We often fail to predict the influence and ethical dilemmas posed by technology until we've seen them in action. The speed of tech development is rapidly exceeding our human ability to understand it. But what happens when technology outpaces us?

Complexity and Speed of Tech: Can we fathom a world where source code becomes too intricate or lengthy for us to comprehend in our lifetimes? What if trading bots operated at speeds humans couldn't track? We've seen the advantages of these technological activities, despite occasional glitches, stock market crashes, and security issues.

 

Example: Think about a video game speed run competition where professional gamers try to finish a game as quickly as possible. Now, imagine a speed run that's completed by an AI in a fraction of the time it takes a human - this is the speed of technology we're talking about!

 

Surprises from Technology: Technology often yields both intended and unintended results. Sometimes, the rush of nation states, corporations, or entrepreneurs for competitive edge could push the advancement of technology too fast. People with the power to develop and implement tech might overlook potential negative impacts or might cross ethical boundaries.

 

Example: Remember the hoverboard fad a few years back? The idea was cool and fun, but manufacturers rushed to make and sell them before ensuring they were safe. Result? Some models caught fire, leading to injuries and property damage.

 

Technology as a "Black Box": While the tech itself might be baffling, we can understand the human processes that drive it, such as business and state interests, regulation, and research. This understanding allows us to foresee potential ethical implications.

 

Example: Think of a magician's trick as technology. While the trick itself might confuse us, we can often figure out the trick by understanding the magician's actions and motives.

 

Technology and Morality: Our understanding from big data mainly tells us about the past. Human thought and moral consideration are essential for shaping our future actions. It's crucial that businesses consider not just profits but also ethics when leveraging data.

 

Example: Consider a company deciding whether to sell users' personal data to increase profits. Human thought and morality play a crucial role in deciding whether this is an ethically right move or not.

 

Technology's Neutrality: Technology might be neutral, but its ethical implications depend on the user. Tech can reflect the values, biases, and ideas of its creators, leading to potential bias.

 

Example: Let's take facial recognition software. If the training data is predominantly white faces, the software will be less effective at recognizing faces from other ethnicities, thus reflecting the bias in the dataset.

 

The Opacity of Tech Models: Technologies like mathematical models, simulations, and big data analyses are often seen as "neutral." Yet, they reflect the opinions of their creators, embedded in mathematics. The lack of visibility of these tech's inner workings can make it difficult for people, including regulators, to understand them.

 

Example: A teacher performance measurement system that uses algorithms to rank teachers based on student test scores might not correlate with actual teaching ability, leading to potential wrongful terminations.

 

Impacts of Tech Models: Poorly tested or understood models can result in unfair or problematic outcomes, such as teachers getting fired randomly or people with lower educational backgrounds paying higher insurance premiums.

 

Example: Imagine an algorithm that determines college admission based on zip codes. This algorithm might unintentionally disadvantage students from lower-income areas, perpetuating systemic inequality.

 

Incentives to Improve Models: Some institutions, like Amazon, continuously improve their models due to clear profit incentives. However, institutions like the US prison system might not be as proactive in improving their models to reduce recidivism.

 

Example: Amazon's recommendation system might suggest products you're likely to buy based on your browsing history, while the US prison system might not have an effective model for predicting and preventing repeat offenses.

 

Unforeseen Feedback Loops: Models can also produce unexpected feedback loops, such as low credit scores leading to higher interest loans, which further lowers the credit score.

 

Example: Imagine being unable to get a job because of poor credit, but your credit is poor because you've been unemployed and unable to pay off debts. It's a vicious cycle!