As we continue the end-of-the year review on all things tech, digital ethics and the progress of artificial intelligence (AI) in people-related technologies springs to mind. People tech affects HR, recruitment and other areas that enable businesses to hire, manage and plan their key asset – people. With new suppliers coming out consistently, it is very difficult for businesses to understand which technology is ethical with regard to data, code and algorithms, versus technology that is not.
The first thing to highlight is that AI is a huge buzzword for people tech these days. However, it is abused more often than it should be, resulting in confusion for businesses that simply may not have the time to keep on top of tech or research it before buying, typically costing them huge resources. To clarify, AI has several strands, two of which are machine learning and automation. These two are significantly highest in use at the moment in people tech, whereas other forms of AI are more relevant in other sectors. As an example, autonomous cars use robotics and other relevant strands of AI.
Now, regardless of the use of AI and its specific strand, especially when it concerns algorithm-building stages, it is extremely important for every developer and tech business to not only think about “ethics” and “biases,” but to actually implement practices that would help them not only tackle their own challenges with regards to ethics and biases, but also those of their employees and users. This truly allows them to build and code for purpose-driven, value-add commercial products. Increasingly, a lot of experts are talking about this issue, from TechUK committees that I participate in, to IEEE guidelines I am part of globally. There are a lot of experts, individuals and organizations constantly talking about this important topic.
However, very little has been seen in terms of action, and so, for my part, I am “practicing what I preach.” While we are a startup, and it does add a couple hours to my time reviewing the code for new features, it is very satisfying to know that this work comes from a place of supporting users. In addition, we prioritize careful data use and management; we will strictly only use the data that helps our users with analytics (based on what our platform offers) and provides a better experience.
How can larger tech companies and software houses implement this? I believe that the larger the business, the easier it should be to have processes and resources that effectively address the desired outputs of the business vision and support customers, while also to serve as an in-house ethics and bias reviewer. This gives businesses a lot of power internally to follow guidelines drawn by governments and other organizations working actively to support this framework-building.
There is no doubt that 2019 will be a key year for growth in digitization, automation, augmented analytics and blockchain. So, I really hope that businesses stop talking about the fundamental challenges of digital and AI ethics, and start building tools and frameworks to monitor them.
About the author: Bhumika Zhaveri is a non-conventional and solutions-driven technology entrepreneur and businesswoman. As an experienced HR Technologist, she has expertise in HR and Recruitment: Technology & Programme Management for Change & Transformation. Privileged to look at challenges differently than most due to versatile life, personal and professional experiences, she is actively involved with TechUK, IEEE for data ethics, AI & digital committees and TechSheCan charter with PWC, Girls Who Code and similar organizations supporting women in stem . Currently, she is also the Tech Advisor for Resume Foundation and Bridge of Hope, while also being a founding member of Digital Anthropology.