Three Strategies for a More Ethical Approach to AI
It’s time to focus on building an Ethical Approach to AI.
As the pandemic continues to impact consumers and organizations around the world, business leaders are looking for ways to cut costs and rationalize their business operations in the wake of plummeting sales.
One solution to which many have turned at an accelerated rate: Artificial Intelligence, or AI. Many see the promise of AI as a solution that will, for example, allow them to move quickly to an all-online business model, or to adjust their supply chains to meet new consumer demands that, prior to the pandemic, were radically different.
And, in the wake of the pandemic, businesses are going a step further to hone AI algorithms that promote empathy, loyalty, and diversity.
Today’s “new normal” has its own new challenges. AI is an ideal way to address these challenges in three key areas:
Personalizing the Customer Experience
Personalization involves knowing when and how to interact with the customer. This depends on who the customer is, at what stage of the buying cycle they are, what they are buying, what they might like to buy based on their interests and needs, and their communication preferences.
However, with the ongoing pandemic increasing stress levels across all levels of society, it is important to prioritize empathy and intelligence over other interests. Companies need to have a more sophisticated approach to understanding the point at which the customer is in their purchasing journey to optimize their experience. Profiling tools and data analysis can process this information in real-time, allowing a company to quickly respond to customer needs.
For example, in the banking sector, companies can improve the customer experience by taking into account whether someone has lost their job through algorithms and AI.
The data gleaned from these algorithms can help the banking sector tailor financial products that will help an individual’s financial needs and reflect their ability to pay back a loan at a certain monthly rate and time. By offering products that fit a consumer’s needs and expectations, banks can promote an empathetic response, while forging a strong, lasting bond with their customers based on an ethical approach to AI.
Eliminating Bias with an Ethical Approach to AI
As companies look to build AI models that focus on personalization, they must ensure these models do not create bias or discrimination, and that that data acquisition and use does not occur at the expense of consumer privacy.
Removing AI bias can increase the opportunity to build customer relationships. According to a recent Genpact study, 58 percent of consumers are more likely to recommend a company that can demonstrate its AI algorithms are bias-free, and 56 percent are more likely to purchase products or services from such businesses.
Leaders should also emphasize their organization’s core values and make sure they’re translating them into diverse analytics teams. Companies should stress all types of inclusion, including different ages, genders, ethnicities, and backgrounds to ensure teams represent a broad range of experiences and perspectives.
Companies should focus on developing, testing, and monitoring models. During development, teams typically test model performance and are increasingly bringing in specially-trained internal teams or external service providers to conduct further tests to ensure the algorithms are not generating bias. The same rigor should be applied to the task of testing models against the organization’s definition and metrics for fairness.
And, just as model performance is monitored throughout the life of an AI system, model fairness needs to be monitored by risk teams to ensure that biases don’t emerge over time as the systems integrate new data into their decision making.
Gathering Evolving Data to Improve Forecasting
Just because the right data and the right features are in place still doesn’t necessarily mean an algorithm will deliver a fair outcome. Effective forecasting is critical, and updated tools that look at new data sources is critical.
External data sources can help predict product demand.For example, a beauty brand saw its supply chain disrupted when a social media influencer organically raved about one of its products. Traditional methods of forecasting would have no idea how to factor social media into to the prediction process. However, AI can look at trends and patterns in social media to better forecast demand and make supply chain adjustments more quickly.
New and improved data sources have become a pillar of next generation forecasting approaches based on ethical AI concepts. Looking at historical timeseries in a vacuum are no longer enough for companies to be able compete. As companies become more agile, businesses and customers become more connected, the need to incorporate new/alternate data sets in to transformed forecasting processes becomes imperative. AI techniques are necessary to successfully navigate all the data sources and find relevant relationships that can improve forecast performance.
The steps businesses take today can significantly impact their fortunes tomorrow, and AI plays a vital role. But finding the best path through difficult times will require a considered strategy for maximizing AI opportunities. To secure sustained success, companies must allocate resources to areas that will increase their productivity and competitive edge. By empowering companies with the knowledge to drive maximum value from AI, businesses can vastly improve customer experiences and build resilience in a pandemic world and beyond.
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