Building Trust in AI-Driven Workforce Management

Robot High Five

By Nick Bandy
Chief Marketing Officer, LiveVox

Employees often struggle to understand how AI systems function and make decisions. This lack of transparency can generate unease, suspicion and even fear. In addition, when employees perceive biased or unfair outcomes resulting from AI decision-making, their trust in the technology further erodes.

When employees can access information about data, AI training and AI algorithm logic, they can collaborate effectively with AI systems. Transparency in AI empowers employees to understand AI-driven decisions and builds confidence in working alongside this technology. A study by MIT Sloan School of Management found that when employees understand AI algorithms, they trust the technology and feel comfortable working with it.

By creating a culture of transparency and trust around AI-driven decision-making, companies can help employees feel safe and respected. In addition to building morale, this will lead to more productive and successful workplaces as well as better overall outcomes for the organization.

Take, for instance, AI-driven recruitment systems. Companies can promote fairness and reduce bias by being transparent about the selection criteria used in AI algorithms such as education, work experience and skills. This gives HR staff involved in the hiring process valuable insights into how AI systems make decisions. That leads to increased confidence in making informed hiring choices.

In other areas of the organization, AI-powered features like real-time analytics and performance dashboards allow frontline employees and supervisors to understand how their actions are being evaluated. That, in turn, make them more likely to trust the data-driven decisions from AI. This transparency fosters accountability and empowers supervisors to manage their teams effectively.

To address the lack of transparency in AI decision-making: 

  • Invest in robust explainability techniques such as visualization tools and interpretable machine learning models.
  • Provide ongoing education on AI concepts, capabilities and limitations. Ensure employees are equipped with the necessary knowledge and skills to navigate the world of AI effectively.
  • Establish an environment that values open communication and feedback, empowering employees to question AI-driven decisions and express any concerns they may have.
  • Ensure that AI remains a tool for augmentation by enabling employees to comprehend its implications effectively.

The Black Box: How Lack of Explainability Fuels Employee Distrust

LiveVox's Nick Bandy
LiveVox’s Nick Bandy

Transparency emphasizes the importance of openness and access to information. Explainability takes it a step further by providing clear and justifiable explanations for the decisions made by an AI system. By enabling employees to understand the reasons behind AI decisions, organizations create an environment where individuals feel empowered to question, refine and enhance these systems.

A report by IDC reveals that business decision-makers consider explainability a “critical requirement” in AI. When employees have insight into how AI systems make decisions, they are more likely to trust these technologies as valuable tools that augment their expertise instead of viewing them as intrusive “black boxes.”

In addition, explainability helps identify potential biases, errors or ethical concerns that may arise, allowing organizations to address these issues and ensure that algorithms are accountable and aligned with their values. This approach instills confidence in employees, reinforcing the notion that AI systems are designed with their best interests in mind.

To understand how it works in practice, imagine a scenario where a company uses an AI-powered system to screen job applications. If an applicant is rejected based on the system’s decision, but the reasoning behind that decision remains unclear, HR personnel may question the fairness and reliability of the AI system. However, if the AI system provides clear explanations for its decisions, employees can understand the factors considered, reducing skepticism and fostering trust.

Or consider a company using sentiment analysis to analyze customer conversations. Sentiment analysis algorithms provide transparency by showing which aspects of customer interactions were most influential in shaping the sentiment. These algorithms can identify the keywords, phrases or topics that drive positive or negative sentiments, helping service agents understand the underlying factors affecting customer satisfaction.

By incorporating sentiment analysis into their workflow, agents can better understand customer needs, preferences and pain points. This enables them to deliver personalized and tailored experiences, ultimately building trust between employees and AI systems.

To address AI explainability: 

  • Communicate the purpose of using AI-driven decision-making, how it works and what safeguards are in place to ensure accuracy.
  • Work to balance explainability and confidentiality in AI algorithms, prioritizing the need to safeguard proprietary information and sensitive data.
  • Foster open dialogues and collaborations among stakeholders, including HR executives, business leaders, data scientists and legal experts. This is essential to develop frameworks that balance competing interests.

Human Oversight as a Safeguard for Ethical Decision-making

One of the most critical aspects of implementing AI in workforce management is maintaining a balance between automated decision-making and human oversight. While AI-driven decision-making can be more efficient and accurate in some cases, employees often feel more secure when decisions are made by humans who can be held accountable for their actions. 

Human oversight’s key role is ensuring fairness and ethical decision-making in AI systems. A study from MIT Sloan Review points out that, while AI can process vast amounts of data quickly, human involvement remains essential to prevent biases and ensure the alignment of AI systems with organizational values. 

How are organizations integrating human oversight in AI-driven workforce management?

  • In hiring processes, AI-powered algorithms may inadvertently amplify gender or racial biases in historical data. However, by incorporating human oversight, organizations can review and validate the algorithm’s recommendations, promoting fairness and diversity in hiring practices.
  • Employee self-service portals and HR contact centers often use AI to guide employees through getting answers to common questions. As an example, if an employee wishes to update their personal details or get information on benefits, AI algorithms can guide the user based on historical data. However, there can be times when an AI may misunderstand the user. Here, human oversight is necessary to review flagged or ambiguous interactions.
  • In HR contact centers, AI-powered speech analytics can monitor interactions to ensure they align with company policy and regulations. Instead of manually monitoring calls, which will capture only about 2% of total interactions, AI allows for nearly complete oversight. If an answer is potentially non-compliant, the interaction can be flagged in real-time and an HR supervisor can take over the interaction.

Incorporating human oversight into AI-based decision-making processes helps build employee trust in the systems. Employees can have confidence in the technology knowing that there is transparency and accountability in how AI decisions are made. This fosters a collaborative environment where employees work alongside AI to deliver value to the business.

To incorporate human oversight into AI-based decision-making processes: 

  • Establish a committee or advisory board that reviews decisions made by AI systems and ensures that employees feel heard and protected.
  • Involve employees in developing AI systems to leverage their expertise and improve hiring, management and retention strategies. Employee inclusion fosters ownership and increases trust in AI technology.
  • Also, include employees in the monitoring of AI systems. Their firsthand knowledge of the organization’s operations, culture and values can help shape AI systems that align with its goals and ensure that ethical considerations are integrated into decision-making processes.

The Journey Toward Trust and Collaboration

AI technology offers a wide range of applications that can greatly improve workforce management processes. With their advanced capabilities, these systems have the potential to optimize efficiency, streamline operations and enhance overall productivity in various industries. However, lack of employee trust and buy-in often leads to resistance in adopting these technologies. 

To overcome resistance, companies should prioritize transparency and explainability in AI-driven workforce management solutions. It is possible to foster collaboration among employees by providing clear explanations of how the system works and building trust by involving humans in AI decision-making is essential.

As AI and humans coexist in the workplace, building trust requires a balance of transparency, explainability and human oversight. HR executives and business leaders must prioritize trust for successful AI implementation. It’s not just nice-to-have, but essential for AI-driven workforce management that enhances productivity, employee satisfaction and retention.

Nick Bandy is the chief marketing officer at LiveVox. He has over 25 years of executive leadership experience in the marketing and technology space, serving private, PE-backed and public organizations. He founded and developed the SpeechIQ product, which was acquired by LiveVox in 2019.

Image: iStock

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