In this special guest feature, Oded Karev, General Manager, NICE RPA, discusses why a human-first approach to AI ethics is key to driving value from automation. Murli is responsible for strategy, operations, and solutions that deliver multi-cloud data services for Kubernetes. In his role at NICE, he leads global Advanced Process Automation line of business, covering the full spectrum of robotics solutions. Prior to his current role, he served as Director of Corporate Strategy at NICE, leading some of the company’s key growth initiatives. Before joining NICE, Oded specialized in delivering multi-channel strategies, operating model designs and digital transformation projects for Accenture.
Many of the modern conveniences that go unnoticed everyday are created by artificial intelligence (AI) and intelligent automation. Already pervasive in our day-to-day lives, these algorithms work behind the scenes in many of our daily interactions with enterprises and apps—scoring credit applications, serving up personalized advertising, recommending things to watch, listen to or buy, and much more.
The value this creates for organizations, their employees and their customers is immense. Intelligent automation speeds up processes, enhances accuracy and efficiency, relieves humans from tedious, repetitive tasks and enables more convenient and personalized experiences for employees and customers. Yet as automated systems play an ever-expanding role in our lives, it raises some thorny ethical questions.
Some of these pressing concerns include:
- People’s data being used to manipulate their behavior
- Bias in algorithms and data leading to decision-making that discriminates against people based on age, race, or gender
- A lack of transparency about how data is used to make decisions that affect people’s lives
- The mass displacement of people from their jobs.
This isn’t an academic or philosophical debate, but one that can have major implications for organizations and individuals. With the increasing unease about companies using personal data and questions around how algorithms make decisions, enterprises that get it wrong may suffer reputational harm or even litigation.
Biased datasets and the customer experience
Consider a scenario, for instance, of a biased dataset leading an automated system to ‘learn’ to disqualify job candidates based on their gender. Another instance might be an opaque, AI-driven insurance claims process that leaves a customer unsure about why a claim was rejected and angry at a lack of an empathetic explanation.
As organizations accelerate their deployment of technologies such as robotic process automation, attended automation and AI, they will increasingly confront such issues. Those that think them through carefully and take robust measures to address them will be able to win the confidence of their employees and customers and drive higher levels of benefit from automation.
We see these dynamics playing out very clearly in the robotic process automation (RPA) and attended automation space, especially as AI works ever more closely with robots on back-end servers and on users’ desktops. The more meaningful data we have to drive robots and AI, the more powerful the automations and experiences we can create.
Picture an agent in a contact center, for instance. Attended automation via a desktop robot can help them to rapidly access data from different applications and sources so they can offer personalized support to the customer in the moment. Such a robot can also support the agent with recommended next steps and compliance guidelines in real time.
Human trust holds the key
Although the power and efficiency of such a solution are clear, human trust is key to getting the most from it. If the agent fears that the robot will eventually make them redundant, they will resist using it. The customer, meanwhile, might become concerned or annoyed the conversation with the agent reveals that their data was collected or used in an unexpected way.
Organizations that are investing in RPA and AI should seek to get ahead of these concerns by putting in place a sound ethical framework and governance structures that guide how they use data and put automation to work.
A good starting point is establishing an ethics board, composed of people from a cross-section of functional areas, such as IT, legal, product management, and customer service, to provide oversight of data, AI and automation ethics. This board could also incorporate external members, such as customers and professional ethicists.
Always augment human potential
Automated systems should be designed with the goal of augmenting human potential. To achieve this goal here are five guiding principles for ethical deployment of robots and AI in the workplace:
- Robots must be designed for a positive impact: They must be built to contribute to the growth and well-being of the human workforce.
- Bias should be eliminated. Robots must disregard group identities such as color, religion, sex, gender, age and other protected status.
- Robots must safeguard individuals. Careful consideration should be given to whether and how decisions are delegated to robots. The algorithms, processes, and decisions embedded within robots must be transparent and explainable.
- Robots must be designed to act based upon verified data from trusted sources.
Robots should be managed with holistic governance and control. Humans should be informed of a system’s capabilities and limitations.
AI has the potential to have a significant impact for good in our world, offering numerous benefits including speed, consistency, and reduced labor costs. However, the way in which it will transform our world certainly deserves careful thought and attention.
The ethical issues presented by AI and automation can be complex and challenging, but they are not going to go away. Organizations that are proactive will be able to navigate the minefield successfully. This will clear the way for them to use AI and automation in powerful ways that enrich the customer experience, benefit the employee and drive higher levels of efficiency and productivity for the business.
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