For a deeper understanding and more trust: We need AI to explain its actions

About

• Over 10 years of experience in multidisciplinary Data Science research, including Gen AI, LLM bias and transparency, deep learning, AI-driven drug discovery, and explainable AI
• Expert in big data analytics, consulting for local, defense, and global clients on human-AI teaming, human performance modeling, organizational performance prediction, AI adoption, and risk mitigation
• IEEE awards: Outstanding Young Professional, IEEE Huntsville Young Professional of the Year, IEEE Eta Kappa Nu (HKN) Outstanding Young Professional

As a leading scientist in the field of AI machine learning, how do you perceive the current challenges organizations face in adapting to the digital era and what strategies do you recommend for overcoming these challenges?

One of the challenges I perceive for a lot of companies adapting to the digital era is how to come up with the right kind of learning technologies for knowing how to utilize AI in the right context for productivity, and most importantly, how can we use AI in a more ethical, safe, reliable, and consistent manner.

Could you share some examples of successful implementations of AI principles within organizations?

A lot of businesses have leveraged AI for multi-layers hierarchies of automation, be it from ground-level manufacturing, deployment, to leadership training, product development and marketing. One strategy organizations can embrace is cultivating a continuous skill development learning environment, where employees have ample opportunities to learn, reskill, upskill, and apply their evolving skills alongside advancing technology. I think the oversight in adopting AI technology is going to be: How can we consistently evolve with technology and give employees the right kind of resources to grow with it, adjust with it, have more interactive learnings and kind of use AI as a means to bridge the gap between the newer generation of employees and the employees with decades of experience for a collaborative work environment.

As explainable AI gains traction, how do you see its role evolving in enhancing transparency and trust in AI systems, particularly in industries where decision-making processes are highly consequential?

Explainable AI serves to unmask the black box of AI technology by translating its operations into understandable terms for end users. It provides clear, actionable outcomes such as “stop” or “proceed”, making the decision-making process more comprehensible and user-friendly. That, in a way, bridges the gap between I have this technology, I know what it does, but I‘m not sure if I trust it enough to utilize it. That is exactly where explainable AI will indeed be incredibly useful. For example, Tesla provides transparent insights into how the vehicle‘s AI systems make decisions, to ensure that users understand and trust the technology‘s actions. It would look something like this: Tesla talks to the user (driver), “Hey, I see a stop sign. Anticipate slow down or anticipate sudden breaks,” then the user would be better equipped to take the right kind of action and/or accept the AI’s action, rather than AI doing its own thing without any heads up – which could cause distrust in technology.

Given the rapid progression of AI, Machine Learning, and Large Language Models, what are the key skills and competencies that you believe will be most critical for the future workforce, and how can organizations effectively cultivate these skills among their employees?

Generative AI, large language models have changed the way we use AI and again use AI in our daily lives for productivity. The important part is going to be again focusing more on training and education so that you don‘t just take advantage of AI but utilize it in the right manner. Now more than ever, there‘s a great need of the right mix of skills. One needs tech skills, AI skills, and the soft skills.

To effectively communicate what AI is saying and doing to your customers, colleagues, and team members to maximize impact, the employees require a blend of technical proficiency, AI expertise, and soft skills. Employee training and development programs play a crucial role in equipping staff with these essential abilities. Employees gain the tech skills, so that they understand what the model is doing, why it is doing, how it is doing, and can explain it to the end users and make the most impact out of it. Fostering a culture of education and learning will be foundational for success, alongside embracing transparency, explainability, responsible adoption, and ethical integration of AI. Organizations implementing policies and frameworks to support these principles will drive significant change in their roles and the broader landscape.

Which industry or which areas in business will be mostly disrupted by AI? And in which way?

Virtually every industry that touches technology, from manufacturing, marketing, healthcare, customer service, to daily life productivity tools, will be disrupted in a revolutionary way by AI.

The purpose of AI, as I see it, is in its utility as a powerful assistive decision support tool that can help us streamline any tech domain as in manufacturing via digital twins, optimization of agile flows, interactive marketing and collaborative product development with customers using Gen AI, diagnostic, forecasting and target KPI goals analysis (how to get there, and why trust the AI decisions) using transparent AI and improve data mining capabilities and make it more accessible to all using large language models (LLMs).

What does a successful approach or framework look like to cultivate the need skills and culture around AI to make the organization and business more successful?

For any organization, now more than ever, it is important to invest in people and frameworks to build skills and foster an AIfriendly work culture for a sustainable success in the era of AI.

In my extensive research using AI and machine learning for organizational performance modeling and prediction based on the Baldrige model and lean six sigma models, we have shown that ‘leadership’ is one of the crucial factors that dictates the success of any organization. Hence, having a visionary leadership on how adoption of AI can benefit the organization and promoting accessible learning environments for all employees to upgrade their skills is quintessential for success.

Inclusion of strategic planning and development frameworks to empower employees with additional tech skills that can leverage their domain expertise along with integration of agile operation models to optimize inhouse processes will be beneficial. AI can also bridge the cross-domain communication barriers and bring diverse teams together for problem solving, advance innovation and creativity and pave pathways towards sustainable AI integration. Finally, leveraging the power of Gen AI to create compelling business and customer vision models to convey what the organization can offer will be a game changer.

1. Inclusion of ethical AI design frameworks: Create equitable AI technologies for addressing social implications that arise due to adoption of AI: E.g. For recruitment softwares, we need AI that can incorporate diversity in data and learning to successfully mitigate any automated decision that implicitly discriminates against any ethnic, gender, race, and disabilities.
2. Responsible and explainable AI for transparency and accountability:
   We need legal, political and organizational policies that hold AI accountable for its decision making (both right and wrong decisions), and risk management strategies to address the scenarios when AI does indeed make incorrect conclusions. Inclusion of human-in-the-loop and human-centric design can help mitigate and promote an evolutionary learning AI environment.

3. Privacy, Security and Data regulation:
   This is a key concern plaguing everyone in the modern age of big data. How can we secure data, ensure privacy and regulate different levels of data access? What are individual organizational policies to navigate adoption of AI to ensure these concerns versus globally what the take of different nations on how data transactions are viewed, will influence this landscape. This is a great conversation to collectively evolve in the era of AI revolution.