The discussion amongst leading scientists concerning the cultural and social implications for artificial intelligence (AI) is in the daily news, resulting in a public, intellectual debate over whether AI will be a harmful, helpful, or benign influence on society. Notably among others, Stephen Hawking and Elon Musk represent the concerned thought leaders, and Mark Zuckerberg and Jeff Bezos advocate the benefits of continued AI investment.
In support of those advocates, humans face challenges in making efficient and rational decisions, specifically when a risky decision is made by an individual expert. Though we wouldn’t have conceived of this possibility five years ago, it’s now not too extreme to consider whether AI-enhanced decision-making platforms might address these challenges, leading to better outcomes in risk-laden situations like medical surgery, litigation, psychotherapy or military job placement. Recommended reading: Undoing Project by Michael Lewis; Never Split the Difference by Chris Voss.
I believe areas where machines are wonderful partners include:
- 24-hour execution
- Repetitive and tedious tasks
- Tasks requiring rapid, detail-heavy calculations
- Robust operational environments: because error adjustment is complicated
- Detail orientation
- Storage and retrieval
- Collecting information from a large group of people in distributed manner
Areas where people are still necessary (and one might argue, always will be):
- Making connections
- Rendering and exercising judgment
- Synthesis, interpretation, explanation, persuasion
- Creating and being creative
- Developing and nurturing relationships
- Decision making and value assessments
- Deciding when and when not to take action
- Problem definition and resolution
With the above distinctions made, let’s take a look at whether it’s plausible for AI to replace decision scientists. Below are the areas of decision science that require more than machine learning and AI to be successful:
- Defining the process that generates the data
- Designing and re-designing an analytics strategy
- Building data integration and cleansing strategies (i.e., ETL and MDM), and specifically, making decisions about survivorship and business usage of recorded data
- Integrating data sources and systems
- Data cleansing, domain and rule definition
- Optimizing the allocation of resources to customers based on analytic guidance
- Changing the culture of the organization and how they use the result of data science to improve how the business performs and serves its customers
Despite recent advances, AI is not a new idea. With traditional, and long held concepts at its core, it’s my contention that what sets modern AI and machine learning apart is the dramatic expansion of certain data domains (i.e. speech, images, remote sensing etc.), and perhaps most importantly, the successful adoption by some practitioners with a tightly-focused investment, in a well-defined domain, to address a specific social or business challenge. Most recently, Alpha Go Zero and the OpenAI challenge in the DOTA2 game domain represent notable examples.
Regardless of much media hype, we believe the bottom line is, and always should be, how do new approaches and technologies lead to better action. Otherwise, it’s all just an exercise in academic debate.
