The use of artificial intelligence (AI) is becoming more and more prevalent in our lives. Between our digital assistants, chatbots, virtual assistants, automobiles and recommendation engines across industries such as medicine, finance, insurance, manufacturing, marketing and entertainment, AI is everywhere.
AI is used to inform healthcare decisions, help customers resolve customer service issues, talk with us as companion bots, make financial decisions, drive autonomous cars and help employees make more informed, faster decisions. Many brands are already using AI and will use it more often as time goes on. But do their customers trust those brands’ use of AI?
Consumer Trust in AI Is Variable
With recent headlines about AI applications that are able to create images from a text description (such as Craiyon), many consumers are rightfully concerned that AI may pose the risk of being used for nefarious purposes, such as creating deepfakes. Other concerning news came from a Google engineer who was fired for saying that Google’s AI bot was sentient.
Because artificial intelligence has been featured prominently in so many science-fiction movies, usually with negative connotations, AI has been incorporated into the human consciousness, and many consumers are hesitant to trust the use of AI in their daily lives. In a 2021 report from YouGov, 52% of respondents indicated that they’re worried about the implications of AI. Additionally, many consumers believe that AI may be a threat to their job.
A 2021 CMSWire article on unconscious biases reflected on Amazon’s failed use of AI for job application vetting. Although Amazon did not purposely use prejudiced algorithms, its data set looked at hiring trends over the past decade and suggested the hiring of similar job applicants for positions within the company. Unfortunately, the data revealed that the majority of those who were hired were white males. Amazon eventually gave up on the use of AI for its hiring practices, instead relying on human decisioning.
Also concerning is that, according to Timnit Gebru, founder and executive director of the Distributed Artificial Intelligence Research Institute, computer vision researchers have apparently shown a general disregard for ethical considerations and the potential human rights impacts of computer vision-based technologies that are used for border surveillance, autonomous drone warfare and law enforcement.
In 2018, Elon Musk, the founder of Tesla and SpaceX, stated at the SXSW conference, "Mark my words, AI is far more dangerous than nukes," claiming that there needs to be a regulatory body overseeing the development of superintelligence. Additionally, Musk stated that he was “really quite close, I am very close, to the cutting edge in AI and it scares the hell out of me.” Many consumers would tend to agree.
That said, consumer trust in AI, at least when it comes in the form of chatbots, is still fairly high. A report from Capgemini showed that 54% of customers have daily AI-based interactions with brands, and 49% of those customers found their interactions with AI to be trustworthy.
That trust isn’t limited to just customers — employees also trust their interactions with AI. A report from Oracle revealed that 64% of employees would trust an AI chatbot rather than their manager, and 50% have used an AI chatbot rather than going to their manager for advice. Additionally, 65% of employees indicated that they are optimistic, excited and grateful for their AI "co-workers," and almost 25% said they have a gratifying relationship with AI at their workplace.
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The Importance of Transparency in AI: Explainable AI
The complexity of most AI and machine learning (ML) applications prevents most people from understanding exactly what is going on. Even those who are part of the development process may be unable to understand all but the part they are working on — the AI seems to exist in a “black box” of mystery.
According to a report from FICO and Corinium, 65% of surveyed employees could not explain how AI model decisions or predictions are determined. This lack of understanding creates doubt and hinders trust. Explainable AI (XAI), on the other hand, is similar to a normal AI application except that the processes and results are easily explainable and easier to understand.
The complex nature of artificial intelligence means that AI is making decisions in real-time based on the insights it has discovered in the data it has been fed. XAI allows people to understand how organizations use AI and machine learning (ML) to make decisions, predictions and insights and enables brands to be transparent in their use of AI applications, which increases user trust and overall acceptance.
Matt Darrow, CEO and co-founder of Vivun, a buyer experience (BX) software provider, told CMSWire that XAI is extremely important for his company. "It’s well documented that even in use cases where AI algorithms outperform, say a physician, people prefer to get the diagnosis of the physician. In part, they make this choice because they want to understand the ‘why’ of the diagnosis, and secondly, they believe their case is unique."
Vivun, he added, takes this point to heart, offering transparency and explainability to create better buyer experiences.
