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[REPORT PREVIEW] The Customer Experience of AI

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[REPORT PREVIEW] The Customer Experience of AI

  2. 2. Executive Summary In the next five years, machine intelligence will become ubiquitous, and technology innovations, such as Internet of Things (IoT), chatbots, and augmented reality, will proliferate. We will interact more and more with devices via talk and text, on a range of devices and locations, often determined and delivered by machine learning algorithms. As a result, Artificial Intelligence (AI) will shape our experiences with companies, products, and services in unprecedented ways. This report explores the impact of AI on the customer experience, lays out a set of operating principles, and includes insight from technology users, developers, academics, designers, and other experts on how to design customer-centric experiences in the age of AI. More than anything, business leaders today should begin to treat AI as fundamental to the customer experience. This means thinking about the values it perpetuates as an essential and eventually indistinguishable expression of product, services and brand. 12
  3. 3. AI Transforms the Customer Experience However we define it, whether we know it or not, most of us interact with AI daily. The cluster of technologies that we think of as AI is fundamental to recommendation engines, search engines, word processing programs, messaging, personal digital assistants, social networks, and everyday household items. It’s used in everything from Snapchat’s puppy and flower crown filters to driverless cars, children’s toys, and predictive analytics. Now, after decades of stop-and-start growth, AI finally has the momentum to change how businesses and customers interact.1 IDC expects global spending on cognitive and AI solutions to achieve a Compound Annual Growth Rate (CAGR) of 54.4% through 2020, when revenues will exceed $46 billion.2 And many consumers generally believe that AI is a positive phenomenon. A recent PwC research study found that 63% of American consumers believe that “AI will help solve complex problems that plague modern societies.” 3 Every day, we see extraordinary advances in healthcare, natural language processing, self- driving cars, image recognition, and other technologies based on AI. But unlike rules-based systems, machines that can learn—from images, movements, locations, interactions, sounds, relationships, temperature, past errors and a host of other signals—create a new set of considerations for customer experiences. While people expect to interact with organizations in multiple ways—on the web, in person, via email, on social media, or through other venues—experiences can be quite different when they are informed or even delivered by systems using machine learning algorithms. In addition, AI arrives on the scene with a set of preconceptions shaped by popular culture. As early as the 1940s, authors such as Isaac Asimov, Arthur C. Clarke, and William Gibson explored the implications of intelligent machines and their impact on society.4 More recently, luminaries such as Elon Musk and Bill Gates have warned of “The Singularity,” a period by which they believe machine intelligence will surpass human intelligence. This report, however, focuses on more immediate business issues: the impact of AI on customer experiences and what we can learn from technologists, academics, startup entrepreneurs, think tanks, and business leaders who are building the foundation for a customer-centric experience of AI. The first step is to look at the norms, processes, and expectations AI disrupts. 3 “We can only see a short distance ahead, but we can see plenty there that needs to be done.” ALAN TURING, AUTHOR OF COMPUTING MACHINERY AND INTELLIGENCE
  4. 4. How AI Changes Expectations While the fundamental principles of customer experience haven’t changed, the environment in which businesses are operating certainly has. AI upends many of the norms that govern normal business interactions. Chatbots create new interaction models between people and organizations, while the presence of AI-enabled applications or services expands the possibilities for engagement and provides important challenges to consider. Figure 1 lays out some of the changes that affect customer experience. 4
  5. 5. CUSTOMER EXPERIENCE CHALLANGES OF AI FIGURE 1 NEW INTERACTION MODEL CHANGE DESCRIPTION IMPACT Machine learning algorithms introduce a new layer between people and organizations that is sometimes obvious and sometimes hidden. Traditional interaction models (apps, websites) are instrumented and explicit, while a chatbot or Virtual Personal Assistant (VPA) is open-ended. For example, AI is embedded into recommendation engines and ad targeting but embodied in VPAs and driverless cars. Lack of clarity and confidence in how to interact. In the case of chatbots or driverless cars, it may be unclear: • What it knows and what it doesn’t • Whether one is interacting with AI or a rules-based system • Whether one is interacting with AI or a person • What it can and can’t do • How and when to override or escalate INFORMATION ASYMMETRY & AMPLIFICATION OF BIAS AI introduces information asymmetry between people and organizations. We may not know for certain whether AI is present (for example, in a recommendation engine), what its agenda may be, or exactly what caused it to reach its conclusion or behave in a particular way. Machine learning algorithms are also prone to bias. Common datasets, such as Word2Vec and ImageNet, have been shown to include intrinsic gender and racial biases that require remediation. Potential for unfairness and disenfranchisement. This is fairly benign in some use cases (Netflix recommendations) but powerful in others, such as an algorithm that determines the cost of an insurance premium, who gets hired, who is eligible for parole, or who has access to a particular medical treatment. THE BLACK BOX PROBLEM It is possible to observe an algorithm’s inputs and outputs but difficult or impossible to diagnose and remediate errors in data or judgment. Lack of trust in results and risk. In fairness, human decision-making can also be opaque, but organizations that use machine learning algorithms need to be able to understand and explain the justification for their conclusions and decisions (“Explainability”).5 UNCLEAR REDRESS Few if any ways for people to opt out of algorithmic decision-making. Unclear escalation processes. Lack of confidence in or frustration with AI-enabled systems and services. 5 All of these factors: New interaction models, information asymmetry, amplification of bias, the “black box problem,” and unclear accountability, combined with the sheer novelty and momentum of AI, strain the norms of customer interactions in unprecedented ways. The question is: What can businesses learn from customers to focus efforts not on fighting crises, but on unlocking AI’s potential to enable innovation?
