Engineers love clear problems with delineated right and wrong answers. Data, especially quantified data they think, is objective and clean. Without painting too strong of a stereotype, they don’t like to muck around with soft skills, or social/political factors in problems. They like to keep engineering pure.
The problem with this view is that it’s not correct – it makes an underlying assumption about what makes something a right or wrong answer to a problem. Most problems that engineers deal with when designing a solution are not value neutral. When we think of problems with clear right or wrong answers, we think of problems that are purely mathematical or having discrete binary solutions (e.g. “will the object handle the forces that it will be subjected to under normal conditions?”). The secret is that all problems have “right” and “wrong” solutions based on the underlying values you are trying to optimize for.
An engineering problem that is optimizing for maximizing return on investment might have different solutions than one that optimizes for addressing systemic inequity for particular people. The tradeoffs are not just opportunity costs, but instead are tradeoffs on which values inform the vision of the final outcome of your solution. When you seek to return on investment, to maximize profit, the answers are pretty clear – drive down expenses, raise prices as high as the market will bear, communicate the value proposition to the customer, and produce enough goods at the right rate to meet demand without excess goods sitting idle. When you seek to address systemic inequity, your solutions will have decidedly different considerations – your expenses will go up as you pay fair wages, prices might not maximize your margins, you will be more candid with your customers, and your manufacturing and distribution will be likely slower and more intentional as you make ethical considerations in your processes. You will also consider all sorts of other externalities that pop up as a result of your solutions, boosting the positives while capping the downsides.
This is not to say that all solutions will be equally easy to implement under any one set of values systems that you choose. However, it’s fallacious to believe that the same answer will always be given for “can we build this?” and “should we build this?” if you aren’t also examining the underlying values that you set in your assumptions.