The good and the bad of data

In the data science community, we are optimistic about the incredible potential of AI and other advanced technologies to empower people, greatly benefit current and future generations, and work for the common good. AI must therefore be accountable: user-centered, impartial and tested.

Sometimes I hear people say things like, “AI tools are going to take our jobs away”. The question is, if all the work has to be done by machines, what are humans going to do? For example, a lawyer might think that data tools or AI machines will support things like risk assessment, contract analysis, or business negotiations. And where will that leave them? Today, if a lawyer has to read a 100-page contract, it can take three or four days. If this task were automated using AI, lawyers could focus on high priority tasks rather than going through tedious documents. A machine can identify the most important clauses as medium risk or high risk. You can skip low-risk clauses and focus on just 15 clauses, then figure out what could be done in hours rather than days. Machines aren’t there to take the chores off you, but they will make your job easier by allowing you to focus on priority issues.

The data relate only to incoming waste, outgoing waste. If you feed garbage data, you will get garbage output. This is where responsible AI comes in. If you feed the machine with the right data, it will give you the right results. Data science has shown its early successes, as evidenced by our social life, where people feel empowered through the use of data science tools. These include mobile maps (Google Maps), GPS (live road map for autonomous vehicles), fitness and sleep management applications (MotionX, BodyMedia), goal management applications ( Lift, Strides, Way Of Life) and personal finance apps (Emma, ​​Yolt, Cléo). Olivia, for example, is a free financial assistance app that uses AI and behavioral economics to find patterns in how a person typically spends money, and then uses those patterns to create strategies that help. users to spend less on the same things.