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Explainable AI And The Future Of Machine Learning
Kamelia Aryafar, Chief Algorithms Officer, Evp, Overstock.Com
Artificial intelligence (AI) is ushering a new era of technological innovation, paving the way for increased adoption across several industries ranging from healthcare to e-commerce. The novel capabilities offered by AI are empowering businesses to automate the different operations, making them faster and smarter, and enhancing the overall productivity. One of the most significant benefits facilitated by AI is the management of massive datasets, which enables organizations to handle the flow of information efficiently. Overall, AI is making significant headway in the business world, generating robust benefits for integrators and adopters.
Today, firms across the world are incorporating AI-based systems to automate their business processes and enable their workforce to focus on more valuable tasks. However, the incorporation of AI technology is impeded by several challenges, and the biggest one is the lack of transparency. AI systems are often considered as a black box, which makes it challenging to pinpoint logical errors in the underlying algorithms. These challenges in data privacy, protection, and cybersecurity have introduced nuances into the field, making it imperative to develop explainable AI, which offers greater visibility and transparency
Balancing Stability with Innovation
To make the most of AI, enterprises should also focus on incorporating the right tools, talent, and culture. While a myriad of different AI solutions has permeated the marketplace today, businesses still lack the expertise to identify which solution aligns with their organizational goals. This is where having the right partner to help you through every stage of the integration process makes all the difference. These partnerships should be compatible with the values, goals, and strategies of an organization. It is advisable to consider the risk factors and conduct an impact analysis on how the partnership will help drive business growth. For instance, I often collaborate with numerous partners, many of which possess robust and proven solutions. We also work with nascent companies that have a more novel approach to balance the risk versus the impact of the AI integrations.
When choosing partners, businesses should have a firm knowledge of the different areas of improvement within their organization. Successful collaboration relies on minimizing the risks and maximizing the benefits. By narrowing the AI integration to specific processes that require immediate upgrading, the overall workflow can be streamlined seamlessly. Once the relevant areas have been identified, the tech teams can decide on collaborations across the organizational line. Often, enterprises fail to see beyond the hype of AI products and rush into piloting several things without a clear vision of the end result. In such cases, businesses cannot derive the expected value from the solutions. Hence, it is crucial to have a specific goal in mind when integrating AI technology.
Augmenting AI with Robust Leadership
Along with having an AI-first mindset, businesses should be swift and versatile when executing on the AI technology. As a practitioner in this area for several years, I have witnessed a steady rise of interest in data science. One does not need a doctorate to be a data scientist. Perfection comes with practice. My advice to budding professionals is to follow their passion as they have a myriad of resources at their disposal, including journals, and coding courses. As for moving up the leadership ladder, practitioners must take bold decisions and get involved in the community. It also pays to take part in cooperative projects and contribute to the academic community by publishing papers, attending conferences, giving talks, and supporting the cause. Hence, as a leader, it is vital to adapt and change according to the trends, while also focusing on the key areas that need improvement.