Artificial Intelligence (AI) and People: They’re Complementary

“The real problem is not whether machines think but whether [people] do.” – B.F. Skinner

I embrace human-centered design.

In this Framework issue, we explore two very different articles that ponder the relationship between people and AI, which is complementary. The first one: “Why Algorithms Will Never Be Better than Humans,” by Jonas Altman — reinforces the importance of “mavens.” The second — “Smarter together: Why artificial intelligence needs human-centered design,” by Jim Guszcza — offers an in-depth analysis of the evolving relationship between people and AI.

Business leaders who embrace human-centered design focus on the needs of the people they serve, what problems or desires this group has, and then use technologies to design solutions which address these. Here are some thought-provoking ideas and insights into how to leverage this philosophy in your organization.


FRAMEWORK : Data Analytics

Why Algorithms Will Never Be Better than Humans

In 2014, Stephen Hawking proclaimed AI could threaten our future: “Humans, limited by slow biological evolution, couldn’t compete and would be superseded by A.I.” [sic] That statement has been seconded by Bill Gates and Elon Musk.

This article takes a different view. Altman believes the reason algorithms can’t replace people is summed up in one word: maven. It comes from the Hebrew mēvīn for “person with understanding, teacher,” and is a participle of the verb hēvīn for “understanding.” To me, this is understanding at a deep level.

Altman believes that mavens long to share their knowledge. This differs from algorithms, which are a set of rules written to solve problems. Then he points out several other differentiating characteristics.

Mavens are curious. They ask questions and listen to understand. They also have personality: something that algorithms have a hard time scaling.

That’s because algorithms follow a different path. They achieve understanding through inputs. No matter how innovative an algorithm is, it can only produce the results that it determines. This can be limited by AI’s inability to pick up on the multitude of body language and voice modulation cues that a maven can – and get a sense when a person isn’t asking the question she really wants the answer to.

TripAdvisor and Netflix provide valuable information on vacation destinations or entertainment choices. We often supplement our online research by asking a friend — our personal maven — who has experienced what we’re exploring. Ultimately, we use both sets of information from our trusted sources — machine and human!

The relationship between AI and humans is complementary. AI presents us with a breadth of information analyzed and correlated. People provide “depth” — a deep dive tailored to our interests and delivered with passion. This combination of breadth and depth helps us discover and create solutions which are truly differentiated.


FRAMEWORK : Data Analytics

Smarter together: Why artificial intelligence needs human-centered design

Tay was a chatbot that learned through conversations with users. Twenty-four hours after it went online, the designers shut it down. That’s because pranksters trained it to utter racist, biased and fascist statements.

This raises an important difference between AI and people. We have common sense, which is proving elusive to teach machines. Tay wasn’t a maven and couldn’t understand the skewed input it was receiving. (For more on the link between common sense and emotional intelligence, see Think About the Human Touch in Simplifying Decisions, Using Big Data and Teaching Machines to Learn.)

AI can more efficiently and effectively perform certain tasks than people, such as reviewing a mass of customer complaint data and identifying emerging trends. AI contributes scale, speed and accuracy — which is important in the success of any business.

Algorithms — courtesy of big data and machine learning — are programmed to capture and transmit meaning. This is very different from the human qualities of being able to understand and originate meaning.

“Technology is the easy part. The hard part is figuring out the social and institutional structures around the technology.”  – John Seely Brown

Human-centered design creates a system that enables the best from both AI and people. Truly productive collaborations meet these four requirements:

  • Goal Relevance: What a computer sees as an optimal design must be tempered by the reality of how people use the system.
  • Handoff: People need to intervene during situations that are ambiguous, require common sense, or need context.
  • Feedback Loops: When creating algorithms that are fast and accurate, we must watch for any user and societal bias programmed into the data.
  • Psychological Impact: Just as we look at our effect on AI, we need to monitor its effect on us.

How can you make human-centered design relevant to you? Give your people and teams the leeway to share how they explore and understand their data, analysis and results. Ask for their straightforward assessment of what makes sense and what may require judgmental overlays — and why. Make them feel like mavens — so your business reaps the benefits.

“To grow your business, you must recognize your biggest challenge as a leader. This is to reward curiosity and common sense when embracing AI and other future-forward technologies.” – Marcia Tal