Being Nice to Bots

There is no need to be nice to a machine, not an objective one at least.

In the context of robots pretending to be people (text NLP bots) we rarely consider the feelings (not utility) of our customer.

A socially balanced individual 20 years ago would have no reason to remove all manners and niceties when approaching a company, on an issue related to a recent purchase say.

Not necessarily the case today. If you know that you’re conversing with an algorithm you may remove niceties, to little effect. Beyond philosophizing, the machine would only compute your perceived tone if they fit in a model (say sentiment analysis: are you happy or angry).

As much discussed in various places (including here) current machines lack basic human mechanisms when it comes to actual conversation. Which is the reason we’re seeing more and more seamless human and machine collaborations (say in a messaging environment for a brand). A bot could get stuck and the system passes you on to a human, often within the same window.

Should we not alert the other side of the conversation if they’re talking binary (to a bot) or symbolically (to a human)?

How (1) can we design processes of sending routine communication to a machine and more abstract problems to a human (2) in a way that eliminates miscommunication and orients our customer?

Introducing robots to our communication landscape warrants closer to how we talk to robots (maybe the same), and more importantly how and when we switch from talking to robots, to talking to humans.

Roy and Zeckhauser on Ignorance

Unknown outcomes can be further classified into risk and uncertainty. Risk applies when probabilities are known, as they are at gambling tables, or for insurance companies that have vast amounts of data on individual risks. Uncertainty prevails when even those probabilities are unknown, as they are for virtually all real-life decisions.

Ignorance is both an enemy and a friend; it imposes big risks, yet offers the possibility of substantial gains. It confronts most of us much of the time, in decisions large and small.

Ignorance describes the state of the world when one has moved from uncertainty to conditions where some potential outcomes are unknowable and not merely unknown (Zeckhauser 2006)

The first challenge in grappling with ignorance is to recognize its presence. A person who suffers primary ignorance has usually failed to consider the possibility of CADs (Consequential Amazing Development). So we start with the suggestion of raising self-awareness.

Our key lesson is that as individuals proceed through life, they should always be on the lookout for ignorance. When they do recognize it, they should try to assess how likely they are to be surprised—in other words, attempt to compute the base rate.

When ignorance is recognized, any of three common biases may emerge:

  1. Status quo bias.
  2. Action bias.
  3. Indecision Bias

1. Status Quo Bias (SQB) leads one to stay the course by “doing nothing or maintaining one’s current or previous decision” (Samuelson and Zeckhauser 1988). A psychological explanation is that errors of commission weigh more heavily than errors of omission (Ritov and Baron 1990, 1992). This bias is reinforced when most outcomes are likely to be bad. A switch in strategy that leads to a bad outcome is more susceptible to blame from others, or indeed oneself, than simply sticking with things as they are and doing equally poorly.

2. Action Bias (AB) arises when people take actions when standing pat or simply waiting would be preferred in expectation (Patt and Zeckhauser 2000). AB is the converse of SQB. It is likely to operate when most outcomes will be favorable. People seek credit from themselves (as well as others) from taking an action that leads to a good outcome. Often it is not clear what would have happened had they simply stuck with what they had.

3. Indecision Bias (IB) arises when one must choose among alternatives, the future is cloudy, and high-magnitude outcomes are possible. Many individuals get frozen with indecision, putting off till tomorrow what should be decided today, particularly when postponement will not secure new information.

It is perhaps a Zen-like axiom that not taking any action is also a form of action. When individuals recognize their ignorance they frequently respond through complete inaction or a state of being frozen with indecision, a phenomenon we label indecision bias (IB). IB is not rooted in the impulse to gather more information; it is, on the contrary, wishful waiting for the situation to resolve itself on its own. At times like this, we might heed President Roosevelt’s (1933) words: “[L]et me assert my firm belief that the only thing we have to fear is fear itself—nameless, unreasoning, unjustified terror which paralyzes needed efforts to convert retreat into advance.” IB, motivated in part by fear of error, is a fundamental affliction.

The recognition of ignorance accentuates difficulties in decision-making among the already indecisive. Too much positive evidence is required before making the switch from a known to an unknown choice (Trautman and Zeckhauser 2013). Thus IB can prove costly. In a situation of recognized ignorance, often a switch from the status quo or some other action yields information that will prove extremely valuable in repeat-choice situations. Yet individuals vastly underestimate or completely ignore this benefit when the action they take is “unable to decide.”

“Ignorance: Lessons from the Laboratory of Literature” Devjani Roy and Richard Zeckhauser

Ubiquity and Differentiation

One outcome of productizing design processes and deliverables is that those are taught in university courses, MOOCs and other immersive environments to non–designers.

This leads to a ubiquity of design jargon across new industries, and by new agents (business leaders, managers, software developers)

The less discussed concern is differentiation. If the process is productize (universalized) why would a prospect decide to work with one studio over another? (To be fair I am viewing this with an 80/20 resolution)

Seth Godin likes to call this kind of lack of differentiation (intention–less client work) as leading to the race to the bottom. Who is a believable person who can give me this thing for the lowest cost possible?

In and of itself I think that educating the market on how to be more empathetic and agile in thinking is not a bad thing at all. The real question is what now?

Are we just left as Design Thinking TA’s for our clients? At what point do we feel the urgency (and agency) to write new modals that make stronger use of the contexts involved (ours, our clients, and the environment today).

If we accept that our practice is who we are–and not what we do–then it is obvious that we must move forward, building on what our clients know, switching from efficient agility to effective liminality.