Recapping AI related risks to Organizations

When they develop predictive models for business, Data Scientists often feel pressure to create results within a very short time span. These feelings may indicate a larger problem with risk management.

With uncertainty, the natural thing is to divest, i.e. not invest large sums in an uncertain endeavour. But AI risks are not easily disposed of in small projects either.

This might leave organizations perplexed as to what to do. On one hand, there is the call to embrace AI. On the other, the risks are real.

As a rule of thumb, a longer time perspective won’t hurt. Predictive  modeling and automation are long-running investments. As such, they should be subject to risk assessment and scrutiny. There should be management for their entire life span.

Because of AI solutions’ partly speculative nature, their risk of failure is relatively high. A recent study underlined this, suggesting that roughly four out of five AI projects fail in the real world.

A predictive model has its particular strengths and weaknesses. But it has some recurring costs too, both implicit and explicit. Some of these costs may fall immediately to the supporting organization. And some of them might even fall outside of it.

The following (otherwise unrelated) tweet from a couple of days back pinpoints these risks neatly.

Leaving aside the social discourse, I very much agree on observations about organizations. There is a certain mindset about DS magically fixing business perspectives and organizational shortcomings. In my personal opinion, this is naïve at best. It is not an overstatement to call it dangerous in some cases.

The use of automation requires a certain robustness from surrounding structures.

AI as part of larger systems

In classical control theory, systems are designed around the principle of stability. A continuously working system, like a production line, is regulated with the help of measured and desired outputs. The problem is to make processes optimal by making them smooth, and get a good output per used resource ratio out of it.

Often, AI is a part of a larger production machinery. The whole process may involve human beings and other machine actors as well. Recent examples of AI victory make a lot of sense when seen in this kind of framing.

If we look at a famous example, Google AlphaGo’s victory over human players was supported by human maintained tournament protocols, servers, and arrangements. Not to speak of news media that helped to sculpt the event when it took place.

The AI’s job was relatively simple as comes to inputs and outputs: receive a board position and suggest the next move. Also how that AI learned to play Go in the first place was a result of multiple years of engineering. Its training was enabled by human work, and its progress was assessed by humans along the way.

The case of  adverse outcomes

If we look at organizations, there are always hidden costs when adapting new procedures and processes. Predictive model performance, on the other hand, is largely measured by the number of explicit mistakes that it makes. These kinds of explicit mistakes may capture part of the cost of an automated solution. But fail rate is hardly a comprehensive measure in a complex setting.

Just like in a game some moves may be very costly as regards winning, some mistakes may be very costly to an organization.

One recent observation within the field has been about implicit “ghost” work that goes into keeping up AI appearances: fixing and hiding AI based errors, even fixing AI decisions in the first place before they have time to cause harm.

Now traditional production lines have fallback mechanisms. For example for turning the line off in a case of emergency. Emergency protocols are in place because unexpected events may occur in the real world. This is a very healthy mindset for any AI development also. We should embrace it fully. An organization should take these things into account when planning and assessing a new solution.

No matter how good preliminary results a solution should show, it will start failing sooner or later when something unexpected happens. And it will not fix itself. Its use will probably also create unexpected side effects even when it is doing a superb job.

Data career opportunities

A data scientist’s abc to AI ethics, part 2 – popular opinions about AI

In this series of posts I’ll try to paint the borderline between AI and ethics from a bit more analytical and technically oriented perspective. Here I start to examine how AI is perceived, and how we may start to analyze ethical agency.

Multiple images

From 3D apps to evil scifi characters, in everyday use it can mean almost anything. It’s a bit of a burden that it is associated with Terminator, for instance. Or that the words deep learning might receive god like overtones in marketing materials.

Let’s go on with some AI related examples. On a PowerPoint slide, AI might be viewed as an economic force. For yet another example, we could look at AI regulation.

Say a society wants to regulate corporate action, or set limits to war damage with weapon treaties. Likewise, core AI activities might need legal limits and best practices. Like, how to make automatic decisions fair. My colleague Lassi wrote a nice recap about this also from an AI ethics perspective.

Now in my view, new technology won’t relieve humans from ethics or moral responsibility. Public attention will still be needed. Like Thomas Carlyle suggested, publicity has some corrective potential. It forces institutions to tackle their latent issues and ethical blind spots. Just like public reporting helps to keep corporate and government actions in check.

Then one very interesting phenomenon, at least from an analytical perspective, are people’s attitudes towards machines.

Especially in connection to ethics, it is relevant how we tend to personify things. Even while we consciously view a machine as dumb, we might transfer some ethical and moral agency to it.

A good example is my eight year old son, who anticipated a new friend from Lego Boost robot. Even I harbor a level of hate towards Samsung’s Bixby™ assistant. Mine is a moral feeling too.

These attitudes can be measured to a certain extent, in order to improve some models. This I’ll touch a bit later.

Perceived moral agency

There is a new analytical concept that describes machines and us, us with machines. This concept of perceived moral agency describes how different actors are viewed.

Let’s say we see a bot make a decision. We may view it as beneficial or harmful, as ethical or unethical. We might harbor a simple question whether the bot has morals or not. A researcher may also ask how much morals the bot was perceived to have.

Here we have two levels of viewing the same thing, a question about how much a machine resembles humans, and then a less intermediate one about how it is perceived in the society.

I think that in the bigger picture we make chains of moral attribution, like in my Bixby case. My moral emotion is conveyed towards Samsung the company, even if my immediate feelings were triggered by Bixby the product. I attribute moral responsibility to a company, seeing a kind of secondary cause for my immediate reactions. The same kind of thing occurs when we say that the government is responsible of air pollution, for instance.

