Using AI in an Uncertain World

Like anything else in life except death and taxes (and even the particulars of these are uncertain), uncertainty is something that humans deal with every day.  From relying on the weather report for umbrella advice to getting to work on time, every day actions are fraught with uncertainty and we all have learned how to navigate an unpredictable world.  As AI becomes widely deployed, it simply adds a new dimension of unpredictability. Perhaps, however, instead of trying to stuff the genie back in the bottle, we can develop some realistic guidelines for its use.

Our expectations for AI, and for computers in general have always been unrealistic.  The fact is that software is buggy; that algorithms are crafted by humans who have certain biases about how systems and how the world works—and they may not match your biases.  Furthermore, no data set is unbiased, and we use data sets with built in biases or with holes in the data to train AI systems.  These systems are by their very nature, then, biased, or lacking in information.  If we depend on these systems to be perfect, we are letting ourselves in for errors, mistakes, and even disasters.

However, relying on biased systems is no different from asking a friend, who shares your world view, for information that may serve to bolster that view rather than balance it.  And we do this all the time.  Finding balanced, reliable, reputable information is hard and sometimes impossible.  Any person trying to navigate an uncertain world tries to make decisions based on balanced information. The import of the decision governs (or should) the effort we make in hunting for reliable but differing sources.  The speed with which a decision must be made often interferes with this effort. And we need to accept that our decisions will be imperfect, or even outright wrong, because no one can amass and interpret correctly everything there is to know.

Where might AI systems fit into the information picture?  We know that neither humans nor systems are infallible in their decision making. Adding the input of a well-crafted, well-tested system that is based on a large volume of reputable data, to human decision making can speed up and improve the outcome. There are good reasons for this because human thinking balances AI systems. They can plug each others’ blind spots.  Humans make judgments based on their world view.  They are capable of understanding priorities, ethics, values, justice, and beauty.  Machines can’t.  But machines can crunch vast volumes of data. They don’t get embarrassed.  They may find patterns we wouldn’t think to look for.  But humans can decide whether to use that information.  This makes a perfect partnership in which one of the partners won’t be insulted if their input is ignored.

Adding AI into the physical world in which snap decisions are required, raises additional design and an ethical issues which we are ill-fit to resolve today.  Self driving cars are a good example of this.  In the abstract, and at a high level, it’s been shown that most accidents and fatalities are due to human error.  So, self-driving cars may help us save lives.  Now we come down to the individual level. Suppose we have a sober, skilled, experienced driver who would recognize a danger she has never seen before. Suppose that we have a self driving car that isn’t trained on that particular hazard. Should the driver or the system be in charge?  I would opt for an AI assisted system with override from a sober, experienced driver. On the other hand, devices with embedded cognition can be a boon that changes someone’s world. One project at IBM research is developing self driving buses to assist the elderly or the disabled live their lives independently. Like Alexa or Siri on a smaller scale, this could change lives. We come back to the matter of context, use, and value. There is no single answer to human questions of “should.”

This brings us to the question of trust. Should we trust AI systems and under what circumstances? That depends on:

  • The impact of wrong or misleading information:       Poor decisions? Physical harm? Momentary annoyance?
  • The amount and reliability of the data that feeds the system
  • The goals of the system designers:  are they trying to convince you of something?  Mislead you?  Profit from your actions?
  • The quality of the question/query

Is there some way to design systems so that they become an integral part of our thinking process, including helping us develop better questions, focus our problem statements, and reveal how reliable their recommendations are? Can we design systems that are transparent? Can we design systems that help people understand the vagaries of probabilistic output? Good design is the key—within the context of the use and the user.

2 comments

  1. Sue,

    Great post. I agree with your points on getting good data. One point to add is AI costs money. The platforms/APIs Google, IBM, Microsoft, and others cost money. The data has costs. The benefits are big, but people need to understand the costs too.

    1. James,

      You’re right, and many new technologies don’t pay off for a considerable while. As these technologies become embedded in other applications, and as the preparation work becomes easier, we will see them becoming more affordable and more pervasive. However, early adopters will see competitive advantages that may mitigate their investment costs. And that’s another unknown.

      sue

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