This blog is a continuation of the Building AI Leadership Brain Trust Blog Series which targets board directors and CEOs to accelerate their duty of care to develop stronger skills and competencies in AI in order to ensure AI programs achieve sustaining life cycles. Did you know that 80% of AI research never achieves the last mile, which is very worrisome given the trillions of investment dollars flowing into this industry?
If you have been following this series, I have identified over forty skill domains in the AI Leadership Brain Trust Framework, and you can see the full roster in my first blog. Each of the blogs in this series explores either a group of skill attributes or does a deep dive into one of the skill domain areas.
This blog drills down to explain curiosity (experimentation) which is one of the most significant Emotional and Social Intelligence skills in building an AI Leadership Brain Trust – whether its’ in your recruitment practices or in your skill development practices, creating a culture where curiosity capacity is nurtured and treasured is key.
Too often board directors are CEO’s push for results too hard, and building a culture that values trusted discovery and innovation experimentation is often not genuinely rewarded which impacts employees’ risk-taking behaviors to genuinely say: I don’t have the answers, and we need the required time to THINK more.
Do you remember the “THINK” slogan, which was first used by Thomas Watson in December 1911, when he said at a sales meeting: “The trouble with every one of us is that we don’t think enough? We don’t get paid for working with our feet – we get paid for working with our heads”. Watson then wrote THINK on the easel and still today IBM reinforces its Brand THINK on its culture and in its marketing to reinforce the power of THINKING.
Curiosity To Explore (Experimentation): No one will ever dispute that AI is a discipline that requires tremendous curiosity, as there are often multiple experiments required to test diverse data sets, using different discovery methods and algorithms to identify or correlate higher performance outcomes.
How does one define curiosity though?
There are few renowned researchers with proven approaches to curiosity identification. We will discuss three of them.
The first is Dr. Todd Kahan, a psychologist who defines curiosity as recognizing and seeking out new information and experiences, a dimension he refers to as exploration. In 2009, his research added two curiosity dimensions – the motivation to seek out knowledge and new experiences (called stretching) and second, the willingness to embrace the uncertain and unpredictable nature of everyday life, referred to as embracing.
Dr. Kahan’s research identified five dimensions of curiosity. The five dimensions are described, with my interpretation of relevant linkages to Artificial Intelligence.
Joyous Exploration: willingness to seek out new knowledge and information, the joy of learning and growing. Within an AI context, it is a field that you must be learning daily. Selecting skilled professionals in AI and data sciences requires employers to recruit talent that is demonstrating diverse levels of curiosity behavior like: actively publishing research in reputable peer journals, demonstrating leadership in peer networks, speaking at conferences, writing books, blogs, or engaging actively in social media, continually learning about new methods/ testing new algorithms and most importantly being able to communicate stories of impactful use cases – are all signs of curiosity – joyous exploration at work.
In other words, when selecting your data scientists and AI engineers, ensure that you dig in deeply using situational discovery interviewing questions, to determine if your desired candidate is a joyous explorer to help you recruit the strongest candidates.
Deprivation Sensitivity: This dimension has a distinct emotional tone, with anxiety tension being more prominent than joy-pondering abstract or complex ideas, trying to solve problems, and seeking to reduce gaps in knowledge. In terms of AI, and relevance to this type of curiosity behavior, I could easily relate to our data scientists in our AI research labs, as when we are working on custom AI models, the scientists are always striving to improve the model’s accuracy outcomes. There is usually a steady persistency to solve complex problems and reduce false positives that are often feverish in tempo at times.
A key question for board directors and CEO’s, is what are you willing to accept in terms of margin of error risks, in relationship to the type of AI application or use case being solved? Is 90% predictive accuracy good enough?, or can you live with 80% or even lower rates. Each use case may require different threshold and risk acceptance levels – so recognizing in the field of AI, you never have one size fits all is key to appreciate. What is key is to ensure you value abstraction discovery practices and can suspend judgment when you are still in grey zones, as AI takes time and patience.
The Third Type Of Curiosity Is Stress Toleration which is about the willingness to embrace the doubt, confusion, anxiety, and other forms of distress that arise from exploring new, unexpected, complex, mysterious, or obscure events. AI experts and data scientists cherish and thrive on exploring ambiguity and grey issues, as it allows them to try different combinations in their model feature engineering methods.
However what I have observed over the past ten years in the field of AI is that often business executives are far less tolerant to provide the exploration space that the data scientists need to investigate obscure contradictions in data, and refine their research modeling efforts. Hence, it is important that board directors and CEO’s that are striving to solve very complex problems using AI as a discovery or a solution approach be able to handle ambiguity more and provide for the evidence validation and iterative feedback loops to attract, develop and retain the strongest AI Data Scientists.