  6. 6. LEARNING FROM MISTAKES While it is still early for a set of best practices, there are plenty of “worst practices” and unintended consequences from which to learn. One of the most widely circulated stories is a study by researchers from Boston University and Microsoft arguing that “the blind application of machine learning runs the risk of amplifying biases present in data” and that Word2Vec, a data set commonly used to train machine learning algorithms, such as search engines, recommendation engines, and machine translation, was in their words “blatantly sexist.” Figure 2 illustrates an example of the “word embeddings” (or associations) that the researchers discovered between occupation and gender.6 EXAMPLES OF MOST EXTREME OCCUPATIONS BY GENDER FIGURE 2 EXTREME HE OCCUPATIONS 1. HOMEMAKER 2. NURSE 3. RECEPTIONIST 4. LIBRARIAN 5. SOCIALITE 6. HAIRDRESSER EXTREME SHE OCCUPATIONS 7. NANNY 8. BOOKKEEPER 9. STYLIST 10. HOUSEKEEPER 11. INTERIOR DESIGNER 12. GUIDANCE COUNSELOR 1. MAESTRO 2. SKIPPER 3. PROTEGE 4. PHILOSOPHER 5. CAPTAIN 6. ARCHITECT 7. FINANCIER 8. WARRIOR 9. BROADCASTER 10. MAGICIAN 11. FIGHTER PILOT 12. BOSS 6 But algorithmic bias is not confined to text. Machine learning algorithms in image and facial recognition can amplify bias as well. In 2015, Flickr faced widespread criticism after it was discovered that its auto-tagging feature mislabeled people and places in offensive ways. Subsequent stories revealed gender, race, and other types of human bias in machine learning algorithms taught by images.7 Source: Bolukbasi, Tolga; Chang, Kai-Wei; Zou, James; Saligrama, Venkatesh; and Adam Kalai. “Man Is to Computer Programmer as Woman Is to Homemaker? Debiasing Word Embeddings.”
  7. 7. The types of issues these stories raise are directly relevant to any organization that is using or planning to use machine learning in predictive analytics, ad targeting, recommendation engines, search, audience segmentation, customer engagement, product/service development, or a host of other enterprise use cases (see Figure 3).8 AI CRISES IN THE HEADLINES FIGURE 3 JUNE 1, 2016 Google voice searches and keeps records of conversations JANUARY 13, 2017 “Koko,” a user on Reddit depression thread, fails to disclose it is a bot AUGUST 16, 2017 AI programs learning to exclude some African-American voices JUNE 21, 2017 Smart doll fitted with AI chip can read your child’s emotions VIRTUAL PERSONAL ASSISTANTS AND CHATBOTS SELF-DRIVING CARS JUNE 30, 2017 Self-driving cars confused by kangaroos SEPTEMBER 8, 2017 Computers can tell if you’re gay from photos AUGUST 21, 2017 Machines taught by photos learn a sexist view of women AUGUST 22, 2017 Transgender YouTubers had their videos grabbed to train facial recognition software AUDIENCE SEGMENTATION AD TARGETING SEPTEMBER 21, 2017 Instagram uses “I Will Rape You” post as latest ad SEPTEMBER 15, 2017 Google allowed advertisers to target people searching racist phrases RECOMMENDATION ENGINES AUGUST 25, 2017 New app scans your face and tells companies whether you’re worth hiring SEPTEMBER 9, 2017 Facebook pulls 9/11 trending topic after it promotes a hoax SEPTEMBER 18, 2017 Amazon suggests users purchase dangerous item combinations 7
  8. 8. Granted, these stories are shocking, and it can be tempting to dismiss them as edge cases, but they hold three critical lessons: 1. Algorithms are not pristine mathematical formulas for truth.9 2. Intentions are irrelevant; AI-enabled experiences will become a mirror for brand reputation and corporate values. 3. These are cautionary tales best viewed as an early warning system for many more crises to come. The most powerful leaders in AI technology already know this. On October 7, 2017, Alex Stamos, chief security officer of Facebook, tweeted the following in response to claims of alleged Russian interference in the 2016 US election:   “Nobody of substance at the big companies thinks of algorithms as neutral. Nobody is not aware of the risks.”10 In the meantime, companies such as Google, Microsoft, Facebook, and the academic and non-profit communities are conducting research into AI ethics and are publishing comprehensive studies, such as the AI Now Institute 2017 Report, which looks at issues such as labor and automation, inclusion and bias, and rights and liberties.11 While efforts to address algorithmic bias will solve some problems for some audiences, it will fall on business leaders to use AI in a way that augments rather than detracts from customer experience. The first step is to establish healthy and sustainable norms for AI, both now and in the future. 8
  9. 9. This preview version of “The Customer Experience of AI” contains only the first seven pages of the report. To download the entire report, free of charge, please visit the link below: http://bit.ly/altimeter-cx-of-ai