WHY DATA SCIENTISTS MUST BE ABLE TO EXPLAIN THEIR ALGORITHMS

摘要： The models you create have real-world applications that affect how your colleagues do their jobs. That means they need to understand what you’ve created, how it works, and what its limitations are. They can’t do any of these things if it’s all one big mystery they don’t understand.

“I’m afraid I can’t let you do that, Dave… This mission is too important for me to let you jeopardize it”

Ever since the spectacular  2001: A Space Odyssey  became the most-watched movie of 1968, humans have both been fascinated and frightened by the idea of giving AI or machine learning algorithms free rein.

In Kubrick’s classic, a logically infallible, sentient supercomputer called HAL is tasked with guiding a mission to Jupiter. When it deems the humans on board to be detrimental to the mission, HAL starts to kill them.

This is an extreme example, but the caution is far from misplaced. As we’ll explore in this article, time and again, we see situations where algorithms “just doing their job” overlook needs or red flags they weren’t programmed to recognize.

This is bad news for people and companies affected by AI and ML gone wrong. But it’s also bad news for the organizations that shun the transformative potential of machine learning algorithms out of fear and distrust.

Getting to grips with the issue is vital for any CEO or department head that wants to succeed in the marketplace. As a data scientist, it’s your job to enlighten them.

ALGORITHMS AREN’T JUST FOR DATA SCIENTISTS

To start with, it’s important to remember, always, what you’re actually using AI and ML-backed models for. Presumably, it’s to help extract insights and establish patterns in order to answer critical questions about the health of your organization. To create better ways of predicting where things are headed and to make your business’ operations, processes, and budget allocations more efficient, no matter the industry.

In other words, you aren’t creating clever algorithms because it’s a fun scientific challenge. You’re creating things with real-world applications that affect how your colleagues do their jobs. That means they need to understand what you’ve created, how this works and what its limitations are. They need to be able to ask you nuanced questions and raise concerns.

They can’t do any of these things if the whole thing is one big mystery they don’t understand.

WHEN MACHINE LEARNING ALGORITHMS GET IT WRONG

At other times, algorithms may contain inherent biases that distort predictions and lead to unfair and unhelpful decisions. Just take the case of this racist sentencing scandal in the U.S., where petty criminals were rated more likely to re-offend based on the color of their skin, rather than the severity or frequency of the crime.

In a corporate context, the negative fallout of biases in your AI and ML models may be less dramatic, but they can still be harmful to your business or even your customers. For example, your marketing efforts might exclude certain demographics, to your detriment and theirs. Or that you deny credit plans to customers who deserve them, simply because they share irrelevant characteristics with people who don’t. To stop these kinds of things from happening, your non-technical colleagues need to understand how the algorithm is constructed — in simple terms — enough to challenge your rationale. Otherwise, they may end up with misleading results.

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