If Data Scientists or AI Engineers feel rushed and experience that the integrity of their research investigations is cut short or compromised, the odds are very high that your organization will experience talent attrition. Building a partnering approach is key in embracing the complex and often unexpected findings that are frequently unlocked in an AI project.
The fourth type of curiosity behavior is Social Curiosity: Wanting to know what other people are thinking and doing by observing, talking, or listening in to conversations. Ensuring your data scientists get to experience the front lines of an organization’s operating processes is critical to ensure they are also grounded in reality. Often data scientists are more introverted and they often are very comfortable in working behind their computers jamming with data sets, and working out the precise details of their investigations, before surfacing to share and communicate their results. Their work is more often than not very intense and quiet time is needed, with limited interruptions, to enable them to think clearly.
At the same time, ensuring your organization’s AI data scientist(s) experts can build communication skills and appreciate your organizations’ unique business realities is also key, so a balance is needed as companies must strive to ensure focus with valuable outcomes are achieved, but also enable social curiosity growth of their AI talent.
The fifth type of curiosity behaviour is Thrill Seeking: The willingness to take physical, social, and financial risks to acquire varied, complex, and intense experiences. This type of curiosity behaviour is more apt to be in an entrepreneurial profile pursuing AI pursuits and in most larger enterprises, publicly-traded, thrill-seeking behaviours need to be kept in check in candidate selection in high risk AI projects.
Other noteworthy experts in measuring curiosity are Tengbin Juo, Yiman Li from their Peking University Research wherein the 2019 research determined that curiosity ties different parts of the brain together into a more coherent whole, while also increasing one’s sense of self-efficacy. Self-efficacy correlates with better performance. When we engage curiosity fully, we take control of our own salience network.
Essentially their research summarized means that the more confident a performer is and their demonstrated self-efficacy -i.e.: being more confident impacts their curiosity demonstration(s) or skill competency.
Dr. Diane Hamilton is also one of the foremost leading experts in the field of curiosity. What I like about Dr. Hamilton’s research is that she has tied innovation capacity to curiosity capacity – which is so critical in the field of AI.
AI for the sake of AI has limited value.
The goal is to build an organization culture that values, appreciates and supports innovation practices, which means trust building and collaboration behaviours are respected. These strength ties enable curiosity development.
At the end of the day, every problem that is solved with AI methods, has an opportunity to move into a sustainable operating process or invent a new product or end user experience – without curiosity being strong in an organization’s cultural DNA, you will not achieve AI value realization.
Key questions for CEOs and Board Directors to correlate in striving for AI capacity is measuring their own organization’s innovation capacity and ensuring curiosity and trust building are front and center. These are interdependent enabling attributes and one without the other will only have marginal success in pursuing AI initiatives.
The next blog will further explore the remaining Emotional and Social Intelligence skills, as outlined below.
Curiosity was so important to AI Enablement(s) that this skill deserved more prominence and coverage as being so foundational in building AI capacity and resilience.
Other Emotional and Social Intelligence Skills in the AI Brain Trust Framework developed by Dr. Cindy Gordon are listed below:
- Listening to Diverse Opinions and Suspending Judgement
- Openness and Collaborative Team Orientation
- Inclusiveness, Diversity Empathy and Kindness
- Coaching and Consultative Orientation
- Flexibility to Work under Changing Conditions and Contradictions
- Adaptability & Resilience
- Immersive Learning (Learning and Re-Learning)
- Reflection and Renewal
- Health and Wellness
originally posted on forbes.com by Cindy Gordon
About Author: Dr. Cindy Gordon is a CEO, a thought leader, author, keynote speaker, board director, and advisor to companies and governments striving to modernize their business operations, with advanced AI methods. She is the CEO and Founder of SalesChoice, an AI SaaS company focused on Improving Sales Revenue Inefficiencies and Ending Revenue Uncertainty. A former Accenture, Xerox, and Citicorp executive, she bridges governance, strategy, and operations in her AI contributions. She is a board advisor of the Forbes School of Business and Technology and the AI Forum. She is passionate about modernizing innovation with disruptive technologies (SaaS/Cloud, Smart Apps, AI, IoT, Robots), with 13 books in the market, with the 14th on The AI Split: A Perfect World or a Perfect Storm to be released shortly. Follow her on Linked In or on Twitter or her Website. You can also access her at The AI Directory.