A 2022 study from Forrester and InRule Technology revealed that 58% of respondents worry that harmful bias can lead to inaccurate decisions, decreased operational efficiency (39%) and loss of business (32%). Plus, 70% of decision-makers agreed that involving humans in decisioning with AI/ML reduces the risks associated with these technologies. Unfortunately, two-thirds of those surveyed have difficulty explaining the decisions their AI systems make, another reason why explainable AI is so important.
For building trust, it’s vital that both developers and customers understand the reasons why AI makes the decisions and predictions it does.
“Vivun customers trust our AI because we emphasize the ‘why’ of our predictions using NLG to explain the prediction in plain English,” explained Darrow. “We explain: What inputs are driving this prediction? What are the major factors, and by how much do they drive the signal? This way, not only can the user readily see what they can do about it, but perhaps more importantly, they can also disagree with it."
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Because AI applications make decisions that affect privacy, health, finances, jobs, criminal justice, safety and overall happiness, ethics and diversity should be built into the fabric of AI applications.
Darrow said that his company is very much aware of these risk factors — and takes them very seriously. “One example...is our Hero Score, an AI-generated score that helps PreSales teams provide their own view of the sales forecast." Due to the way the score is generated, added Darrow, the company can constantly focus on eliminating bias.
Josh Feast, MIT alumni, CEO and co-founder of Cogito, an AI contact center coaching system, told CMSWire that responsible and ethical AI must be a top priority for society, especially with the increased role that such technology plays in nearly all aspects of our lives.
"To properly build ethics into the fabric of AI," he said, "the onus falls on AI business leaders to deeply consider how AI influences human experiences and understand where bias can seep in. Effective and impactful AI can only happen when technology and humans work in symbiosis, and trust must exist for this relationship to be harmonious."
The Benefits of AI
The potential of AI to transform many people’s lives cannot be overstressed. In spite of concerns from Elon Musk and others, AI has the potential to positively impact the lives of billions of people over the next decade.
Darrow reiterated that although humans are good at creative and lateral tasks, as well as recognizing patterns, we still tend to score poorly when it comes to recognizing the biases that appear in our own creations.
“Having a co-pilot AI that captures not just your experience but everyone’s, synthesizes it with less bias and shares that back to the whole community, is a huge multiplier to productivity in both the short run and especially in the long run," he said. "AI is a powerful tool for humans to wield alongside our strengths — but with less of our weaknesses."
While AI and ML have their faults, according to Darrow, the problems are more consistent and obvious. Human bias, on the other hand, is trickier to detect.
Other benefits of AI include:
- Protecting Consumer Privacy: AI can protect customer data through its ability to monitor network behavior and flag anomalies 24/7. Additionally, AI can accelerate the process of data identification to improve customer data privacy.
- Eliminating Biases: Because both conscious and unconscious biases are programmed into the data that an AI application is built upon, AI applications can become biased themselves. Fortunately, AI can actively mitigate the underlying biases of the models and systems deployed.
- Eliminating Mundane Tasks: Self-service AI-based HR portals enable employees to do for themselves what used to involve HR staff. The speed with which tasks can be accomplished through automation is saving brands time and money.
- Hyper-Personalized Experiences: Due to AI's ability to process large amounts of information in real-time, brands can use AI to personalize content for each specific customer based on their purchase history, customer service tickets and browsing patterns.
Vrinda Khurjekar, senior director of AMER at Searce, a tech consulting firm, told CMSWire that consumers expect fast and personalized experiences no matter the brand or industry. “As such, the adoption of AI-driven technologies that can deliver tailored experiences at top speeds hasn’t just been accepted, it is expected."
"In fact," Khurjekar continued, "studies show that over one-third of consumers worldwide report trusting AI to improve their customer experience. At Searce, we know that most consumers are quite comfortable with the use of AI as long as the technology serves them well and they know their privacy is protected."
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Because AI is becoming such a big part of everyone's lives, it’s imperative that brands’ use of AI is both transparent and ethical and that the inherent biases incorporated into data are eliminated. In order to create and retain trust, consumers expect brands to explain how AI works and why it’s making the decisions it does.