influence April Summit today and hear live from panelist John C Havens. He’s Executive Director of the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems (IEEE AIS Ethics Initiative.) 

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