Problem Solving


Companies are leveraging data and artificial intelligence to create scalable solutions — but they’re also scaling their reputational, regulatory, and legal risks. 

  • Los Angeles is suing IBM for allegedly misappropriating data it collected with its ubiquitous weather app.
  • Optum is being investigated by regulators for creating an algorithm that allegedly recommended that doctors and nurses pay more attention to white patients than to sicker black patients.
  • Goldman Sachs is being investigated by regulators for using an AI algorithm that allegedly discriminated against women by granting larger credit limits to men than women on their Apple cards.
  • Facebook infamously granted Cambridge Analytica, a political firm, access to the personal data of more than 50 million users.
Colin I. (@colinii53) - Profile Photo



Problem Solving

Just a few years ago discussions of “data ethics” and “AI ethics” were reserved for nonprofit organizations and academics. Today the biggest tech companies in the world are putting together fast-growing teams to tackle the ethical problems that arise from the widespread collection, analysis, and use of massive troves of data, particularly when that data is used to train machine learning models, aka AI.

These companies realized one simple truth: failing to operationalize data and AI ethics is a threat to the bottom line. Missing the mark can expose companies to reputational, regulatory, and legal risks, but that’s not the half of it. Failing to operationalize data and AI ethics leads to wasted resources, inefficiencies in product development and deployment, and even an inability to use data to train AI models at all.

  • Identify existing infrastructure that a data and AI ethics program can leverage. 
  • Create a data and AI ethical risk framework that is tailored to your industry.
  • Change how you think about ethics by taking cues from the successes in health care. Leaders should take inspiration from health care, an industry that has been systematically focused on ethical risk mitigation since at least the 1970s.
  • Optimize guidance and tools for product managers. 
  • Build organizational awareness.
  • Formally and informally incentivize employees to play a role in identifying AI ethical risks.
  • Monitor impacts and engage stakeholders.
  • The academic approach: this means spotting ethical problems, their sources, and how to think through them. But it unfortunately tends to ask different questions than businesses. The result is academic treatments that do not speak to the highly particular, concrete uses of data and AI.
  • The “on-the-ground” approach: it knows to ask the business-relevant risk-related questions precisely because they are the ones making the products, but it lacks the skill, knowledge, and experience to answer ethical questions systematically, exhaustively, efficiently and institutional support.
  • The high-level AI ethics principles: Google and Microsoft, for instance, trumpeted their principles years ago. The difficulty comes in operationalizing those principles. What, exactly, does it mean to be for “fairness?” Which metric is the right one in any given case, and who makes that judgment?

The fundamental question is if one can teach intelligence. If we view intelligence as using some basic cognitive abilities for efficient information processing, it is probably impossible.

Other definitions of intelligence include problem-solving and decision-making, planning, strategic exploration, testing hypothesis and correcting them.

The Programme for International Student Assessment (PISA) has given high priority to a broadened understanding of intelligence and found that problem-solving skills broader than the traditional conception of intelligence are markedly different from the traditional proficiency in maths, science and reading.

Looking at the broader implications of this on the existing education systems, teaching and instruction should focus more on cognitive flexibility, problem-solving and aspects of intelligence that are amenable to change.

Conflicting definitions of intelligence

For decades, scientists have investigated intelligent and less intelligent behaviour. Many definitions of intelligence, sometimes even contradicting each other, have emerged as a result.

Despite the differing views among scientists, we do know that intelligence affects life outcomes. In education, several programmes have aimed at increasing intelligence among students with disappointing results.

  • Teaching students strategies to increase self-monitoring and evaluation when problem-solving.
  • Using teaching methods that help with a deeper understanding of the structure that underpins new problems.

The methods boil down to providing students with as many active problem-solving learning opportunities as possible.

Søren Kierkegaard was influenced by Socrates, who thought that his task was not to discover the truth and then communicate it to his students, but to open the question to the pupils and ensure they stay open.

The last thing you should do is turn to an authority to tell you what you should think. You have to do that for yourself.

Doubt is necessary for philosophy

Danish philosopher Søren Kierkegaard (1813-55) believed that in order to practice philosophy, you have to doubt everything.

His belief in thinking for oneself is noticed throughout his pseudonymous works. In writing under aliases, he lessened the sense that an authority wrote the books.

Psychologists and comedians are working in a similar fashion: They observe the world and test a new hypothesis (raw joke matter) on how people see it. They run experiments on individuals and groups that confirm or deny their new theories or jokes.

Both rely on the feedback of the colleagues, scholars or the audience to shape their experimental jokes or theories.

In his book Rhetoric, Aristotle has analyzed what a joke is: Creating an expectation and then breaking it.

“What’s the best thing about Switzerland?”
“I don’t know, but the flag’s a big plus.”

This joke builds an expectation in the first sentence (Chocolates? Watches?) but breaks it in the second, and after a confusing pause, we see that the answer does make sense: The Swiss flag has a big plus sign.

Comedians As Master Psychologists

When inquired about an occupation that has the most insight on human behaviour and human nature, one would assume it would be teaching, as it requires shaping and developing a lot of young minds.

However, it is a comedian who has a much deeper insight into human behaviour, as he(or she) has to make the audience laugh and yet ensure that the comfort barrier isn’t broken. It requires a great deal of insight into the immediate reaction that a live audience is going to have.

Named after "The Fox and the Grapes", the sour-grape effect is a systematic tendency to downplay the value of unattainable goals and rewards. We underestimate our future happiness because we don't always know what we want, and adjust our desires to what appears within reach.

People will rather devalue a goal than devalue the self. It means that people could miss out on the chance to try again because what once seemed impossible might now be within reach.

Failure leads to underestimation

We have all encountered failure, be it failing a final exam, or a job interview. We're told that overcoming difficult obstacles will make a future success much sweeter.

But new research shows that initial failure can lead people to underestimate how good it would feel to succeed.

  • "The grass is always greener on the other side" suggests that people spend much of their time longing for things they don't have.
  • In Aesop's fable of "The Fox and the Grapes", the fox walked away from the grapes he desired because he could not reach it, concluding that the grapes were probably sour anyway. This tale teaches that failure can make future success appear less attractive.

In a study, people who see grass as greener on the other side predict higher happiness with future success. Participants that reacted like Aesop's fox would try to distance themselves from failure. It suggests that initial failure made people underestimate how good it would feel to succeed.

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