Building Trust in AI Through Authentic Leadership

Artificial Intelligence (AI) is a topic on our minds these days. Some regard it with excitement, some with fear, and others, both. According to Pew Research Center, roughly two-thirds of U.S. adults feel ambivalence or concern about AI's growing presence. Yet, until recently, we have ignored or taken it for granted. Over time, we may become accustomed to relying on AI—it gives us directions, recommends what to watch on TV, and so on. With the acceleration of generative AI and tools providing the ability to create written and visual content, alarms bells are sounding due to heighted concerns about privacy, security, bias and mis-information. And while recent developments are a substantial technical advance, AI is still in its infancy. To put it in perspective, the AI of today is the simplest of three categories determined by their type and scope:

1. Narrow AI- designed for specific tasks (aka Siri, Alexa, Google Assistant. All the AI we use today falls into this category).

2. General AI- these will be fully autonomous machines capable of reasoning and learning (we aren't there yet, the droids in Star Wars and HAL in 2001 are examples of General AI).

3. Superintelligent AI- even more futuristic than General AI, these are self-aware machines possessing cognitive abilities surpassing our own. (Think Blade Runner's replicants, The Terminator, and Data in Star Trek).

The most popular uses of AI include content writing, travel planning, and summary generation. However, we will soon see more sophisticated AI assistants, more refined content creation, and more automation in sectors where labor is scarce. For better or worse, AI is becoming more and more integrated into our daily lives and with that comes the inevitable: more significant risks of its misuse. The need for ethical AI governance is clear. Governance and accountability frameworks will help address the safeguarding of human rights, legal and privacy issues, a methodology for responsible AI development, the maintenance of policies mandating transparency, the determination of responsibility for AI liabilities, and the establishment of clear guidelines for international global governance. In a nutshell, the challenges of balancing AI advancement with human values require compassionate, visionary leaders who prioritize ethics, transparency, and inclusivity.

Authentic Leadership: A Key Driver in the AI Era

When you think leadership skills, does authenticity make your list? If your answer is no, consider putting it right at the top. Today's leaders face complex, interconnected, rapidly developing issues, and the core skills commonly associated with effective leadership aren't always enough. Authentic leaders demonstrate agility, curiosity, humility, and a greater level of honesty. Cultivating authentic leadership may involve discomfort as you ask yourself questions like, What are the contributions I most want to make in the world? How can I infuse my role as a leader with my sense of purpose as a human being? This is a process of ongoing self-reflection. It might involve shedding some of your work personae and revealing more of who you are outside of your professional life. The resulting behavior will be less about wearing a veneer of self-confidence and more in alignment with your deepest-held values. For some, this will bring instant relief; for others, unease. Consciously narrowing the gap between who you are at work and who you are at home requires a vulnerability that we rarely associate with professional leadership.

Regardless of your unique version of authentic leadership, in general, authentic leaders

1. Are service-minded—aligning business goals with what is best for the larger world.

2. Embrace humility and vulnerability, admitting what they don't know, prioritizing transparency even when it feels uncomfortable. No one has all the answers. Not knowing is authentic.

3. Hone their listening skills, take a genuine interest in others and ensure their employees feel heard.

4. Approach problems from a place of curiosity and creativity. They let go of the limited and fixed thinking patterns historically associated with problem solving. Many of today's issues are developing and emergent, demanding solutions that are equally so.

5. Accept that authenticity requires ongoing self-reflection and self-awareness. They understand that identifying their core self and purpose increases confidence, risk tolerance, and resilience.

(Studies suggest that this is even more beneficial for women in leadership. Eliminating dissonance between your values and behavior is energizing—leading to enhanced leadership potential).

On the whole, authentic leaders personify ethics, accountability, transparency, and adaptability—shaping organizational culture from the top down. They embody ethical principles to set the tone for their employees. These leaders hold themselves accountable and take responsibility for their actions. They foster open, transparent communication, encouraging employees to voice their concerns. Authentic leaders welcome feedback and embody a commitment to continuous improvement and adaptability.

Authentic Leadership in Action: A Checklist for Ethical AI Governance

Complex and rapidly changing, AI demands robust and continuously adaptive oversight. Authentic leadership will play a vital role in its successful governance—establishing healthy organizational ecosystems to meet both predicted and unforeseen challenges. Here are some suggestions for incorporating authentic leadership to drive AI governance.

-Demonstrate compliance with laws, standards, and regulations for AI data processing.

-Outline clear guidelines and policies for how your organization will adhere to ethical principles (i.e., legal requirements, regulations, and best practices).

-Oversee AI's use and regularly review potential areas of concern; conduct regular audits of AI data to prevent bias and discriminatory outcomes and to maintain quality standards.

-Maintain a policy of transparency via clear explanations of the decision-making process.

-Protect users. Be transparent about data collection and its intended use, protect users' privacy with de-identification.

-Conduct internal monitoring and receive regular external feedback from stakeholders and users.

-Implement ongoing training to keep up with technological advances.