When does an organization need to consider building a solution using AI as opposed to conventional Software Engineering?
The primary question an organization should examine while innovating in this area, is whether the customer problem they are trying to solve really requires an algorithmically and operationally complex approach like AI, instead of a relatively more wieldy technology. For starters, a solution built using AI doesn’t usually give a single concrete answer to a given question or query, instead it gives probabilities representing which set of answers are likely to be correct. Furthermore, this process is not bullet proof as it lacks immunity to producing false positives and even missing the correct answers altogether.
Sometimes the newest technological advancement isn’t the most effective or suitable solution. AI, while becoming an increasingly hot topic in tech and the media, isn’t always the best choice. Instead, innovators need to evaluate the most effective technology options without overcomplicating the development process. Sometimes, the smartest choice is based on tried and tested legacy technology.
Imagine a law enforcement agency needs a dashboard containing crime statistics in a given geographical area. We can provide these reports by utilizing conventional software, including databases and the appropriate query language, to calculate those crime analytics. This is a deterministic approach by definition and as such it would be counterproductive to use AI for the same exact purpose. However, an algorithmic approach would be called for in a more complicated case, such as when a law enforcement agency wants a tool to optimize the allocation of patrol officers over a given geographical area based on myriad set of factors like time of day, crime rate and personnel availability. In this case AI might be a potential candidate for a solution that is worthy of investigating.
To summarize: while conventional software, analytics and AI algorithms maybe overlapping, they all have different purposes, risks and associated development costs. Making it is essential to educate an organization on what AI is and what it isn’t.
Furthermore, AI is quite unsustainable in the long-term unless you can apply it in a responsible manner. Irresponsible AI which doesn’t take into account the impact on society, is easy to implement for small problems, but it’s not scalable. On the other hand, using science and engineering responsibly, although time consuming, provides a competitive advantage because people who ignore ethics will erode both trust and performance over time.
A commitment to innovation, positive impact, and community
Ethics-by-Design is an organizational approach to the responsible use of technology, and as such there needs to be deliberate steps organizations take to implement it. This development cannot be done in a vacuum; its social impact ought to be considered from the beginning. Organizations that use technology ethically consider not only the benefits but also the potential harm to society at every stage of their decision-making process, which is especially important for Axon and how we serve our public safety customers.