What’s more to the point, these attributive chains apply to human professionals too. An IT manager or a doctor is bound by professional ethics. Their profession in turn is bound by the consensus within that group. If a doctor’s actions are perceived as standard protocol, it is hard to see them as personal ethics or lack of it.

Design and social engineering

Medical decision assistants and other end products are the result of dozens of design choices. And sometimes design choices, if not downright misleading, voluntarily support illusions.

For instance, an emotional reaction from a chat bot. It might create an illusion that the bot “decides” to do something. We may see a bot as willing or not willing to help. This choice may even be real in some sense. A bot was given a few alternative paths of action, and it did something.

Now what is not immediately clear are a bot’s underlying restrictions. We might see a face with human-like emotions. Then we maybe assume human emotional complexity behind the facade.

Chat bots and alike illustrate the idea of social engineering. What it means is that a technical solution is designed to be easy to assimilate. If a machine exploits cultural stereotypes and roles in a smart way, it might get very far with relatively little intelligence.

A classic example is therapist bot ELIZA from 1960s. Users would interact via a text prompt, and ELIZA would respond quite promptly to their comments. Maybe it asked its “patient” to tell a bit more about their mother. It didn’t actually understand any sentence meanings, but it was designed to react in a grammatically correct way. As the reports go, some of the users even formed an addictive relationship with it.

The central piece of social engineering was to model ELIZA as a psychotherapist. This role aided ELIZA in directing user attention. It might also have kept them away from sizing up ELIZA and its limitations. To read more about ELIZA, you may start from its Wikipedia page.

Engagement and management

ELIZA of course was quite harmless. For toys, it is even desirable to entice the imagination. A human facade can create positive commitment in the user. This type of thing is called engagement in web marketing.

On the other hand, social engineering is hard work and not always rewarding. An interesting related tweet came from a game scriptwriter.

This scriptwriter would wish players to submerge and have profound emotional experiences in her games. In her day to day work she had noticed a constant toil with her characters. Was this need for detail even bigger than, say, in a novel? Yes, she suggested.

The scriptwriter also analyzed this a bit. She noticed that repetitive out-of-context action is likely to distance a user. What’s more, it is also very likely to occur when prolonged interaction is available.

I’m tempted to think that these are the two sides of engaging a user. The catch and the aftermath.

As far as modeling and computational perspective go, another significant theme is the nature of automatic decisions.

The most relevant questions are these. How is the world modeled from the decision making agent’s perspective? What kind of background work does it require? How then about management? What kind of data does the agent consume? How to control data quality?

These will get a bit more detail in my next post. Stay tuned, and thanks for reading!

This is the second post about AI and ethics, in a series of four.

A Data scientist’s abc to AI ethics, part 1 – About AI and ethics

In this series of posts I’ll try to paint the borderline between AI and ethics from a bit more analytical and technically oriented perspective. My immediate aims are to restrict hype meanings, and to draw some links to related fields.

In the daily life we constantly encounter new types of machine actors: in social media; in the grocery store; when negotiating a loan. Some of them appear amiable and friendly, but almost all are difficult to understand deeply. Their constitution may be cryptic and inaccessible.

It’s of course a subject of interest as in which ways algorithms and machine actors impact our society. Maybe they do not remain value-free or neutral in a larger context.

Philosophical and other types of interest

The Finnish Philosophical Society’s January 2019 colloquium targeted these kinds of questions. Talks concerned AI, humanity, and society at large. Prominent topics included the existence of machine autonomy and ethics. One interesting track concerned the definitions of moral and juridic responsibility. Many weighty concepts like humanity,  personhood, and the aesthetics of AI, were discussed too.

From a purely philosophical perspective, technology might be viewed as one particular type of otherness. It is something out of bounds of direct personal interest.

On the other hand the landscape around AI may appear supercharged at the moment. Even the word AI reveals many interest vectors. “Whose agenda does the ethics of AI in each case forward?” Maija-Riitta Ollila asked in her presentation.

No wonder many people with a technical background are a bit wary of the term. Often it would be more appropriate to use a less charged one – some good alternatives include machine learning, statistical analysis, and decision modeling.

Between AI and ethics

Most of the talks in the colloquium shared this very sensible view that AI as a term should be subject to critique. One moral responsibility then for tech people is just shooting down related hype.

But the landscape of AI and ethics is complex and controversial. As if to back this observation, many presenters in the colloquium openly asked the audience to correct them on technical points if they should go wrong.

For instance, cognitive and emotional modeling are named as two quite distinct areas of research within cognitive science and neuroscience. The first holds much more progress than the other, when we compare their achievements. Logic is relatively easier to simulate than emotional attitudes. We may equate this with the innate complexity of human action and information processing that this simulation platform only exemplifies.

Furthermore, as illustrated by many intriguing thought experiments, problems arise when we try to attribute an ethical or moral role to a machine actor. Some of these I’ll try to explicate in later posts.

Interests divide the world

A bit of a discomfort for me has been the relationship between AI discussion and ethics. Is the talk always morally sound? Sometimes it felt that ethics won’t fit into the world of AI marketing. If I should define ethics with a few words, I would probably state that it is deep thinking about prevalent problems of good and bad.

Some wisdom about AI

We may juxtapose this with a punchline about contemporary AI. “[The] systems are merely optimization machines, and ultimately, their target is optimization of business profit”, one fellow Data scientist wryly commented to me.

So on the surface level, computer science and mathematical problems might not connect to ethics at all. The situation may be alike in sales and marketing. Also in philosophy, formal logic on the one hand and ethics and cultural philosophy on the other are largely separate areas.

What to make of this divide? My next post will examine popular perceptions of AI in the wild.

This is the first of four posts that will handle the topics of AI and ethics from a bit more technical angle.